Tag Archives: Security, Identity & Compliance

AI security strategies from Amazon and the CIA: Insights from AWS Summit Washington, DC

2025-06-13 Danielle Ruderman

Post Syndicated from Danielle Ruderman original https://aws.amazon.com/blogs/security/ai-security-strategies-from-amazon-and-the-cia-insights-from-aws-summit-washington-dc/

Speakers during AWS Summit Washington, DC 2025 on June 10, 2025.

At this year’s AWS Summit in Washington, DC, I had the privilege of moderating a fireside chat with Steve Schmidt, Amazon’s Chief Security Officer, and Lakshmi Raman, the CIA’s Chief Artificial Intelligence Officer. Our discussion explored how AI is transforming cybersecurity, threat response, and innovation across the public and private sectors. The conversation highlighted several key themes: how organizations can leverage AI to improve security outcomes, the rise of agentic AI and its impact on security, the importance of maintaining human oversight in AI systems, workforce development strategies, and practical approaches to implementing AI securely in enterprise environments. Below are a few excerpts from our conversation.

On leveraging AI to improve security outcomes

Steve Schmidt: “We’ve applied AI internally at Amazon in a couple of places that led to some significant benefits, including in the application security review process. By training our large language models internally on prior security reviews that we’ve done, it has allowed us to apply the knowledge and learning that our more senior staff have embodied in the documents that the LLM was trained on and expose that to our more junior staff. It really raises the bar on the absolute level of security that we can offer.”

Lakshmi Raman: “In the cybersecurity realm, we’re thinking about how AI helps us in our accreditation and authorization process, helping us ensure that the process to get systems accredited is going as quickly as possible, because the industry is moving so fast. Another area that we’re applying AI and machine learning is triaging data. We have vast amounts of data that comes in at an exponential rate, so we need to be able to go through it quickly so that we can surface insights. You can imagine a cybersecurity analyst who traditionally has gone through network data manually in order to think about blocking suspicious IP addresses or connections. Now there’s an opportunity to do all of that really efficiently and let the security analysts make the decision.”

On the rise of agentic AI and its implications for security

Steve Schmidt: “The biggest change we’re seeing right now in AI is the rise of agentic AI. The reason agentic AI is particularly interesting is that it brings with it a set of challenges about ensuring the software is taking actions within the context of the person who’s asking it…Think about that in the context of a government organization, where you have sets of information that are restricted to certain populations, there are classification decisions, access control limitations, and reasons that you can access certain data that have to be present before you can do so. Agentic AI brings opportunities—you can take actions using software automatically—but also challenges: how do we make sure that the software is doing exactly the right thing every single time, and more importantly, that we can prove what it did to stakeholders and regulators?”

Lakshmi Raman: “AI agents definitely have an opportunity to transform enterprise automation. Leveraging them to do complex multi-step workflows—to do tool calling across a variety of databases and other foundational tools—has tremendous potential, with a human as a crucial step to review what’s going on.”

On the importance of maintaining human oversight with AI

Lakshmi Raman: “In my world, I spend a lot of time thinking about how AI is impacting the workforce. One of the areas we’re looking at is the intersection between AI and our people. AI is able to speed up the processing and do automation, but at the end of the day, it’s really about who is taking on the risk, or deciding the intents and making the decisions. Whatever the machine output happens to be, really it’s about the human who’s deciding the level of oversight, the risk to take, and even whether to intervene.”

Steve Schmidt: “One thing that many people don’t realize about AI systems is that they’re nondeterministic. What nondeterminism means is you can ask an AI model the same question 100 times, and you will not get the same answer every time. So, having a human who can make a judgment about what the AI comes up with is critically important. We look at it this way: if you’re just asking a question and getting an answer, that may be one set of scrutiny that you have to get assistance. But if you’re going to take an action, you’ve got to be really sure the AI is correct. There has to be that skilled person that Lakshmi spoke about, at the end of the AI use process saying, ‘Yes, this is the right thing to do at this point in time with this context.’”

On building an AI-savvy security workforce

Steve Schmidt: “There’s a real problem in our industry: we don’t have enough security people. We simply can’t hire enough people with the right skills to do this job. What we’ve we found is that AI allows us to do a lot of the heavy lifting for the security staff, using tooling that used to have to be done by humans. Our staff is actually materially happier with their jobs if we remove a lot of that grunt work from them, which is super important. You want to keep the employees you have, so you give them tooling that helps them get the job done more efficiently, and they enjoy their job.”

Lakshmi Raman: “We’re looking for people who can live between the intersection of technology and social intelligence, people who can understand how those two areas can potentially interact around human behavior and how to think about future activities. When we’re thinking about analysts, for example, we’re thinking about people who have critical thinking skills, who can demonstrate analytic rigor, who can think multiple steps ahead with incomplete information. We’re also looking for people who have digital acumen with an understanding of cloud and cyber and AI, so that we have those technical skills in house. And finally, people who are interested in lifelong learning and curiosity, because threats change over the years. We need people who understand and are willing to learn about that.”

On advice for security leaders as AI accelerates

Steve Schmidt: “When you’re looking at making a decision, ask the person who’s bringing the information to you: ‘Why can’t AI do this?’ And if they don’t have an answer, ask ‘When will it be able to and under what condition?’ Move it into the now, the probable, the possible, and make it real for all of your staff all the time. If they’re not intentionally making that decision, they’re missing an opportunity.”

Lakshmi Raman: “You’ve got to get training out there for your users. We think of it at three different levels. First is our general workforce—which might be the most important user base—people who are sitting side by side with our AI practitioners and can help describe the workflows that need automation. Then we think about it for our practitioners, so they are keeping up with the latest. And then finally, our senior executives, who can think about how they can transform their organization with AI and generate that buy-in from the top level.”

AI is not just changing what we can do, but how we work. As Steve and Lakshmi emphasized, the most successful AI implementations will be those that thoughtfully balance automation with human oversight, focusing on use cases that deliver tangible value while managing risks appropriately. For security professionals, understanding both the technical and human dimensions of AI will be critical as we navigate this changing space.

AWS CIRT announces the launch of the Threat Technique Catalog for AWS

2025-06-13 Steve de Vera

Post Syndicated from Steve de Vera original https://aws.amazon.com/blogs/security/aws-cirt-announces-the-launch-of-the-threat-technique-catalog-for-aws/

Greetings from the AWS Customer Incident Response Team (AWS CIRT). AWS CIRT is a 24/7, specialized global Amazon Web Services (AWS) team that provides support to customers during active security events on the customer side of the AWS Shared Responsibility Model. We’re excited to announce the launch of the Threat Technique Catalog for AWS.

When the AWS CIRT assists customers with incident response during security investigations, we gather AWS service metadata on the types of tactics and techniques that threat actors have used against AWS customers. We use this information to build an internal dataset of indicators of compromise (IOCs) and threat patterns that provides insight into how threat actors are taking advantage of misconfigured AWS resources, overly permissive access, or the methods they use in attempting to achieve their objectives.

We capture this metadata and use it internally to continually improve AWS services to help make them more secure for our customers by making it more difficult for threat actors to perform unauthorized actions. For example, some of the metadata that the AWS CIRT has captured as a result of investigating security events where a threat actor has used the Amazon Bedrock service to consume tokens by invoking large language models (LLMs) has been used to supplement the Amazon GuardDuty IAM Anomalous Behavior finding. Earlier this year, the AWS CIRT identified an increase in data encryption events in Amazon S3 that used an encryption method known as server-side encryption using client-provided keys (SSE-C). AWS CIRT used the Threat Technique Catalog for AWS to classify the new techniques identified in these security events to communicate internally and with other Amazon security teams.

We’ve received feedback from AWS customers that information about the adversarial tactics, techniques, and procedures (TTPs) observed by the AWS CIRT would be valuable and helpful if made available to them, so they could use the information to configure their AWS resources more securely. Over the previous year, we’ve been working with MITRE to make these techniques and sub-techniques available to the global security community. As a result of this collaboration, MITRE has updated and added some of these techniques to MITRE ATT&CK® as part of their October 2024 update cycle (for example, Data Destruction: Lifecycle-Triggered Deletion).

“We greatly appreciated the insight AWS shared with us, and it inspired improvements to a number of techniques in the October release of MITRE ATT&CK. For ATT&CK to keep up with the latest threats, community contributions that benefit the ecosystem are needed, and we value AWS being a part of the ATT&CK community.”
–Adam Pennington, project lead, MITRE ATT&CK, MITRE

Companies, entities, and organizations use ATT&CK to help them understand, prioritize and protect against the threats to their on-premises environments, and we believe that taking advantage of an already existing framework to present these adversarial techniques will provide AWS customers and the global security community with the ability to identify and categorize threats on their AWS infrastructure the same way that the AWS CIRT does.

The Threat Technique Catalog for AWS—based on MITRE ATT&CK Cloud—extends these contributions and includes categories of adversarial techniques that are specific to AWS and have been observed by the AWS CIRT; in addition to information on ways to mitigate those techniques and how to detect them. For example, you can go to the Threat Technique Catalog for AWS, filter by the AWS services in your account, and review the content that will help make your environment more secure. The Getting Started section includes additional ways that you can use the Threat Technique Catalog for AWS. We will continue to update and provide additional changes to the Threat Technique Catalog for AWS to help guide you into making your AWS environment more secure and will continue collaborating with MITRE to advise them of new and trending threat actor techniques.

To get started, visit the Threat Technique Catalog for AWS.

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Introducing the AWS Security Champion Knowledge Path and digital badge

2025-06-12 Sarah Currey

Post Syndicated from Sarah Currey original https://aws.amazon.com/blogs/security/introducing-the-aws-security-champion-knowledge-path-and-digital-badge/

Today, Amazon Web Service (AWS) introduces the Security Champion Knowledge Path on AWS Skill Builder, featuring training and a digital badge. The Security Champion Knowledge path is a comprehensive educational framework designed to empower developers and software engineers with essential AWS cloud security knowledge and best practices. The structured learning path enables development teams to accelerate their delivery while maintaining robust security standards in cloud environments, addressing customers’ need for a structured curriculum to develop and validate security expertise across their organizations.

AWS Skill Builder logo

A new era of security education

The AWS Security Champion Knowledge Path complements the existing AWS security training offerings, providing a structured, self-paced journey to security expertise. Hart Rossman, Vice President of Global Services Security at AWS, emphasizes the program’s significance: “Security in the cloud isn’t a destination; it’s an ongoing journey. The AWS Security Champion Knowledge Path equips our customers with the tools and knowledge to navigate this journey confidently, fostering a culture where security is woven into every aspect of cloud operations.”

Designed for a diverse audience including software developers, solutions architects, technical leaders, and cloud practitioners, the training plan covers a wide range of topics that are critical for a strong security posture in the cloud. This AWS security learning journey begins with essential fundamentals and progressively builds toward advanced concepts across a well-structured curriculum. Starting with AWS Security Fundamentals and the AWS Shared Responsibility Model, learners establish core principles before diving into AWS Identity and Access Management (IAM), including detailed troubleshooting scenarios. The curriculum advances to critical security elements such as encryption and comprehensive threat modeling through the AWS Builders Workshop. Security governance and auditing form the next tier, followed by practical implementations of monitoring, alerting, and network infrastructure best practices. The learning path then covers specialized areas including web-facing workload protection, network control, and incident response procedures. The knowledge path culminates with deep dives into container security and core security concepts through AWS SimuLearn, providing hands-on experience with real-world scenarios. This carefully orchestrated progression helps facilitate a thorough understanding of AWS security principles while maintaining a practical, implementation-focused approach.

AWS SimuLearn logo

What is a Security Champion?

A Security Champion is a bridge between security teams and development teams, promoting security best practices and making sure that security is embedded into every stage of the development lifecycle. However, Security Champion isn’t just a role—it’s a mindset. In today’s distributed and agile cloud environments, having Security Champions across different teams provides a competitive advantage for releasing products quickly and securely.

This distributed ownership of security brings numerous benefits: faster development cycles because teams can address security requirements proactively, reduced security incidents through early detection, and improved collaboration between security and development teams. Most importantly, it creates a culture of security where every team member understands their role in protecting the organization’s assets and data.

By becoming a Security Champion, you’ll gain valuable expertise, earn recognized credentials, and develop leadership skills that can accelerate your career growth. Most importantly, you’ll be empowered to make meaningful contributions to your organization’s security posture by promoting best practices, identifying potential vulnerabilities early in the development cycle, and fostering collaboration between teams—ultimately helping your organization deliver products that are both innovative and secure.

How can I become an AWS Security Champion?

Security enthusiasts can enroll into the AWS Security Champion Knowledge Badge Readiness Path on AWS Skill Builder and complete the assessment successfully to earn the AWS Security Champion digital badge available on Credly.

AWS Security Champion training is a self-paced, hands-on, and interactive approach to upskilling on security concepts. As a participant, you’re introduced to security best practices, performing basic audits, planning for governance at scale, incident response concepts and more. You can engage with real-world scenarios through hands-on labs, interactive game-based learning, gain access to AWS sandbox environments, and conduct practical security assessments. This applied learning helps make sure that knowledge isn’t just acquired, but truly internalized and ready for immediate application.

“The AWS Security Champion Knowledge Path represents a significant milestone in democratizing security expertise. We’ve designed this program to transform how organizations approach security training, making it accessible, practical, and immediately applicable. This isn’t just about learning security concepts—it’s about creating a culture where security becomes second nature to every team member.”
– Jenni Troutman, Training and Certification Director at AWS

Recognition and community

Upon successfully completing the assessment in this training path, participants earn the prestigious AWS Security Champion knowledge badge in Credly to showcase their accomplishment, such as on LinkedIn, and join a growing community of security professionals. This recognition not only validates individual expertise but also signals an organization’s commitment to security excellence, and helps organizations identify qualified security champions within their team.

Getting started

To begin your journey to becoming an AWS Security Champion, log in or create an account with AWS Skill Builder and enroll in the Security Champion Knowledge Badge Readiness Path. The training plan is available through flexible pricing options, including individual subscriptions at $29 per month and team subscriptions at $449 per month with enterprise volume pricing available.

Rossman concludes, “The AWS Security Champion Knowledge Path represents a paradigm shift in how organizations approach security education. It’s about creating a shared language of security across teams, enabling faster, more secure development cycles, and ultimately, delivering better outcomes for our customers.”

Ready to elevate your organization’s security capabilities? Visit AWS Skill Builder to enroll and start your journey towards becoming an AWS Security Champion. For enterprise inquiries, reach out to your AWS account team.

Stay tuned to the AWS Security Blog for more updates on AWS Security services, features, and best practices. Together, we’re building a more secure cloud for all.

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AWS completes Police-Assured Secure Facilities (PASF) audit in Europe (London) AWS Region

2025-06-11 Vishal Pabari

Post Syndicated from Vishal Pabari original https://aws.amazon.com/blogs/security/aws-completes-police-assured-secure-facilities-pasf-audit-in-europe-london-aws-region/

We’re excited to announce that our Europe (London) AWS Region has renewed its accreditation for United Kingdom (UK) Police-Assured Secure Facilities (PASF) for Official-Sensitive data. Since 2017, the Amazon Web Services (AWS) Europe (London) Region has been accredited under the PASF program. This demonstrates our continuous commitment to adhere to the heightened expectations of customers with UK law enforcement workloads. Our UK law enforcement customers who require PASF can continue to run their applications in the PASF-accredited Europe (London) Region in confidence.

The PASF is a long-established assurance process, used by UK law enforcement, as a method for assuring the security of facilities such as data centers or other locations that house critical business applications that process or hold police data. PASF consists of a control set of security requirements, an on-site inspection, and an audit interview with representatives of the facility.

The Police Digital Service (PDS) confirmed the accreditation renewal for AWS on May 27, 2025. A confirmation letter can be found on AWS Artifact. The UK police force and law enforcement organizations can also obtain confirmation of the compliance status of AWS through the Police Digital Service.

To learn more about our compliance and security programs, see AWS Compliance Programs. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

Reach out to your AWS account team if you have questions or feedback about PASF compliance.

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Building identity-first security: A guide to the Identity and Access Management track at AWS re:Inforce 2025

2025-06-11 Rahul Sahni

Post Syndicated from Rahul Sahni original https://aws.amazon.com/blogs/security/building-identity-first-security-a-guide-to-the-identity-and-access-management-track-at-aws-reinforce-2025/

AWS re:Inforce 2025: June 16-18 in Philadelphia, PA
Join us at AWS re:Inforce 2025 from June 16 to 18 as we dive deep into identity and access management, where we’ll explore how organizations are securing identities at scale. As the traditional security perimeter continues to dissolve in our hybrid and multi-cloud world, this year’s sessions showcase how AWS customers are building comprehensive identity-centric security strategies that span workforce and customer identities. From authenticating and authorizing human and machine identities to implementing least privilege access controls and securing identities that help drive AI adoption, you’ll discover practical approaches to modernizing your identity architecture.

Whether you’re managing enterprise workforce identities across complex organizational structures or building customer-facing applications that require seamless and secure authentication experiences, the Identity and Access Management track offers insights for every security professional. We’ve carefully curated sessions that address today’s most pressing identity challenges, including zero trust implementation patterns, unified workforce identity management across cloud and on-premises environments, and scalable customer identity and access management (CIAM) solutions. Through technical deep-dives, hands-on workshops, and customer case studies, you’ll learn how to use AWS Identity and Access Management (IAM), AWS IAM Identity Center, AWS Directory Services, Amazon Cognito, and other AWS services to build robust identity foundations that support both security and business agility.

In this post, we highlight some of the key sessions. With over 30 sessions dedicated to identity management, we feature valuable learnings for executives and practitioners alike. Let AWS experts and partners share practical challenges and solutions with you. Let’s explore what you can expect at this year’s conference.

Zero trust and principle of least privilege

IAM304 | Breakout session | Empowering developers to implement least-privilege IAM permissions
Wolters Kluwer, a global provider of professional information, software solutions, and services and GoTo Technologies (formerly LogMeIn Inc.), a U.S.-based software company that provides cloud-based remote work tools for collaboration and IT management use AWS IAM Access Analyzer to simplify and accelerate their journey to least privilege. Join this session to learn more about their use cases and their journey to empower their builders to refine IAM policies to remove excessive permissions. Gain insights into their strategies, best practices, and lessons learned for continuously monitoring unused permissions across their organization and building processes to streamline remediations.

IAM343 | Code talk | Scale Beyond RBAC: Transform App Access Control using AVP & Cedar
This session focuses on transforming an existing application from role-based access control (RBAC) to policy-based access control (PBAC) using Amazon Verified Permissions (AVP) and Cedar policy. The drive for least privilege has led to role explosion in RBAC model and necessitates a shift towards PBAC, augmenting RBAC with attribute-based access control (ABAC). You will learn how to move authorization logic out of application code and implementing a centralized PBAC model. Attendees will also learn to define permissions as policies using Cedar and seamlessly migrate from RBAC to PBAC with minimal application logic changes, enabling more granular and scalable access control.

Securing Identities in the AI era

IAM373 | Workshop | Identity without barriers: user-aware access for AWS analytics services
This hands-on workshop explores AWS IAM Identity Center’s Trusted Identity Propagation, teaching participants how to enable secure identity propagation across integrated applications. Through practical exercises, attendees will learn to configure identity propagation and use it with services such as Amazon Redshift, Amazon Athena, Amazon Q Business, and more. Participants will gain experience with cross-account scenarios, audit logging configuration, and troubleshooting common integration challenges. You must bring your laptop to participate.

IAM321 | Lightning talk | Building trust in Agentic AI through authentication and access control
AI agents execute tasks for humans, operating independently with or without human presence, while collaborating seamlessly across on-premise and multi-cloud environments. This dynamic setup poses unique challenges in human/agent authentication, identity propagation/delegation, and resource authorization. Leverage Amazon Cognito, Verified Permissions, and Bedrock to master effective Identity and Access Management (IAM) for your AI agents. Through real-world examples using OAuth2-based identity management, machine-to-machine authentication, and policy-based access control, you’ll unlock the ability to scale complex agent interactions securely, empowering you to build robust, scalable Agentic AI solutions.

IAM441 | Code Talk | The Right Way to Secure AI Agents with Code Examples
GenAI agents run tasks on behalf of human users with or without users being present, and often interact with each other across on-premise and different cloud providers. This brings new challenges in identity authentication, propagation, delegation, and resource authorization in the overall agentic AI solution. Learn how Amazon Cognito’s OAuth2-based identity management, machine-to-machine authentication, combined with Amazon Verified Permission’s fine-grained authorization can enable secure delegation patterns for AI agents, while preserving human identity and consent, agent machine identity, and other request context throughout the agent chain. We’ll walk through real-world examples with agents built on Amazon Bedrock or other frameworks.

Workforce identity management

IAM302 | Breakout session | Workforce identity for gen AI and analytics
Managing secure, consistent workforce access for generative AI and analytics is critical for unlocking innovation while protecting sensitive data. In this demo-filled session, you’ll see how centralized identity management and trusted identity propagation can deliver a user-centric data access experience. You’ll also learn how AWS IAM Identity Center simplifies access to AWS services such as Amazon Redshift, Amazon Athena, and AWS Lake Formation, while enabling fine-grained access to data based on user identity to help meet your security and compliance needs.

IAM341 | Code Talk | Visualizing Workforce Identity: Graph-Based Analysis for Access Rights
Discover how to gain deep insights into workforce identity relationships and resource access patterns by visualizing AWS IAM Identity Center data using graph databases. Learn how you can explore complex identity relationships, permission inheritance and resource access across your organization; get practical approaches to ingestion of identity data, creating graph queries for security analysis, and building visualization dashboards to help identify potential resource access risks. We’ll explore real-world scenarios for detecting excessive permissions, analyzing group memberships and resource access, and tracking resource access rights changes over time to strengthen your identity security posture.

Customer and Machine identity management

IAM332 | Chalk Talk | Securing and monitoring machine identities with Amazon Cognito
Unlock the power of secure machine-to-machine (M2M) authorization using Amazon Cognito’s OAuth2 client credentials flow. This session dives deep into implementing M2M authorization, featuring real-world optimization strategies for both security and cost. Learn essential security best practices, multi-tenant reference architectures, and monitoring techniques that ensure your M2M usage remains efficient and secure. Whether you’re building microservices, handling API authorization, or scaling your distributed systems, this session will equip you with actionable insights and patterns for successful M2M implementations. Bring your challenges and questions for an interactive discussion on Cognito-powered M2M authorization.

IAM372 | Workshop | Building CIAM Solutions with Amazon Cognito
Learn how to use Amazon Cognito for your solutions’ CIAM needs. Use hands on examples to build fully functional solutions and see some of the new features in action like the new Managed Login UI, Passwordless logins now supported natively and more.

AWS identity foundation

IAM305 | Breakout session | Establishing a data perimeter on AWS, featuring Block, Inc.
Organizations are storing an unprecedented and increasing amount of data on AWS for a range of use cases including data lakes, analytics, machine learning, and enterprise applications. They want to make sure that sensitive non-public data is protected from unintended access. In this session, dive deep into the controls that you can use to create a data perimeter to help ensure that only your trusted identities are accessing trusted resources from expected networks. Hear from Block, Inc. a leading fintech company about how they use data perimeter controls in their AWS environment to meet their security objectives.

IAM451 | Builders session | Securing GenAI Apps: Fine-Grained Access Control for Amazon Bedrock Agents
Want to secure GenAI applications accessing your organizational data? Learn how to implement intelligent access controls for Amazon Bedrock-powered applications accessing your organizational data. In this builder’s session, you’ll build a defense-in-depth approach that combines authentication using Amazon Cognito and fine-grained authorization with Amazon Verified Permissions to secure access for Bedrock AI Agents. Implement layered permissions that protect sensitive data without limiting your GenAI capabilities.

Conclusion

As organizations continue to navigate the complexities of modern identity architecture, implementing a robust IAM framework remains critical for maintaining security posture while enabling seamless access across hybrid environments. The disappearance of the identity perimeter and the shift towards identity-first security demands a more sophisticated approach to authentication and authorization workflows, making continuous validation and adaptive access policies paramount. The community at re:inforce, strives to provide you with solutions, tactics, and strategies that you can use to propel your business forward.

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Building secure foundations: A guide to network and infrastructure security at AWS re:Inforce 2025

2025-06-09 Brandon Carroll

Post Syndicated from Brandon Carroll original https://aws.amazon.com/blogs/security/building-secure-foundations-a-guide-to-network-and-infrastructure-security-at-aws-reinforce-2025/

AWS re:Inforce 2025: June 16-18 in Philadelphia, PA

A full conference pass is $1,099. Register today with the code flashsale150 to receive a limited time $150 discount, while supplies last.

Securing cloud infrastructure has never been more critical as organizations continue to expand their digital footprint and embrace modern architectures. At AWS re:Inforce 2025, the Network and Infrastructure Security track brings together security experts, practitioners, and industry leaders to share insights on building and maintaining secure, automated, and observable cloud foundations.This year’s track focuses on several key themes that are shaping the future of cloud security. Learn how to implement comprehensive defense-in-depth strategies through multiple layers of controls, from perimeter to workload protection. Discover the latest approaches to network visibility and inspection, including tools and architectures for deep packet inspection and enhanced traffic analysis across cloud environments.As organizations scale their cloud presence, automated policy management becomes crucial. This track showcases solutions and approaches for scaling security policy deployment, management, and compliance validation through automation and infrastructure as code (IaC). You’ll also dive deep into zero trust infrastructure implementations, exploring frameworks for identity-based network segmentation and access controls aligned with zero trust principles.With the growing complexity of distributed applications, protecting workloads across cloud, edge, and hybrid environments requires integrated security architectures. Sessions in this track demonstrate how to build comprehensive protection strategies that secure your entire infrastructure footprint while maintaining operational excellence.

Whether you’re just beginning your cloud security journey or leading mature enterprise security initiatives, the Network and Infrastructure Security track at re:Inforce 2025 will equip you with practical guidance and actionable insights to advance your organization’s security posture. Join in on the fun, and register for re:Inforce 2025!

Breakout sessions, chalk talks, and lightning talks

Breakout sessions are lecture-style, 1-hour sessions delivered by AWS experts, customers, and partners—perfect for deepening your knowledge on important topics, gaining actionable insights, and connecting with industry leaders.

Chalk talks are 1-hour long, highly interactive sessions with a small audience. This format is ideal for diving deep into specific topics, engaging directly with AWS experts, and getting your questions answered in real time.

Lightning talks are short (20 minutes) theater presentations dedicated to a specific customer story, service demo, or AWS Partner offering.

NIS301 | Breakout session | Egress control deployments made easy
Speakers: Sofía Aluma (AWS), Jesse Lepich (AWS)
Discover the latest AWS Network Firewall features that simplify implementation and enhance your security posture. In this hands-on workshop, learn how recent updates to AWS Network Firewall and Amazon Route 53 Resolver DNS Firewall streamline deployment, reduce threat exposure, and strengthen security policies. We’ll share practical recommendations for configuring firewall rules that match your specific use cases and help verify that your security controls meet intended objectives.

NIS302 | Breakout session | How Itaú Bank leverages AWS Shield Advanced to combat DDoS events
Speakers: Douglas Lopes (AWS), Guilherme Greco (AWS), Ricardo Donadel (Itaú Bank)
Learn how Itaú, Latin America’s largest bank, uses AWS Shield Advanced to protect their critical financial infrastructure from sophisticated DDoS events. In this session, Itaú’s security team shares how they architected their defense strategy by integrating Shield Advanced with existing security operations and collaborating with the AWS DDoS Response Team. Discover how they maintain robust protection while meeting financial regulatory requirements and examine the business value of their implementation. Whether you work in financial services or other regulated industries, you’ll gain actionable insights for enterprise-grade DDoS protection.

NIS303 | Breakout session | Thinking beyond traditional firewalling architectures
Speakers: Tom Adamski (AWS), Ankit Chadha (AWS)
In this session, we’ll discuss a brave new world where we think beyond traditional firewalling architectures. We’ll explore the use-cases that require firewalls including workload-to-workload, client-to-workload, and workload-to-internet traffic flows. After defining the use cases, we’ll discuss AWS services that allow customers to retain their desired security posture without inserting inline firewalls. We’ll wrap with specific considerations on when firewalling is a good option. For example, for scenarios when customers require AppId-like functionality, or for creating data loss prevention (DLP) deployments for egress traffic.

NIS304 | Breakout session | Integrate Zero Trust into your cloud network
Speakers: Dave DeRicco (AWS)
In this session, learn how to adopt Zero Trust alongside traditional network security functions such as firewalls and VPNs. Explore how services like Amazon VPC Lattice and AWS Verified Access complement your existing network security posture by leveraging identity and network controls to continuously authenticate and monitor access. and how these services can integrate into your existing network architecture. Learn about common adoption approaches and migration patterns and hear best practices for building Zero Trust mechanisms into a secure, modern network architecture.

NIS305 | Breakout session | Advanced network defense: From basics to global scale with AWS Cloud WAN
Speakers: Sidhartha Chauhan (AWS)
Starting with core security principles, this session demonstrates how to build robust network security architectures in AWS. Learn to establish effective network isolation boundaries using AWS Cloud WAN and AWS PrivateLink, followed by implementing traffic filtering through strategic firewall deployments. We’ll compare centralized versus distributed inspection architectures, culminating in how AWS Cloud WAN’s service insertion and policy-based approach enables global-scale centralized inspection flows. Through practical scenarios, attendees will master designing scalable network security architectures that maintain security posture across complex cloud environments. Ideal for security engineers and architects managing enterprise-scale AWS deployments.

DAP332 | Chalk talk | Executive perspective: Risk management for generative AI workloads
Speakers: Jason Garman (AWS) & Mark Ryland (AWS)
Don’t let the perceived complexity of responsible AI keep you from deploying generative AI applications on AWS. In this chalk talk, we will present a framework for breaking down AI safety and security risks, introduce AWS best practices for keeping enterprise data secure in generative AI applications using zero trust principles, and mitigate safety risks using technologies such as Amazon Bedrock Guardrails. Discover as a group with fellow security leaders how to identify safety and security risks relevant to your workload, implement appropriate mitigation strategies, and measure efficacy over time.

NIS306 | Breakout session | Securing AWS networks: Observability meets defense-in-depth
Speakers: Anandprasanna Gaitonde (AWS), Ankush Goyal (AWS), Amish Shah (AWS)
AWS customers use multiple security services to build strong network defenses, but visibility into threats, misconfigurations, and vulnerabilities across multi-VPC and multi-account environments can remain a challenge. This session covers AWS network security fundamentals – Security Groups, NACLs, AWS Network Firewall, DNS Firewall, and Gateway Load Balancer—for a layered defense strategy. We will also highlight observability tools like VPC Flow Logs, Reachability Analyzer, and Network Access Analyzer to detect security gaps and troubleshoot access issues. By integrating these tools, organizations can proactively enhance network security, detect vulnerabilities, and ensure secure, scalable architectures across AWS accounts and environments.

NIS231 | Chalk talk | High noon duel: Live events tamed by AWS WAF
Speakers: Tzoori Tamam (AWS), Harith Gaddamanugu (AWS)
In this thrilling session, we’ll build a robust protection setup using AWS WAF and Amazon CloudFront, demonstrating how to fend off increasingly sophisticated live events. Learn to leverage Amazon CloudFront, configure rate-based rules, implement AWS WAF Managed Rule groups, bot control, and create custom defenses. As we construct our digital fortress, our resident “black hat” will launch progressively complex events, showcasing how each layer of defense performs under pressure. Suitable for both newcomers and experienced AWS security professionals.

NIS331 | Chalk talk | Enhance your cloud security infrastructure using Zero Trust techniques
Speakers: Pablo Sánchez Carmona (AWS), Adam Palmer (AWS)
Traditional perimeter-based security and network segmentation often fall short in today’s dynamic microservices environments, creating operational overhead and potential security gaps. Join us in this session to discuss how to evolve beyond conventional security models by implementing Zero Trust architecture in AWS. We will cover different services and techniques such as AWS Verified Access in the human-to-application connectivity, Amazon VPC Lattice for service-to-service communication, and the use of AWS Verified Permissions for fine-grained application authorization. We’ll explore how these services can work together to enable continuous authentication.

NIS332 | Chalk talk | Build secure connectivity with Amazon VPC Lattice and AWS PrivateLink
Speakers: Alexandra Huides (AWS), Jordan Rojas Garcia (AWS)
In this chalk talk, we review the best practices and reference architectures for building secure connectivity with Amazon VPC Lattice and AWS PrivateLink. We focus on service and resource oriented connectivity as we dive into the new VPC Lattice capabilities, such as support for VPC Resources and service network endpoints, and cross-region support for AWS PrivateLink.

NIS333 | Chalk talk | Build defense-in-depth network designs to safeguard apps and data
Speakers: Raghavarao Sodabathina (AWS), Brian Soper (AWS)
Strong adherence to architecture best practices and proactive controls are the foundation of web application security. These techniques allow developers to build applications that are more resilient. In this chalk talk, learn how to build a layered network security approach to achieve defense-in-depth; to protect, detect, and respond to issues faster; and to accelerate your secure migrations to AWS. Discover key considerations, best practices, and reference architectures that include Amazon VPC, Amazon Route 53, Amazon CloudFront, AWS WAF, AWS Shield, Application Load Balancer, and AWS Elastic Disaster Recovery to achieve your defense-in-depth objectives.

NIS431 | Chalk talk | Cloud network defense: Advanced visibility and analysis on AWS
Speakers: Kyle Hanrahan (AWS), Anand Kumar Mandilwar (AWS)
Organizations struggle to maintain comprehensive network visibility in complex cloud environments. This session demonstrates how to implement advanced network monitoring and analysis using AWS’s native services. Learn to leverage VPC Flow Logs, AWS Network Firewall Logs, Route 53 Resolver Logs, AWS WAF Logs and other data sources for traffic analysis. Discover practical implementation of tools for enhanced security and real-time monitoring. Walk away with reference architectures and best practices for building robust network visibility solutions that scale across your AWS environment while maintaining performance. Perfect for security teams modernizing their network defense strategy.

NIS321 | Lightning talk | How Meta enabled secure egress patterns using AWS Network Firewall
Speakers: Syed Shareef (AWS), Robin Rodriguez (AWS)
Meta envisions 2025 as the breakthrough year for its leading AI assistant, aiming to reach over 1 billion people with highly intelligent and personalized interactions. Partnering with AWS, Meta has made substantial investments in AI infrastructure, providing its developers with specialized compute resources for AI training. To secure this ambitious initiative, Meta has had to evolve not just their cloud security but also culture and mindset to secure a growing AWS footprint/infrastructure. Meta leverages AWS Network Firewall (ANF) to centrally inspect and filter VPC traffic before reaching external destinations, using rule-based filtering to control domain access, block malicious IPs, and prevent data exfiltration.

NIS322 | Lightning talk | I didn’t know Network Firewall could do that!
Speakers: Brandon Carroll (AWS), Mary Kay Sondecker (AWS)
This lightning talk will uncover powerful yet often overlooked capabilities that can transform your network security game. In just 20 minutes, we’ll speed through eye-opening features including flow capture and flush operations, advanced Suricata rule capabilities, dynamic packet filtering tricks, and lesser-known integration patterns that even experienced practitioners might have missed. From stateful traffic manipulation to sophisticated protocol inspection and real-world architectural patterns, you’ll discover practical techniques to leverage AWS Network Firewall’s full potential. Whether you’re managing complex multi-account deployments or hunting for advanced threats, this rapid-fire session will equip you with new tools for your security arsenal.

NIS323 | Lightning talk | WAF logs to security gold: A 20-minute dashboard revolution
Speakers: Emmanuel Isimah (AWS), Victor Babasanmi (AWS)
Drowning in AWS WAF logs? Transform raw security data into actionable insights with Amazon CloudWatch dashboards. In this high-energy session, discover how to build powerful visualizations that expose threats in real-time. We’ll cut through the complexity to show you battle-tested patterns for threat detection and alerting that security teams love. Twenty minutes to level up your WAF monitoring game – no fluff, just results.

NIS421 | Lightning talk | VPN-less access to AWS private services with AWS Verified Access
Speakers: John Sol (AWS), Mike Cornstubble (AWS)
In hybrid environments where employees need to access a file server outside their corporate network, they typically use a VPN. This session demonstrates how to establish secure, VPN-free connectivity to an Amazon FSx for Windows File Server using the new TCP protocol support of AWS Verified Access (AVA). Learn how AVA provides fine-grained access controls using AWS.

Interactive sessions (builders’ sessions, code talks, and workshops)

Interact with small groups led by an AWS expert providing interactive learning about how to build on AWS. Each builders’ session begins with a short explanation or demonstration of what attendees are building, then it’s your turn to build! The expert guides you end-to-end through this hands-on experience. Or join code talks, our code-focused interactive sessions where AWS experts lead a discussion featuring live coding or code samples as they illuminate the why behind AWS solutions. Attendees are encouraged to ask questions and follow along.

Workshops are 2-hour interactive sessions where you collaborate in teams or work individually to solve real-world challenges by using AWS services, making them perfect for hands-on learning. Each workshop begins with a brief lecture, followed by dedicated time to work through the problem.

Note: Don’t forget to bring your laptop to build alongside AWS experts.

NIS251 | Builders’ session | Build dashboards to gain visibility into your network perimeter
Speakers: Victor Babasanmi (AWS), Tom Adamski (AWS), Todd Pula (AWS), Vamsi Manthapuram (AWS)
Effective network security requires comprehensive visibility into your security posture and traffic patterns. This hands-on session demonstrates how to build and customize Amazon CloudWatch dashboards for real-time insights into AWS Network Firewall operations. Learn to visualize critical metrics including dropped packets, traffic patterns, and potential threats. We’ll explore creating dynamic widgets to track stateful rule matches, analyze top talkers, and identify suspicious patterns. Through step-by-step guidance, discover how to monitor bandwidth utilization, track rule effectiveness, and create custom alarms. Leave with ready-to-implement templates for enhancing your security operations. You must bring your laptop to participate.

NIS252 | Builders’ session | Mastering Amazon VPC Block Public Access for secure cloud networks
Speakers: Ankush Goyal (AWS), Salman Ahmed (AWS), Kunj Thacker (AWS)>, Ravi Kumar (AWS)
Join this interactive workshop to explore Amazon VPC Block Public Access, a feature designed for secure, scalable cloud environments. Learn to block ingress and egress traffic, enforce compliance, and configure granular exclusions for public and private subnets, with a focus on both IPv4 and IPv6 traffic. Through practical labs, you’ll enable Block Public Access, create exclusions, and use Reachability Analyzer to test connectivity before and after enabling the feature. By the end, you’ll be equipped to secure VPCs effectively while maintaining flexibility for modern workloads. You must bring your laptop to participate.

NIS351 | Builders’ session | Streamlining DNS resource sharing across multiple VPCs and accounts
Speakers: Aanchal Agrawal (AWS), Anushree Shetty (AWS), Mike Torro (AWS), Tyler Pack (AWS)
Amazon Route 53 Profiles is an innovative feature of Route 53 that enables the effortless sharing of hosted zones, resolver rules, and DNS firewall rules across multiple VPCs. This builders’ session will guide you through the process of creating Route 53 profiles and demonstrate how to restrict access using various features tailored to your specific needs, such as different environments. You must bring your laptop to participate.

NIS352 | Builders’ session | Accessing private VPC resources using CloudFront VPC origin
Speakers: Anushree Shetty (AWS), Ramya Mikkilineni (AWS), Aanchal Agrawal (AWS), Anjana Krishnan (AWS)
You can now privately access Amazon VPC resources, including load balancers and Amazon Elastic Compute Cloude (Amazon EC2) instances, and restrict these resources to be only accessed via Amazon CloudFront distribution through a new feature in CloudFront. In this builders’ session, we will set up a website located in a private subnet and access it via a CloudFront distribution. You must bring your laptop to participate.

NIS353 | Builders’ session | Scaling threat prevention on AWS with Suricata
Speakers: Ivo Pinto (AWS), Jesse Lepich (AWS), Michael Leighty (AWS), Miguel Silva (AWS)
Suricata is an open-source network intrusion prevention system (IPS) that includes a standard rule-based language for stateful network traffic inspection. AWS Network Firewall lets you define rules to inspect and control traffic to and from your VPC using IP, port, protocol, domain names, and general pattern matches. Building rules, in this format, for your security needs can be challenging but rewarding. During this session you will learn how you can utilize Suricata-compatible rules in AWS Network Firewall and build rulesets for common use cases as well as complex scenarios. You must bring your laptop to participate.

NIS354 | Builders’ session | Use AWS PrivateLink to set up private access to Amazon Bedrock
Speakers: Akshay Karanth (AWS), Du’An Lightfoot (AWS), Mike Gillespie (AWS), Salman Ahmed (AWS)
When building generative AI applications using Large Language Models on Amazon Bedrock, customers want to generate responses without going over the public internet or without exposing your proprietary data. This builders’ session introduces the Amazon Bedrock VPC endpoint, powered by AWS PrivateLink, as a solution for establishing secure and private connections between customer VPCs and Amazon Bedrock services. You’ll learn how this technology enables communication without public IP addresses, mitigating potential threat vectors from internet exposure. We’ll cover security challenges in generative AI, the architecture of VPC endpoint solution, and hands-on labs for implementation. You must bring your laptop to participate.

NIS451 | Builders’ session | Troubleshooting real-world perimeter protection scenarios
Speakers: Tzoori Tamam (AWS), Manuel Pata (AWS), Kaustubh Phatak (AWS)
Suspicious of an activity spike? Seeing odd traffic patterns? Introduced a new AWS WAF rule and want to make sure it is operating as it should? Join this session for a walkthrough of a day in the life of a security engineer operating AWS WAF, reviewing dashboards, exploring data in the logs, and building new dashboard widgets to make your life easier. You must bring your laptop to participate.

NIS341 | Code talk | A deep dive into Amazon VPC Lattice granular security
Speakers: Pablo Sánchez Carmona (AWS), Cristobal Lopez Callejon (AWS)
Join us for a session exploring Amazon VPC Lattice’s security capabilities and fine-grained access controls. We’ll explore authentication mechanisms, authorization policies, and service-level permissions that enable precise control over network traffic between services. You’ll learn how to leverage authorization policies in VPC Lattice to create layered security controls, and see practical examples of implementing Zero Trust principles within your application network. The session will cover best practices for auditing and monitoring service-to-service communications, managing cross-account access, and implementing security patterns for microservices architectures.

NIS342 | Code talk | Sticky situations: Building advanced AWS WAF honeypots for better security
Speakers: Harith Gaddamanugu (AWS), Manuel Pata (AWS)
Discover how to transform AWS WAF into a powerful threat intelligence platform by building sophisticated honeypots that attract, analyze, and adapt to emerging threats. In this code talk, we’ll demonstrate how to combine AWS WAF with AWS Lambda functions to create intelligent traps that not only capture malicious activity but also generate actionable security insights. Through live coding demonstrations, you’ll learn to implement advanced honeypot techniques including dynamic bait generation, automated attacker profiling, and real-time threat pattern analysis.

NIS231 | Chalk talk | High noon duel: Live events tamed by AWS WAF
Speakers: Tzoori Tamam (AWS), Harith Gaddamanugu (AWS)
In this thrilling session, we’ll build a robust protection setup using AWS WAF and Amazon CloudFront, demonstrating how to fend off increasingly sophisticated live attacks. Learn to leverage CloudFront, configure rate-based rules, implement WAF-managed rules and bot control, and create custom defenses. As we construct our digital fortress, our resident “black hat” will launch progressively complex attacks, showcasing how each layer of defense performs under pressure. Suitable for both newcomers and experienced AWS security professionals.

NIS431 | Chalk talk | Cloud network defense: Advanced visibility and analysis on AWS
Speakers: Kyle Hanrahan (AWS), Anand Kumar Mandilwar (AWS)
Organizations struggle to maintain comprehensive network visibility in complex cloud environments. This session demonstrates how to implement advanced network monitoring and analysis using AWS’s native services. Learn to leverage VPC Flow Logs, AWS Network Firewall Logs, Route 53 Resolver Logs, WAF Logs and other data sources for traffic analysis. Discover practical implementation of tools for enhanced security and real-time monitoring. Walk away with reference architectures and best practices for building robust network visibility solutions that scale across your AWS environment while maintaining performance. Perfect for security teams modernizing their network defense strategy.

Register Now

Don’t miss this opportunity to learn from industry experts and AWS leaders about building secure, automated, and observable cloud foundations. Register for AWS re:Inforce 2025 today to reserve your spot in these Network and Infrastructure Security sessions covering everything from Zero Trust implementations to advanced DDoS protection, network visibility, and defense-in-depth strategies. Browse the full re:Inforce catalog to explore additional tracks, partner sessions, and code talks that can complement your network security journey.

If you have feedback about this post, submit comments in the Comments section below.

2025 ISO and CSA STAR certificates now available with three new Regions

2025-06-06 Chinmaee Parulekar

Post Syndicated from Chinmaee Parulekar original https://aws.amazon.com/blogs/security/2025-iso-and-csa-star-certificates-now-available-with-three-new-regions/

Amazon Web Services (AWS) successfully completed an onboarding audit with no findings for ISO 9001:2015, 27001:2022, 27017:2015, 27018:2019, 27701:2019, 20000-1:2018, and 22301:2019, and Cloud Security Alliance (CSA) STAR Cloud Controls Matrix (CCM) v4.0. EY CertifyPoint auditors conducted the audit and reissued the certificates on May 26, 2025. The objective of the audit was to assess the level of compliance with the requirements of the applicable international standards.

During this onboarding audit, we onboarded three new AWS Regions [Asia Pacific (Thailand), Asia Pacific (Malaysia), Mexico (Central)] to the scope since the last certification issued on February 19, 2025.

For a full list of AWS services that are certified under ISO and CSA Star, see the AWS ISO and CSA STAR Certified page. Customers can also access the certifications in the AWS Management Console through AWS Artifact.

If you have feedback about this post, submit comments in the Comments section below.

How to use on-demand rotation for AWS KMS imported keys

2025-06-06 Jeremy Stieglitz

Post Syndicated from Jeremy Stieglitz original https://aws.amazon.com/blogs/security/how-to-use-on-demand-rotation-for-aws-kms-imported-keys/

Today, we’re announcing support for on-demand rotation of symmetric encryption AWS Key Management Service (AWS KMS) keys with imported key material (EXTERNAL origin). This new capability enables you to rotate the cryptographic key material of these keys without changing the key identifier (key ID or Amazon Resource Name (ARN)). Rotating keys helps you meet compliance requirements and security best practices that mandate periodic key rotation.

AWS KMS has long supported automatic key rotation for AWS KMS keys whose key material is generated by AWS KMS (AWS_KMS origin). Until now, AWS KMS customers who imported their own key material could not rotate that material without creating a new KMS key. This process called manual rotation required updating references to the older key identifiers. With today’s launch, the key ID of the imported key remains unchanged after rotation, so existing workloads are not disrupted. In this post, we tell you how the new capability works, look at key material expiry and deletion features unique to imported keys, and review pricing for this new feature.

How it works

When you create an AWS KMS key with EXTERNAL origin, AWS KMS assigns a fixed identifier to the key called the key ID. However, AWS KMS doesn’t generate key material for the cryptographic operations. You must import your own key material using the ImportKeyMaterial operation.

When you import key material, AWS KMS computes a unique key material identifier based on the key ID and the key material. Even if you import the same key material in different keys, AWS KMS will assign distinct key material identifiers. This computation uses a cryptographic hash so the key material identifier doesn’t reveal information about the key material itself. AWS KMS embeds this key material identifier in the ciphertext blob produced by symmetric encryption.

Until now, after you imported key material into an AWS KMS key, you could not import additional key material into that key to rotate the key. With this new feature launch, you can associate multiple imported key materials with a single, symmetric-encryption key. You can use the RotateKeyOnDemand operation to make the most recently imported key material the current key material. AWS KMS uses the current key material to generate new ciphertext. Unless deleted or expired, the other key materials remain available for decryption. When you present ciphertext for decryption, AWS KMS automatically selects the correct key material using the key material identifier embedded in the ciphertext.

To help improve the auditability that keys have rotated, we’ve added new identifiers in KMS API responses for the specific key material used. The KeyMaterialId is a new field that AWS KMS will return in addition to the KeyId. Similarly, the DescribeKey response for these keys now displays the identifier of the current key material as CurrentKeyMaterialId. The inclusion of the KeyMaterialId and CurrentKeyMaterialId in API responses makes key rotation more transparent.

Before we dive into the details, the following is an outline of the overall process to rotate an imported key:

Create a symmetric encryption KMS key with EXTERNAL origin
Import key material into the key using GetParametersForImport and ImportKeyMaterial APIs. The first key material becomes usable immediately. This part is unchanged and maintains backwards compatibility with the current behavior of AWS KMS.
Use the key to create ciphertext and decrypt it. You’ll notice the key material ID matches the CurrentKeyMaterialId displayed in the DescribeKey response.
When you want to rotate this key, import a second key material into the key. The ImportKeyMaterial API now has a new ImportType input parameter which lets you inform AWS KMS whether you are associating new key material with a key (--import-type NEW_KEY_MATERIAL) or re-importing previously associated key material (--import-type EXISTING_KEY_MATERIAL).
Use ListKeyRotations with --include-key-material ALL_KEY_MATERIAL to view both key materials. The key material state of the second key material will be PENDING_ROTATION.
Use the RotateKeyOnDemand operation to initiate on-demand key rotation.
Optionally, you can use the GetKeyRotationStatus operation to monitor the in-progress rotation. The response will contain OnDemandRotationStartDate only while the rotation is in progress.
Use ListKeyRotations with --include-key-material ALL_KEY_MATERIAL after rotation completes to view key materials associated with this key. The KeyMaterialState of the new key material you imported will change from PENDING_ROTATION to CURRENT. The key material state of the first key material will change from CURRENT to NON_CURRENT.
Use the key to create ciphertext and decrypt it. You’ll notice the CurrentKeyMaterialId is used for creating ciphertext, but the key material used for decryption is automatically determined by AWS KMS.

Using the AWS CLI for rotating an imported key

The following is a sample sequence of AWS KMS commands to exercise the import key rotation functionality using the AWS Command Line Interface (AWS CLI). The specific commands that follow work in Linux or MacOS environments and might need to be modified for use in a Windows environment. This functionality can also be exercised through the AWS SDKs. These operations, except for wrapping a key material for import, generate-data-key, and decrypt can be initiated in the AWS Management Console.

Step 1: Create a key and import key material

This section should be familiar to anyone who has used the existing import key functionality in AWS KMS.

Create a symmetric encryption key with EXTERNAL origin and save the key ARN. The initial state of this key is PendingImport.

export EXTERNAL_KEY1_ARN=$(aws kms create-key --origin EXTERNAL | tee /dev/stderr | jq -r .KeyMetadata.Arn)
{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "CreationDate": "2025-05-08T13:55:04.605000-07:00",
        "Enabled": false,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "PendingImport",
        "Origin": "EXTERNAL",
        "KeyManager": "CUSTOMER",
        "CustomerMasterKeySpec": "SYMMETRIC_DEFAULT",
        "KeySpec": "SYMMETRIC_DEFAULT",
        "EncryptionAlgorithms": [
            "SYMMETRIC_DEFAULT"
        ],
        "MultiRegion": false
    }
}

Generate a 256-bit (32-byte) key material to be imported. In the following command, we use OpenSSL to generate the imported key material.
```
openssl rand 32 > "KeyMaterial1.bin"
```

Use the get-parameters-for-import command to create the wrapping key and import token and save them to files. AWS KMS supports multiple wrapping algorithms; we use RSAES_OAEP_SHA_256 with a 4096-bit RSA key in the following example. The value of the ImportToken and PublicKey fields in the response has been trimmed for brevity.

export WRAPPING_ALGORITHM="RSAES_OAEP_SHA_256"
export WRAPPING_KEY_SPEC="RSA_4096"
export KMS_PARAMETERS_FOR_IMPORTED_KEY_MATERIAL1=$(aws kms get-parameters-for-import \
    --key-id "${EXTERNAL_KEY1_ARN}" \
    --wrapping-algorithm "${WRAPPING_ALGORITHM}" \
    --wrapping-key-spec "${WRAPPING_KEY_SPEC}" | tee /dev/stderr)
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "ImportToken": "AQECAHie/gkutEg+qLc1mscyCnHfNPS1aKVSxf6xd3PX05ny2wAADWIwgg1eBgkqhkiG9w0BBwaggg1PMIINSwIBADCCDUQGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMn8AovXPs/ 
…
O22maj4+9nDAgjVkxZQWhjA2VHEyORnw8Qb29X8gvjwmE8xhlNWbnM1zZlDcClDzfJriLVuoXAO92HK1Vihs5hiE8/9tu6DegtXfp28WVIVTttnGjkjdVmChC7cg7yZhs4xu1LzN39LLyR9Q1O/9EQYHbwYXgp6tpMt2JyGhH/lRt2kJl+BPUEfKNsWkoj0Cq3Y=",
    "PublicKey": "MIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAvXskfFFVoZVaZrEuN8UK3vckGww8tJHvsIxLmqKEbFt7D3dp/Jh8AoaVm2n/kdcGD5Hm8NwOQC40jsRjJKTqiTSr8/y8XEmpB0qi68EtKM8RyEmz7u1R5Vn6uoIZxKb/WMvVME/7tntyU4/uhVTGUlrfZItAV+
…
nuGzOptwcaprtT2iNthNZ38UQ2orETbfwdG6yZPt1qS8Jk/O0H+KtkcsNLHrDUE9XjQX4WDfkNbf/fyqaCLUpSTdONTLXqE16pVieLgrXr1845mECAwEAAQ==",
    "ParametersValidTo": "2025-05-09T13:56:45.632000-07:00"
}

export PUBLIC_KEY_FOR_IMPORTED_KEY_MATERIAL1=$(echo "${KMS_PARAMETERS_FOR_IMPORTED_KEY_MATERIAL1}" | jq -r .PublicKey) 
export IMPORT_TOKEN_FOR_IMPORTED_KEY_MATERIAL1=$(echo "${KMS_PARAMETERS_FOR_IMPORTED_KEY_MATERIAL1}" | jq -r .ImportToken) 

echo "${PUBLIC_KEY_FOR_IMPORTED_KEY_MATERIAL1}" | base64 -d > "WrappingKeyForKeyMaterial1.bin"
echo "${IMPORT_TOKEN_FOR_IMPORTED_KEY_MATERIAL1}" | base64 -d > "ImportTokenForKeyMaterial1.bin"

Wrap the key material using the wrapping key. We use OpenSSL, a popular open source cryptographic library to illustrate this step. For a detailed explanation of this step, see the AWS KMS Developer Guide.

openssl pkeyutl -encrypt -in "KeyMaterial1.bin" -out "WrappedKeyMaterial1.bin" -inkey "WrappingKeyForKeyMaterial1.bin" -keyform DER -pubin -encrypt -pkeyopt rsa_padding_mode:oaep -pkeyopt rsa_oaep_md:sha256 -pkeyopt rsa_mgf1_md:sha256

Import the key material. Optionally, you can assign a key material description. The description can be used to keep track of where the key material is durably maintained outside AWS KMS. This key material description is displayed alongside other information for this key material in the console and the ListKeyRotations API response. We also capture the key material ID from the response. In this example, the key material doesn’t expire. Optionally, you can set an expiration time.

export KEY_MATERIAL1_ID=$(aws kms import-key-material --key-id "${EXTERNAL_KEY1_ARN}" --encrypted-key-material fileb://WrappedKeyMaterial1.bin --import-token fileb://ImportTokenForKeyMaterial1.bin --expiration-model KEY_MATERIAL_DOES_NOT_EXPIRE --key-material-description "Q1 2025 key from HSM1" | tee /dev/stderr | jq .KeyMaterialId | tr -d \")
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241"
}

The key is now be enabled for use in cryptographic operations and the CurrentKeyMaterialId in the DescribeKey response should match ${KEY_MATERIAL1_ID}

echo "${KEY_MATERIAL1_ID}"
04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241

aws kms describe-key --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "CreationDate": "2025-05-08T13:55:04.605000-07:00",
        "Enabled": true,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "Enabled",
        "Origin": "EXTERNAL",
        "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
        "KeyManager": "CUSTOMER",
        "CustomerMasterKeySpec": "SYMMETRIC_DEFAULT",
        "KeySpec": "SYMMETRIC_DEFAULT",
        "EncryptionAlgorithms": [
            "SYMMETRIC_DEFAULT"
        ],
        "MultiRegion": false,
        "CurrentKeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241"
    }
}

Use ListKeyRotations to view key materials associated with the key. There should only be one key material with the same ID as in ${KEY_MATERIAL1_ID} and with a key material state of CURRENT.

aws kms list-key-rotations --key-id "${EXTERNAL_KEY1_ARN}" --include-key-material ALL_KEY_MATERIAL
{
    "Rotations": [
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
            "KeyMaterialDescription": "First key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE"
        }
    ],
    "Truncated": false
}

Step 2: Use the first key material to create and decrypt ciphertext

This step demonstrates how to verify the key material ID of your imported key in cryptographic operations.

Use the GenerateDataKey operation and capture the ciphertext. This operation returns a data key in both plaintext and ciphertext form. The KeyMaterialId in the response matches the identifier for the first key material.

export KEY1_CIPHERTEXT_BLOB1=$(aws kms generate-data-key --key-id "${EXTERNAL_KEY1_ARN}" --number-of-bytes 32 | tee /dev/stderr | jq -r .CiphertextBlob)
{
    "CiphertextBlob": "AQIBAHgE9c9Q7o8Ff5dLmCCrj6iDfSg2OoWo+O8GpGyTrcWyQQFGzFweTmSjHkrflCzUMvY+AAAAfjB8BgkqhkiG9w0BBwagbzBtAgEAMGgGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMdNV4KMSitkzp9j0pAgEQgDuXnjXR/KvDWjf3KfTAksjAyQJETPoC7zBq6ND2n2c7IUT/EUn+cbmBhXx5P/EI3l9drqsP/6NS5rRYSw==",
    "Plaintext": "3OQAOySTM3/E7QHH3a+GGzEz3HKS30rUcvUep+uueas=",
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
    "KeyOrigin": "EXTERNAL"
}

Decrypt the ciphertext and compare it to the plaintext key. The KeyMaterialId in the response matches the identifier for the first key material. The plaintext in decrypt response matches the plaintext data key in the GenerateDataKey response.

aws kms decrypt --ciphertext-blob "${KEY1_CIPHERTEXT_BLOB1}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "Plaintext": "3OQAOySTM3/E7QHH3a+GGzEz3HKS30rUcvUep+uueas=",
    "EncryptionAlgorithm": "SYMMETRIC_DEFAULT",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
    "KeyOrigin": "EXTERNAL"
}

Step 3: Import a second key material into the key for on-demand rotation

Import key rotations start with importing new key material into this key.

Generate a second key material (also 256 bits in length).
```
openssl rand 32 > "KeyMaterial2.bin"
```

Use the get-parameters-for-import command to create the wrapping key and import token for the second key material to be imported. The value of the ImportToken and PublicKey fields in the response has been trimmed for brevity.

export WRAPPING_ALGORITHM="RSAES_OAEP_SHA_256"
export WRAPPING_KEY_SPEC="RSA_4096"
export KMS_PARAMETERS_FOR_IMPORTED_KEY_MATERIAL2=$(aws kms get-parameters-for-import --key-id "${EXTERNAL_KEY1_ARN}" --wrapping-algorithm "${WRAPPING_ALGORITHM}" --wrapping-key-spec "${WRAPPING_KEY_SPEC}" | tee /dev/stderr)
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "ImportToken": "AQECAHie/gkutEg+qLc1mscyCnHfNPS1aKVSxf6xd3PX05ny2wAADWAwgg1cBgkqhkiG9w0BBwagIINE0aPHP5vUwaR6nukZvp8aGKeKcN3EDCsWtTNEF2RJFLazEeHItB84viBqlNIPpf8gNcCad+ODRrCyZeAisqeyIOPRoNn+vS3KMHIpMjhBUsLF6yys7FJts7P+ncF9n1bmTC4qYln5BaZ9FoI1P4NikEGiDakG8rtVJM7jWKJqVipifZvhlY8EKM8hE8e7zqR3C13TQhJXec0rVaqsFxSyjX/hbKkY55mDw36xMJaK6G9F3Fi9Pg5fKc/oOG4gUGXKSSZBoL3Jt3ssr7ACPuzFfhMOaqQ/
… /mfaCPAJN5dH0cmppKiOYqA7RSMsx2/sPMWekVLh6j38RMqZHHIq67jos0p0h8EseWpEmI2mnbJ/yoWLY/DkOEami+6scIPgvyvVzRM9d2hR7BCd0/9kwZxIN6WX0",
    "PublicKey": "MIICIjANBgkqhkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAiGzUXf7XWN5nN5GOo/5McrsIYfiLQrDjVdOen0v7QqQcQ04p+ozI5NDGLADYd0bLNn4cm2xrlxqUjTqrtW0U8k9A/5DurYprmNmF6M+Q+zj+EkR5XoLmcuLALifHXDMI0Af20L46NK6uVKYqDl7gP4xXOP+ka57NfHmWGWDGTOKUQNZSS0emBe0UGS
…
7AC0FuwndrShwNxiKEIWPi3NhJ70jkYCrle1mQqKHpsFe6bGKLvp1gdMWByy0fY9lOH9HfoQB0FonEDZjldWBn3YXtP/eUwrgyyCk/Po7i5pjA6cjf/mBTmwmVD/jBAxW45YHFaLaJGPillaQy0Xeu89Mdwq8uEh8AixwTKbQ9Jwk3RG5A33QFj6Qb67sCAwEAAQ==",
    "ParametersValidTo": "2025-05-09T14:10:08.028000-07:00"
}
export PUBLIC_KEY_FOR_IMPORTED_KEY_MATERIAL2=$(echo "${KMS_PARAMETERS_FOR_IMPORTED_KEY_MATERIAL2}" | jq -r .PublicKey)
export IMPORT_TOKEN_FOR_IMPORTED_KEY_MATERIAL2=$(echo "${KMS_PARAMETERS_FOR_IMPORTED_KEY_MATERIAL2}" | jq -r .ImportToken)

echo "${PUBLIC_KEY_FOR_IMPORTED_KEY_MATERIAL2}" | base64 -d > "WrappingKeyForKeyMaterial2.bin"
echo "${IMPORT_TOKEN_FOR_IMPORTED_KEY_MATERIAL2}" | base64 -d > "ImportTokenForKeyMaterial2.bin"

Wrap the second key material using the wrapping key.

openssl pkeyutl -encrypt -in "KeyMaterial2.bin" -out "WrappedKeyMaterial2.bin" -inkey "WrappingKeyForKeyMaterial2.bin" -keyform DER -pubin -encrypt -pkeyopt rsa_padding_mode:oaep -pkeyopt rsa_oaep_md:sha256 -pkeyopt rsa_mgf1_md:sha256

Import the second key material. Optionally, you can assign a key material description. We also capture the key material ID from the response. In this example, the key material doesn’t expire. Optionally, you can set an expiration time.

Note: This call will fail if you omit the import-type parameter or set it to EXISTING_KEY_MATERIAL. Specifying import-type as NEW_KEY_MATERIAL allows the API caller to associate additional key material with the imported key.

export KEY_MATERIAL2_ID=$(aws kms import-key-material --key-id "${EXTERNAL_KEY1_ARN}" --encrypted-key-material fileb://WrappedKeyMaterial2.bin --import-token fileb://ImportTokenForKeyMaterial2.bin --expiration-model KEY_MATERIAL_DOES_NOT_EXPIRE --key-material-description "Q2 2025 key from HSM1" --import-type NEW_KEY_MATERIAL | tee /dev/stderr | jq .KeyMaterialId | tr -d \")
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e"
}

Use ListKeyRotations to view key materials associated with the key. There should now be two key materials. The key material state of the second key material should be PENDING_ROTATION.

aws kms list-key-rotations --key-id "${EXTERNAL_KEY1_ARN}" --include-key-material ALL_KEY_MATERIAL
{
    "Rotations": [
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
            "KeyMaterialDescription": "First key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE"
        },
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
            "KeyMaterialDescription": "Second key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "PENDING_ROTATION",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE"
        }
    ],
    "Truncated": false
}

The CurrentKeyMaterialId in DescribeKey response should still be ${KEY_MATERIAL1_ID}.

echo "${KEY_MATERIAL1_ID}"
04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241

aws kms describe-key --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "CreationDate": "2025-05-08T13:55:04.605000-07:00",
        "Enabled": true,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "Enabled",
        "Origin": "EXTERNAL",
        "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
        "KeyManager": "CUSTOMER",
        "CustomerMasterKeySpec": "SYMMETRIC_DEFAULT",
        "KeySpec": "SYMMETRIC_DEFAULT",
        "EncryptionAlgorithms": [
            "SYMMETRIC_DEFAULT"
        ],
        "MultiRegion": false,
        "CurrentKeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241"
    }
}

Step 4: Use on-demand rotation to update the current key material

This step moves the current key material for this key to the newly imported key material.

Use the RotateKeyOnDemand operation to initiate an on-demand key rotation. If the key material in PENDING_ROTATION state is deleted or expires before initiating on-demand rotation, this operation will fail.
```
aws kms rotate-key-on-demand --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041"
}
```
AWS KMS uses a background worker to perform the rotation, so there’s a delay between initiating the on-demand key rotation and its completion. Use the GetKeyRotationStatus command to monitor the rotation status. Until the rotation is completed, the GetKeyRotationStatus response will include the OnDemandRotationStartDate field. When this field disappears, the on-demand key rotation is complete.
```
aws kms get-key-rotation-status --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyRotationEnabled": false,
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "OnDemandRotationStartDate": "2025-05-08T14:12:23.096000-07:00"
}
```

Use ListKeyRotations when rotation completes. The second key material changes state from PENDING_ROTATION to CURRENT.

aws kms list-key-rotations --key-id "${EXTERNAL_KEY1_ARN}" --include-key-material ALL_KEY_MATERIAL
{
    "Rotations": [
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
            "KeyMaterialDescription": "First key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "NON_CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE"
        },
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
            "KeyMaterialDescription": "Second key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
            "RotationDate": "2025-05-08T14:12:30.890000-07:00",
            "RotationType": "ON_DEMAND"
        }
    ],
    "Truncated": false
}

Rotation changes the current key material, which is reflected in the CurrentKeymaterialId field in the DescribeKey response. It should now match ${KEY_MATERIAL2_ID}.

echo "${KEY_MATERIAL1_ID}"
04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241

echo "${KEY_MATERIAL2_ID}"
b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e

aws kms describe-key --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "CreationDate": "2025-05-08T13:55:04.605000-07:00",
        "Enabled": true,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "Enabled",
        "Origin": "EXTERNAL",
        "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
        "KeyManager": "CUSTOMER",
        "CustomerMasterKeySpec": "SYMMETRIC_DEFAULT",
        "KeySpec": "SYMMETRIC_DEFAULT",
        "EncryptionAlgorithms": [
            "SYMMETRIC_DEFAULT"
        ],
        "MultiRegion": false,
        "CurrentKeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e"
    }
}

Step 5: Use the second key material to create and decrypt ciphertext

Similar to Step 2, this step demonstrates how to verify that the key material ID of your imported key in cryptographic operations has been rotated.

Use the GenerateDataKey operation and capture the ciphertext. This operation returns a data key in both plaintext and ciphertext forms. Note that the KeyMaterialId returned in the response matches the identifier of the second key material.

echo "${KEY_MATERIAL2_ID}"
b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e

export KEY1_CIPHERTEXT_BLOB2=$(aws kms generate-data-key --key-id "${EXTERNAL_KEY1_ARN}" --number-of-bytes 32 | tee /dev/stderr | jq -r .CiphertextBlob)
{
    "CiphertextBlob": "AQIBAHi0dJw62Bc3nKmyJkln/nkfyz/pKmoUygZlfDsebq/rTgGjuxQDA9WWHvqF6ZipVLZZAAAAfjB8BgkqhkiG9w0BBwagbzBtAgEAMGgGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMdXVYuY1Sa/Jb/SvrAgEQgDtQUp2gClGPBOuxFO89oAZmmkDghEKM6rXgCeS5A/NCLyX7UPdcpsJG/cJAFdjPE9sCfWjOUXCv9JTWmQ==",
    "Plaintext": "acSB+a6W2TQ3dsb2c78yDaZLi9HcuHSubeQkFaAdl1Y=",
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
    "KeyOrigin": "EXTERNAL"
}

Decrypt the ciphertext and compare it to the plaintext key. The KeyMaterialId returned in the response matches the identifier of the second key material. The plaintext in decrypt response matches the plaintext data key in the previous GenerateDataKey response.

echo "${KEY_MATERIAL2_ID}"
b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e

aws kms decrypt --ciphertext-blob "${KEY1_CIPHERTEXT_BLOB2}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "Plaintext": "acSB+a6W2TQ3dsb2c78yDaZLi9HcuHSubeQkFaAdl1Y=",
    "EncryptionAlgorithm": "SYMMETRIC_DEFAULT",
    "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
    "KeyOrigin": "EXTERNAL"
}

AWS KMS automatically uses the correct key material based on the ciphertext. When you decrypt the ciphertext produced in Step 2, AWS KMS uses the first key material.

echo "${KEY_MATERIAL1_ID}"
04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241

aws kms decrypt --ciphertext-blob "${KEY1_CIPHERTEXT_BLOB1}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "Plaintext": "3OQAOySTM3/E7QHH3a+GGzEz3HKS30rUcvUep+uueas=",
    "EncryptionAlgorithm": "SYMMETRIC_DEFAULT",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
    "KeyOrigin": "EXTERNAL"
}

Step 6: Delete key material, make the key unusable

With the launch of this feature, the DeleteImportedKeyMaterial operation takes an optional KeyMaterialId parameter. If no KeyMaterialId is specified, AWS KMS deletes the current key material. This maintains backwards compatibility with existing behavior.

Delete the first imported key material by specifying its identifier.

aws kms delete-imported-key-material --key-id "${EXTERNAL_KEY1_ARN}" --key-material-id "${KEY_MATERIAL1_ID}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241"
}

Deleting the key material causes the key state to change to PendingImport.

aws kms describe-key --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "CreationDate": "2025-05-08T13:55:04.605000-07:00",
        "Enabled": false,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "PendingImport",
        "Origin": "EXTERNAL",
        "KeyManager": "CUSTOMER",
        "CustomerMasterKeySpec": "SYMMETRIC_DEFAULT",
        "KeySpec": "SYMMETRIC_DEFAULT",
        "EncryptionAlgorithms": [
            "SYMMETRIC_DEFAULT"
        ],
        "MultiRegion": false,
        "CurrentKeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e"
    }
}

ListKeyRotations also reflects the first key material as PendingImport.

aws kms list-key-rotations --key-id "${EXTERNAL_KEY1_ARN}" --include-key-material ALL_KEY_MATERIAL
{
    "Rotations": [
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
            "KeyMaterialDescription": "First key material",
            "ImportState": "PENDING_IMPORT",
            "KeyMaterialState": "NON_CURRENT"
        },
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
            "KeyMaterialDescription": "Second key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
            "RotationDate": "2025-05-08T14:12:30.890000-07:00",
            "RotationType": "ON_DEMAND"
        }
    ],
    "Truncated": false
}

Cryptographic operations fail with a KMSInvalidStateException when a key is in PendingImport state even though the key material required to decrypt the specific ciphertext blob is imported into AWS KMS.

aws kms decrypt --ciphertext-blob "${KEY1_CIPHERTEXT_BLOB2}"

An error occurred (KMSInvalidStateException) when calling the Decrypt operation: arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041 is pending import.

Step 7: Reimport key material to enable the key

You need to re-import all expired or deleted key materials associated with a key for the key to become usable again.

Re-import the missing key material. For this example, we reused the wrapped key and import token we already have. This is an optimization. Optionally, you can get new parameters for wrapping and importing the key material.

aws kms import-key-material --key-id "${EXTERNAL_KEY1_ARN}" --encrypted-key-material fileb://WrappedKeyMaterial1.bin --import-token fileb://ImportTokenForKeyMaterial1.bin --expiration-model KEY_MATERIAL_DOES_NOT_EXPIRE
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241"
}


aws kms list-key-rotations --key-id "${EXTERNAL_KEY1_ARN}" --include-key-material ALL_KEY_MATERIAL
{
    "Rotations": [
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
            "KeyMaterialDescription": "First key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "NON_CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE"
        },
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
            "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
            "KeyMaterialDescription": "Second key material",
            "ImportState": "IMPORTED",
            "KeyMaterialState": "CURRENT",
            "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
            "RotationDate": "2025-05-08T14:12:30.890000-07:00",
            "RotationType": "ON_DEMAND"
        }
    ],
    "Truncated": false
}

Use DescribeKey to validate the key is now enabled.

aws kms describe-key --key-id "${EXTERNAL_KEY1_ARN}"
{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
        "CreationDate": "2025-05-08T13:55:04.605000-07:00",
        "Enabled": true,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "Enabled",
        "Origin": "EXTERNAL",
        "ExpirationModel": "KEY_MATERIAL_DOES_NOT_EXPIRE",
        "KeyManager": "CUSTOMER",
        "CustomerMasterKeySpec": "SYMMETRIC_DEFAULT",
        "KeySpec": "SYMMETRIC_DEFAULT",
        "EncryptionAlgorithms": [
            "SYMMETRIC_DEFAULT"
        ],
        "MultiRegion": false,
        "CurrentKeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e"
    }
}

You can use the following commands to validate that the key works:

aws kms decrypt --ciphertext-blob "${KEY1_CIPHERTEXT_BLOB1}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "Plaintext": "3OQAOySTM3/E7QHH3a+GGzEz3HKS30rUcvUep+uueas=",
    "EncryptionAlgorithm": "SYMMETRIC_DEFAULT",
    "KeyMaterialId": "04f5cf50ee8f057f974b9820ab8fa8837d28363a85a8f8ef06a46c93adc5b241",
    "KeyOrigin": "EXTERNAL"
}

aws kms decrypt --ciphertext-blob "${KEY1_CIPHERTEXT_BLOB2}"
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041",
    "Plaintext": "acSB+a6W2TQ3dsb2c78yDaZLi9HcuHSubeQkFaAdl1Y=",
    "EncryptionAlgorithm": "SYMMETRIC_DEFAULT",
    "KeyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e",
    "KeyOrigin": "EXTERNAL"
}

CloudTrail logging

AWS CloudTrail now includes the key material ID in the additionalEventData field for operations using symmetric-encryption keys (both AWS_KMS and EXTERNAL origin). The following is a sample CloudTrail event for the AWS KMS decrypt operation:

{
    "eventVersion": "1.11",
    "userIdentity": {
        "type": "AssumedRole",
        "principalId": "AROAEXAMPLE:alice",
        "arn": "arn:aws:sts::111122223333:assumed-role/key-user/alice",
        "accountId": "111122223333",
        "accessKeyId": "ACCESSKEY",
        "sessionContext": {
            "sessionIssuer": {
                "type": "Role",
                "principalId": "AROAEXAMPLE",
                "arn": "arn:aws:iam::111122223333:role/key-user",
                "accountId": "111122223333",
                "userName": "key-user"
            },
            "attributes": {
                "creationDate": "2025-05-09T18:12:33Z",
                "mfaAuthenticated": "false"
            }
        }
    },
    "eventTime": "2025-05-09T18:15:02Z",
    "eventSource": "kms.amazonaws.com",
    "eventName": "Decrypt",
    "awsRegion": "us-east-1",
    "sourceIPAddress": "1.2.3.4",
    "userAgent": "aws-cli/2.9.19 Python/3.9.11 Darwin/24.4.0 exe/x86_64 prompt/off command/kms.decrypt",
    "requestParameters": {
        "encryptionAlgorithm": "SYMMETRIC_DEFAULT"
    },
    "responseElements": null,
    "additionalEventData": { "keyMaterialId": "b4749c3ad817379ca9b2264967fe791fcb3fe92a6a14ca06657c3b1e6eafeb4e" },
    "requestID": "68c6013e-24af-4a66-aa88-64cdb27d693d",
    "eventID": "1feca1a6-cbf0-491d-897f-31c52c54c783",
    "readOnly": true,
    "resources": [{
        "accountId": "111122223333",
        "type": "AWS::KMS::Key",
        "ARN": "arn:aws:kms:us-east-1:111122223333:key/97c8e55d-5b04-45a1-8a01-1febde0dd041"
    }],
    "eventType": "AwsApiCall",
    "managementEvent": true,
    "recipientAccountId": "111122223333",
    "eventCategory": "Management",
    "tlsDetails": {
        "tlsVersion": "TLSv1.3",
        "cipherSuite": "TLS_AES_256_GCM_SHA384",
        "clientProvidedHostHeader": "kms.us-east-1.amazonaws.com"
    }
}

Key expiry and key deletion capabilities unique to imported keys

Unlike standard KMS keys that you create within AWS KMS, imported keys offer two unique capabilities for enhanced controls over key material within AWS.

When importing key material into a KMS key, you can optionally set an expiration date and time, up to 365 days from the import date. If you don’t specify an expiration, the key material doesn’t expire. When the expiration time is reached, AWS KMS immediately deletes the key material and the KMS key becomes unusable. This is in contrast to the 7–30 day waiting period required for KMS keys whose key material is generated by AWS KMS. To re-enable the key, you must reimport the key material. With key rotation, you can continue to set expiration periods for new key material that you import.
Unique to KMS imported keys, you can also delete specific key material without deleting the entire KMS key. Deleting the key material of a KMS imported key is temporary and reversible. To restore the key, reimport its key material.

Key expiry and import key material deletion can be useful if you need to demonstrate immediate key suspension in the cloud or when you want to temporarily seed AWS KMS with key material that can be inserted and repeatedly removed from cloud access (hydration and re-hydration of keys for improved digital sovereignty).

Special considerations

AWS KMS is designed to keep imported key material highly available. But AWS KMS doesn’t maintain the durability of imported key material at the same level as key material that AWS KMS generates. You must retain a copy of the imported key material outside of AWS KMS in a system that you control. We recommend that you store an exportable copy of the imported key material in a key management system, such as an HSM. As a best practice, you should store a reference to the KMS key ARN and key material description alongside the exportable copy of the key material.

The deletion or expiration of any key material associated with a KMS key makes that key unusable. You must re-import all the key materials associated with a key before it can be used for cryptographic operations.

The following types of KMS keys with imported key material do not support on-demand key rotation, but you can continue to use manual rotation with these keys.

Asymmetric keys
HMAC keys
Multi-AWS Region keys

Features and benefits

This new capability includes several important features:

Maintain key identifiers: Rotate key material while keeping the same AWS KMS key ID and ARN.
Flexible rotation: Rotate keys on-demand to meet your security requirements or use an external key manager to set a rotation schedule that can drive new key rotations into AWS KMS.
Audit trail: Track key material usage through CloudTrail logs.
Metadata management: Add unique descriptions to each key material version.
Retains support for key expiry and import key deletion (features unique to KMS imported keys)

Pricing

For AWS KMS keys that rotate automatically or on-demand, each key incurs a base cost, and the first two rotations add $1 per month (prorated hourly) in additional charges. The pricing is capped after the second rotation, meaning subsequent rotations beyond the second one aren’t billed. This simplified pricing provides you with more predictable costs while maintaining the flexibility to rotate keys frequently to meet your compliance and security audit requirements.

Getting started

You can start using this feature today in all AWS Regions where AWS KMS is available. To learn more, visit the AWS KMS Developer Guide.

We’re excited to see how you use this new capability to enhance your key management practices. Leave a comment below or on the AWS re:Post community forum to let us know what you think.

Many voices, one community: Three themes from RSA Conference 2025

2025-06-05 Anne Grahn

Post Syndicated from Anne Grahn original https://aws.amazon.com/blogs/security/many-voices-one-community-three-themes-from-rsa-conference-2025/

RSA Conference (RSAC) 2025 drew 730 speakers, 650 exhibitors, and 44,000 attendees from across the globe to the Moscone Center in San Francisco, California from April 28 through May 1.

The keynote lineup was eclectic, with 37 presentations featuring speakers ranging from NBA Hall of Famer Earvin “Magic” Johnson to public and private-sector luminaries such as former US National Cyber Director Chris Inglis, U.S. Secretary of Homeland Security Kristi Noem, and cryptography experts Tal Rabin, Whitfield Diffie, and Adi Shamir.

Topics aligned with this year’s conference theme, “Many Voices. One Community,” and focused on the security industry’s shared drive to foresee risks, counter threats, and embrace new challenges.

Three themes caught our attention: agentic AI, cryptography, and public-private collaboration.

Agentic AI

The potential of agentic AI to augment human decision-making was a common thread among conversations at the conference. Numerous sessions touched on the topic, and the desire of attendees to understand the technology and learn how to balance its risks and opportunities was clear.

Separating hype from reality

An AI agent is a software program that can interact with its environment (as detailed in Figure 1), collect data, and use the data to perform self-determined tasks to meet predetermined goals.

Figure 1: Generative AI agents

Agentic systems offer a fundamentally different approach compared to traditional software, particularly in their ability to handle complex, dynamic, and domain-specific challenges. While traditional systems rely on rule-based automation and structured data, agentic systems use large language models (LLMs)—a subset of generative AI—to operate autonomously. Agents can learn from interactions with users, and make nuanced, context-aware decisions while keeping human analysts in the loop.

Numerous RSAC speakers alluded to AI agents as the next frontier in enterprise transformation. Gartner® predicts that: “By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024,” and “at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from zero percent in 2024.”

However, as organizations build AI agents, understanding the concerns that come with them is critical.

“Agentic AI presents tremendous opportunities to deliver business value and innovative security outcomes. Production deployments require a balance between its capabilities, and robust security and trust mechanisms.”
—Hart Rossman, Global Services Security Vice President at AWS

In the RSAC keynote session The Five Most Dangerous New Attack Techniques…and What to Do for Each, Rob Lee, Chief of Research and Head of Faculty at SANS Institute noted that while security teams are embracing AI to amplify productivity, threat actors are doing the same. He pointed to MIT research that shows adversarial agent systems executing attack sequences are 47 times faster than human operators, with a 93 percent success rate in privilege escalation paths.

Safeguarding GenAI & Agentic Apps, Top 10 Risks in 2025, a half-day Open Worldwide Application Security Project (OWASP) event, focused on helping attendees distinguish real threats from hype. OWASP Gen AI Security Project team members and industry experts reviewed the 2025 OWASP Top 10 List for LLM and GenAI (shown in Figure 2), and introduced Agentic AI—Threats and Mitigations—the first in a series of guides from the OWASP Agentic Security Initiative (ASI) to provide a threat-model-based reference of emerging agentic threats and mitigations. Content feedback can be submitted to ASI in advance of the guide’s next release.

Figure 2: 2025 OWASP Top 10 for LLM Applications

Agentic AI wins Cybersecurity Startup Accelerator

The second annual AWS and CrowdStrike Cybersecurity Startup Accelerator, in collaboration with the NVIDIA Inception program, took place during RSAC. A panel of judges—including George Kurtz, Founder and CEO of CrowdStrike, CJ Moses, Chief Information Security Officer at Amazon, and David Reber Jr., Chief Security Officer at NVIDIA—evaluated startups on innovation, market relevance, and go-to-market potential. Terra Security, a provider of agentic AI-powered, continuous web application penetration testing, was selected from a group of 10 finalists who pitched live. Two runners-up, Kenzo Security and Rig Security, were also recognized for their standout approaches to agentic AI-driven security.

Addressing AI risks

The need to consider your security posture when assessing overall AI readiness was emphasized throughout the conference. A defense-in-depth architecture can help mitigate risks with multiple layers of protection across both traditional and AI software components. Innovative solutions such as AI red teaming, AI behavioral sandboxing, and advanced tracing and evaluation of generative AI agents can enhance your security strategy with a proactive approach to securing AI.

Visit the following resources to help design, build, and operate AI systems: DevsecOps Revolution: Unleashing Generative AI for Automated Excellence, AWS generative AI security, responsible AI, and the Amazon AGI Labs Blog.

Cryptography

Encryption was another key topic. The FIDO Alliance hosted a half-day seminar that focused on developments in the global movement to passwordless technology such as passkeys—cryptographic keys designed to replace passwords by combining the power of public key cryptography with biometric authentication.

In Dude, Where’s My Password? The Challenges of Getting to Passwordless, Andy Ozment, Chief Technology Risk Officer and Executive Vice President at Capital One noted that 88 percent of data compromised in basic web application attacks reported in 2024 involved stolen credentials. Ozment pointed out that “going passwordless” through a combination of X.509 device certificates and FIDO2 passkeys presented Capital One with an opportunity to nearly eliminate entire classes of threats (as detailed in Figure 3), while increasing the quality of user experience.

Figure 3: Using passkeys to reduce risk while advancing user experience

Along the way, Ozment said, Capital One’s journey to passwordless was enabled by its transition from on-premises technology to going “all-in” on the public cloud. Watch the recording of his session or view the slides to learn more.

Post-quantum encryption

The state of post-quantum encryption was detailed in the popular Cryptographer’s Panel, moderated by Tal Rabin, Senior Principal Applied Scientist at AWS.

Panelist Vinod Vaikuntanathan, Professor at MIT underscored the impact of the quantum-resistant algorithm standardization process (Figure 4) started by the National Institute of Standards and Technology (NIST) in 2016. “We now have two public key encryption algorithms, and three new digital signature algorithms that are standardized,” he pointed out.

Figure 4: Post-quantum encryption algorithms

The panelists agreed that even though quantum computers aren’t here yet, the time to deploy these algorithms is now. NIST recommends phasing out existing encryption methods by 2030 in its Transition to Post-Quantum Cryptography Standards report. However, Vaikuntanathan and Adi Shamir, the “s” in the Rivest–Shamir–Adleman (RSA) public-key cryptosystem, advise organizations to take a hybrid approach that combines classic encryption algorithms such as RSA or Elliptic-curve Diffie–Hellman (ECDH) with post-quantum algorithms such as Module-Lattice-based Key Encapsulation Mechanism (ML-KEM). This approach, which is used by AWS and recommended by The European Commission, offers protection against both current and future threats.

RSAC Award for Excellence in the Field of Mathematics

Dr. Shai Halevi, Senior Principal Applied Scientist at AWS, was presented with the Award for Excellence in the Field of Mathematics for remarkable contributions to many areas of cryptography, including fundamental theory, advanced cryptographic primitives, secure multi-party computations, homomorphic encryption, and cryptographic code obfuscation.

Figure 5: Dr. Shai Halevi receives RSAC Award for Excellence in the Field of Mathematics

End-to-end encryption

Concerns about the recent US government group chat leak were also raised during the discussion. Public-key cryptography pioneer Whitfield Diffie noted that the use of an encrypted consumer messaging app to communicate classified information broke archiving laws. Because some commercial tools use 256-bit Advanced Encryption Standard (AES) encryption, which is “good enough” to protect communications, he predicted an increase in the use of consumer applications to protect sensitive information in unapproved ways.

The Cybersecurity and Infrastructure Security Agency (CISA) and the Federal Bureau of Investigation (FBI) recently advised individuals and organizations to start using encrypted messaging apps. However, as the role of these applications in business communication expands, it’s important not to lose sight of recordkeeping and compliance obligations. Organizations should consider solutions that offer administrative controls and data retention capabilities along with encryption.

AWS Wickr, for example, is a messaging and collaboration service that protects messaging, calling, file sharing, screen sharing, and location sharing with 256-bit end-to-end encryption. The data retention and administrative controls that it provides help customers meet regulatory requirements and manage user and device data remotely.

Wickr is Department of Defense Cloud Computing Security Requirements Guide Impact Level 5 (DoD CC SRG IL5) and Federal Risk and Authorization Management Program (FedRAMP) High authorized in the AWS GovCloud (US-West) Region. It also meets compliance programs and standards such as Health Insurance Portability and Accountability Act (HIPAA) eligibility, International Organization for Standardization (ISO) 27001, and System and Organization Controls (SOC) 1, 2, and 3.

Visit the AWS News Blog and the AWS Security Blog to learn about AWS passkey multi-factor authentication, how AWS is migrating to post quantum cryptography (PQC), and how we can help you implement a layered encryption strategy for your organization.

Public-private collaboration

Numerous sessions underlined the importance of collaboration to strengthening security. In his keynote, Johnson called attention to a lesson he learned on the basketball court—his peers made him stronger. “Larry Bird made me a better basketball player,” he said, relating his experience to the need for security teams to assist and learn from each other.

In Making America Safe Again Through Cyber Defense, Kristi Noem, U.S. Secretary of Homeland Security equated cybersecurity with national security, and insisted that building on public-private partnerships is “incredibly important.” “Our goal,” she said, “is to use our maximum effect of cooperation to make sure that we’re going after bad actors.”

After assuring attendees that CISA will continue to be America’s cyber defense agency, she urged congress to reauthorize the Cybersecurity Information Sharing Act of 2015. The law, which is set to expire in September, incentivizes businesses to share threat indicators with the Department of Homeland Security (DHS) and helps make sure that both the federal government and companies can take collaborative steps to address threats.

Panelists at an offsite threat intelligence discussion reiterated the ability of private industry to supplement government security capabilities. Adam Meyers, Senior VP, Counter Adversary Operations at CrowdStrike pointed out that technology companies often have more data and signals than governments. The CrowdStrike Falcon solution, he said, processes over 6 trillion events per day, and 55 million events per second at peak. This volume facilitates the detection of threat patterns that might otherwise go unnoticed.

Similarly, Moses noted that the size and scale of AWS infrastructure gives us unique visibility into internet traffic. Our global network of sensors and associated disruption tools observe over 700 million threat interactions every day, out of which 450 million can be classified as malicious. Internal threat intelligence tools such as MadPot, our sophisticated global honeypot system, produce high-fidelity findings (pieces of relevant information) that can be used to drive proactive intelligence sharing, and reduce investigative workloads.

“We’ll work together in order to be able to put a bow on a case and hand it to the FBI and DOJ, such that they don’t have to expend a great amount of resources in order to go forward and try to figure things out that we already know.” —CJ Moses, Chief Information Security Officer and VP of Security Engineering at Amazon

An example of this is the disruption of the cybercriminal group known as Anonymous Sudan. The group was responsible for tens of thousands of distributed denial-of-service (DDoS) attacks against critical infrastructure, corporate networks, and government agencies. With the help of tools like MadPot, AWS experts were able to identify the hosting provider infrastructure that the group used to launch the DDos attacks, and work with providers to disrupt them. Akamai SIRT, Cloudflare, CrowdStrike, DigitalOcean, Flashpoint, Google, Microsoft, PayPal, SpyCloud, and other private sector entities also assisted law enforcement, leading to the indictment of two Anonymous Sudan leaders.

The value of combined perspectives

RSA Conference 2025 might be over, but the learning continues. Additional highlights that include the west stage keynotes, the Innovation Sandbox, and dozens of insightful sessions on topics such as the changing role of the CISO, women in cyber, and of course—cloud security—are available on demand.

If there’s one key takeaway, it’s a collective sense of transition. As we explore the benefits and risks of emerging AI technologies, encryption strategies, and information sharing, it’s important to remember that we cannot effectively combat threats in isolation. Security is a collective endeavor; only by working together can we adapt to evolving challenges and build cyber resilience.

For more information about cloud security, register to join AWS, Google Cloud, and Microsoft online at the SANS 2025 Cloud Security Exchange on August 21.

Implementing just-in-time privileged access to AWS with Microsoft Entra and AWS IAM Identity Center

2025-06-03 Rodney Underkoffler

Post Syndicated from Rodney Underkoffler original https://aws.amazon.com/blogs/security/implementing-just-in-time-privileged-access-to-aws-with-microsoft-entra-and-aws-iam-identity-center/

Controlling access to your privileged and sensitive resources is critical for all AWS customers. Preventing direct human interaction with services and systems through automation is the primary means of accomplishing this. For those infrequent times when automation is not yet possible or implemented, providing a secure method for temporary elevated access is the next best option. In a privileged access management solution, there are several elements that should be included:

User access should follow the principle of least privileged
Users should be granted only the minimum amount of access required to perform their job duties
Access granted should persist only for the time necessary to perform the assigned tasks
The solution should include:
- An eligibility process for granting access
- An approval process for granting access
- Auditing of the access grants and activities taken

Entra Privileged Identity Management (PIM) is a third-party solution that provides dynamic group management, access control, and audit capabilities that integrate with AWS IAM Identity Center.

In this post, we show you how to configure just-in-time access to AWS using Entra PIM’s integration with IAM Identity Center.

Just-in-time privileged access with Entra PIM and IAM Identity Center

Privileged Identity Management is a Microsoft Entra ID feature that enables management, control, and access monitoring of your important cloud resources. There are many different configuration options when it comes to eligibility and assignment to privileged security groups, including time-bound access with start and end dates, multi-factor authentication (MFA) enforcement, justification tracking, and so on. You can read more about those options in Microsoft’s product documentation.

Figure 1 shows the just-in-time access solution powered by Entra PIM group activation requests. In this solution, Entra PIM is integrated with IAM Identity Center to provide temporary, limited access to AWS resources based on user requests and approvals. Entra ID users can submit requests for specific access to specific AWS permissions sets, which are then automatically granted for a set duration.

Figure 1 – Entra PIM solution integrated with IAM Identity Center

Prerequisites

To try the solution described in this post, you need to have the following in place:

An AWS account with an organization instance of IAM Identity Center enabled. For more information, see What is IAM Identity Center.
An Azure account subscription with Entra ID P1 or P2 licensing.
Entra ID as an external identity provider (IdP) for IAM Identity Center as described in Configure SAML and SCIM with Microsoft Entra ID and IAM Identity Center. Follow Steps 1.1, 3.1, 3.2, and 3.3.
A sample user in Entra ID as described in the step 4.1 of Step 4: Configure and test your SCIM synchronization or use an existing user within Entra ID for testing.
Automatic provisioning enabled in Entra ID and IAM Identity Center. Follow Steps 4.2 and 4.3 in this Step 4: Configure and test your SCIM synchronization.

Step-by-step configuration

In the following steps, you create configurations to enable Entra PIM for Groups to automatically assign users to groups based on approval criteria. The groups will be Entra ID security groups that use direct assignment. Note that, at the time of this writing, dynamic groups and groups that you have synchronized from a self-managed Active Directory cannot be used with Microsoft Entra PIM. While it might be possible to also populate these groups using a third-party synchronization tool, for the purposes of this exercise, we assume that administration is occurring solely within Entra ID.

In the example scenario, the role corresponds to a specific job function within your organization. We use a group called AWS – Amazon EC2 Admin, which corresponds to a DevOps on-call site reliability engineer (SRE) lead.

Step 1: Create a group representing a specific privilege level.

Create a group in Entra ID that represents a specific privilege level that your employees can request for access to the AWS Management Console.

Sign in to the Microsoft Entra admin center with your credentials.
Select Groups and then All groups.
Choose New group.
Specify Security in the Group type dropdown list.
- In the Group name field, enter AWS - Amazon EC2 Admin.
- In the Group description field, enter Amazon EC2 administrator permissions.
- Choose Create.

Step 2: Assign access for the group in Entra ID

Now you need to assign the newly created group to your enterprise application.

Sign in to the Microsoft Entra admin center with your credentials
Select Applications and then Enterprise applications and select the IAM Identity Center application that you created.
Select Users and groups from the Manage menu group and select + Add user/group.
Select the None selected option from the Users and groups section.
Select the AWS – Amazon EC2 Admin group checkbox.
Choose Select and then choose Assign.
Select Provisioning from the Manage menu group and begin synchronizing the empty group by selecting the Start provisioning option.

When you first enable provisioning, the initial Microsoft Entra ID sync is triggered immediately. After that, subsequent syncs are triggered every 40 minutes, with the exact frequency depending on the number of users and groups in the application.

When the initial sync completes, the AWS – Amazon EC2 Admin group will be ready for configuration in IAM Identity Center.

Step 3: Create permission sets in IAM Identity Center

As you prepare to configure your permission set, let’s consider session duration from both the AWS and Entra PIM perspectives. There are two session durations on the AWS side: AWS access portal session duration and permission set session duration. The AWS access portal session duration defines the maximum length of time that a user can be signed in to the AWS access portal without reauthenticating. The default session duration is 8 hours but can be configured anywhere between 15 minutes and 7 days.

Note: Entra does not pass the SessionNotOnOrAfter attribute to IAM Identity Center as part of the SAML assertion. Meaning the duration of the AWS access portal session is controlled by the duration set in IAM Identity Center.

The session duration defined within a permission set specifies the length of time that a user can have a session for an AWS account. The default and minimum value is 1 hour (with a maximum value of 12). Entra PIM allows you to configure an activation maximum duration. The activation maximum duration is the length of time that the specified group will contain the activated user account. The activation maximum duration has a default value of 8 hours but can be configured between 30 minutes and 24 hours.

You should carefully consider the values that you provide for each of these durations. The AWS access portal will display permission sets that the user had access to at the time that they signed in for the duration of the active AWS access portal session.

When you set the permission set session duration, you need to keep in mind that active sessions are not terminated when the Entra PIM activation maximum duration has been reached. Let’s look at an example:

AWS access portal session duration: default (8 hours)
Session duration defined in the permission set: 1 hour
Entra PIM group activation maximum duration: 1 hour

You might be inclined to think that an hour after being added to the group in Entra, the user would no longer have access to AWS resources. This is not necessarily the case. A user could authenticate to the AWS access portal, wait up to 8 hours, and still successfully access AWS through the permission set link. Their session would be active for the duration of the session setting defined in the permission set, which is 1 hour in this case. In this example, we have a potential window of access of 10 hours, as shown in Figure 2 that follows.

Figure 2 – Calculating session duration

With this in mind, configure your test environment with the default setting of 8 hours for the AWS access portal and 1 hour for the permission set session duration value.

Open the IAM Identity Center console.
Under Multi-account permissions, choose Permission sets.
Choose Create permission set.
On the Select permission set type page, under Permission set type, select Custom permission set, and then choose Next.
On the Specify policies and permissions boundary page, expand AWS managed policies.
Search for and select AmazonEC2FullAccess policy, and then choose Next.
On the Specify permission set details page, enter EC2AdminAccess for the Permission set name and choose Next.
On the Review and create page, review the selections, and choose Create.

Step 4: Assign group access in your organization

At this point, you’re ready to assign the Microsoft Entra group to the corresponding permission set in IAM Identity Center. This allows users who are members of the group to be granted the appropriate access level in AWS.

In the navigation pane, under Multi-account permissions, choose AWS accounts.
On the AWS accounts page, select the check box next to one or more AWS accounts to which you want to assign access.
Choose Assign users or groups.
On the Groups tab, select AWS – Amazon EC2 Admin and choose Next
On the Assign permission sets to “<AWS-account-name>” page, select the EC2AdminAccess permission set.
Check that the correct permission set was selected and choose Next.
On the Review and submit page, verify that the correct group and permission set are selected, and choose Submit.

Step 5: Configure Entra PIM

To use this Microsoft Entra group with Entra PIM, you bring the group under the management of PIM by using the Entra admin console to activate the group. You can read more about group management with PIM in the Microsoft documentation. Begin by activating the Entra group that you created.

Sign in to the Microsoft Entra admin center with your credentials.
Select Groups and then All groups
Select the AWS – Amazon EC2 Admin group.

Figure 3 – Selecting groups for PIM enablement
Select Privileged Identity Management under the Activity menu list.
Choose Enable PIM for this group.

Figure 4 – Enable PIM for this group

Now, you will configure the PIM settings for the group. These settings define Member or Owner properties and requirements. It’s here that you can establish MFA requirements, configure notifications, conditional access, approvals, durations, and so on. The Owner role can elevate their permissions using just-in-time access to manage a group, while the Member role is limited to requesting just-in-time membership within the group. In this example, you use the Member properties to demonstrate group membership level temporary elevated access and set a 1-hour duration for the group assignment.

Sign in to the Microsoft Entra admin center with your credentials.
Select Identity Governance, Privileged Identity Management, and then Groups.
Select the AWS – Amazon EC2 Admin group.

Figure 5 – Selecting groups for PIM configuration
From the Manage menu select Settings.
Choose Member to view the default role setting details.

Figure 6 – Settings option for the Member role
Review the default settings. The activation maximum duration should be set to 1 hour and require a justification from the user.
Close the Role setting details – Member blade.

Figure 7 – Closing the Role setting details – Member blade
From the Manage menu select Assignments and choose + Add assignments.

Figure 8 – Adding eligibility assignments to the PIM enabled groups
Select Member from the Select role dropdown menu and choose No member selected. Select the test account, Rich Roe in this example, and then choose Select.

Figure 9 – Adding the test user as an eligible identity for PIM activation to the group
Choose Next and leave the default setting of 1 year of eligibility. Duration eligibility defines the period that the user can request activation for the group. Depending on your use case, you will define this as permanent or for a set period. For testing purposes, keep the default setting. Choose Assign.

Figure 10 – Completing the eligibility assignment

Test the configuration

You should now have a test configuration of Entra PIM and IAM Identity Center. Use the test account to verify just-in-time access.

Sign in to the Microsoft Entra admin center using the test account (Rich Roe in this example).
Select Identity Governance, Privileged Identity Management, and then My roles.

Figure 11 – Browsing to the My Roles section of the Entra admin center
From the Activate menu list, select Groups. Your eligible group assignments should be listed.
Choose Activate for the AWS – Amazon EC2 Admin group.

Figure 12 – Activating the just-in-time group membership
In the Activate – Member blade, enter a justification for the access request and choose Activate.

Figure 13 – Providing a justification for access

In this example, there are no approval workflow processes configured for the group, so Entra validates the eligibility requirements and adds the test account to the AWS – Amazon EC2 Admin group. If you want to dive deeper into the approval workflow process, you can read more about it on the Configure PIM for Groups settings page. Because the group is assigned to the enterprise application and configured for provisioning, the updated group membership is automatically synchronized using the SCIM protocol with the connected IAM Identity Center instance. The provisioning time can vary based on the number of PIM enabled users that are activating their memberships within a given 10-second period. In most situations, group memberships are synchronized within 2–10 minutes, but can revert to the standard 40-minute interval if activity runs up against Entra PIM throttling limits. IAM Identity Center responds to SCIM requests as they arrive from Entra ID.

To test access with the newly activated group assignment, use a separate browser or a private window.

Sign in to the My Apps portal with the test user credentials and select the IAM Identity Center app that you created for testing. If you experience an error or don’t see the expected permission set, wait a few minutes until the group membership has synchronized to IAM Identity Center and try again.

Figure 14 – Accessing IAM Identity Center through the My apps portal
Expand the associated AWS account and confirm the EC2ReadOnly permission set has been granted.
Close the AWS tab. Wait for the access to be revoked, which has been set to 60 minutes in this example.

Figure 15 – Just-in-time access to the EC2AdminAccess permission set
Sign back in to the My Apps portal and select the AWS IAM Identity Center app. Notice that the EC2ReadOnly permission set has been revoked.

Conclusion

The combination of AWS IAM Identity Center and Entra PIM provides a robust solution for managing just-in-time elevated access to AWS. By using security groups in Entra and mapping them to permission sets in IAM Identity Center, you can automate the provisioning and deprovisioning of privileged access based on defined policies and approval workflows. This approach helps to make sure the principle of least privilege is enforced, with access granted only for the duration required to complete a task. The detailed auditing capabilities of both services also provide comprehensive visibility into privileged access activities.

For AWS customers seeking a comprehensive, secure, and scalable privileged access management solution, the Entra PIM and IAM Identity Center integration is a common option that’s worth investigating to see if it’s a good fit for your use case.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

A deep dive into data protection sessions at AWS re:Inforce 2025

2025-06-02 Rahul Sahni

Post Syndicated from Rahul Sahni original https://aws.amazon.com/blogs/security/a-deep-dive-into-data-protection-sessions-at-aws-reinforce-2025/

AWS re:Inforce 2025: June 16-18 in Philadelphia, PA

A full conference pass is $1,099. Register today with the code flashsale150 to receive a limited time $150 discount, while supplies last.

At Amazon Web Services (AWS), security is our top priority. We’re excited to announce the Data Protection track at AWS re:Inforce 2025, happening June 16–18, where we’ll explore how customers use AWS to push their innovation boundaries while protecting data in the age of quantum, AI, and digital sovereignty. This year’s sessions will spotlight innovative approaches to next-generation cryptography, trusted AI, privacy-enhancing technologies, and emerging best practices for safeguarding information across the entire data lifecycle.

The Data Protection track offers insights and practical guidance for organizations of all sizes, whether you’re new to AWS or an experienced security professional. We’ve carefully curated sessions that address today’s most pressing challenges, including regulatory compliance, cross-border data transfers, and protecting data in multi-cloud environments. From hands-on workshops about implementing encryption and data classification at scale to deep-dive technical sessions on the latest AWS data protection services, you’ll find content designed to help you build and maintain a robust data protection strategy.

In this post, we highlight key sessions that feature lecture-style presentations with real-world customer use cases, along with interactive small-group sessions led by AWS experts who will guide you through practical problems and solutions. Let’s explore what you can expect at this year’s conference.

Data access and management

DAP471-R1 | Workshop | Defend against ransomware with data defense, recovery, and response
Ransomware and malware can disrupt business applications. In this expert-level workshop, you will learn how to apply AWS Backup locking mechanisms, logically air-gapped vaults, and restore testing to help strengthen your cyber recovery posture. Experience hands-on configuration of air-gapped, immutable vaults and automated recovery point testing to meet your enterprise’s objectives. Explore how these features can be combined to build a comprehensive, recovery-focused data protection strategy to withstand evolving cyber threats. You must bring your laptop to participate.

Cryptography and post-quantum

DAP472 | Workshop | Examining hybrid post-quantum TLS key exchanges
This workshop provides a practical exploration of post-quantum cryptography, comparing its performance against classical algorithms and demonstrating real-world implementation using AWS services. You will learn how to establish quantum-safe tunnels using AWS Key Management Service (AWS KMS) and AWS SDK for Java v2, implementing hybrid post-quantum TLS for secure data transfer. The session covers critical aspects including CPU and bandwidth performance metrics of post-quantum key exchange algorithms, modifications to TLS handshake protocols, and integration with AWS Transfer Family. Hands-on demonstrations will illustrate how to protect sensitive communications against both current and future quantum computing threats through hybrid classical/quantum-resistant approaches. You must bring your laptop to participate.

DAP452 | Builders’ session | Cryptographic controls with AWS CloudHSM
Gain hands-on experience implementing strong cryptographic controls using AWS CloudHSM. Learn to deploy TLS offload with Nginx, integrate Windows code signing, and create custom key stores. Explore monitoring cryptographic key usage within FIPS 140-3 level 3 hardware security modules (HSMs), using the latest high-performance hsm2m.medium HSM types. This session shows how these advancements help you strengthen your security posture, meet stringent compliance requirements, simplify operational management, and scale your cryptographic operations to support growing workloads—all while maintaining the performance your applications demand. You must bring your laptop to participate.

Data migration and modernization

DAP302 | Breakout session | Fannie Mae’s practical path to modern PKI and certificate management
Explore Fannie Mae’s transformation of their public key infrastructure (PKI) from a legacy system to a cloud-native solution on AWS. This session details their phased migration strategy, addressing challenges such as decentralized trust store updates and securing buy-in from application teams. Learn how Fannie Mae overcame migration hurdles, including legacy dependencies and compliance requirements, to achieve 100 percent adoption while maintaining security and reducing certificate-related overhead. Gain insights into cost optimization, risk mitigation, and architectural best practices for enterprise-scale certificate management in the cloud. This presentation offers actionable strategies for organizations undertaking similar PKI modernization efforts. Finally, we share the latest in enterprise-scale certificate management in the cloud.

DAP322 | Lightning talk | How Monzo Bank protects critical workloads using AWS Nitro Enclaves
Monzo Bank deploys security-critical applications requiring a high level of assurance around code integrity, system hardening, and limited attack surface. They achieved this using reproducible builds and the cryptographic attestation and isolated compute environment provided by AWS Nitro Enclaves. In this talk, we describe the challenges they overcame in building and deploying production workloads using this approach and share what they learned along the way.

Data protection for AI

DAP201 | Breakout session | Veradigm’s security-first approach to amplifying potential with GenAI
How can organizations empower teams with generative AI capabilities while maintaining rigorous data security standards responsibly? Veradigm initially hesitated to adopt generative AI because of data privacy, security, and regulatory compliance concerns. Join Veradigm’s principal developer for internal AI solutions to discover how they implemented practical security measures to build and deploy a compliant generative AI assistant using Amazon Bedrock that enhanced their team capabilities while strengthening their security posture. Learn about essential security controls, architectural decisions, and valuable lessons learned from successfully implementing AI for employees operating in a highly regulated environment.

DAP332 | Chalk talk | Executive perspective: Risk management for generative AI workloads
Don’t let the perceived complexity of responsible AI keep you from deploying generative AI applications on AWS. In this chalk talk, we present a framework for breaking down AI safety and security risks, introduce AWS best practices for keeping enterprise data secure in generative AI applications using zero trust principles, and mitigate safety risks using technologies such as Amazon Bedrock Guardrails. Discover as a group with fellow security leaders how to identify safety and security risks relevant to your workload, implement appropriate mitigation strategies, and measure efficacy over time.

DAP371 | Workshop | Defend your AI: Mitigate prompt injection with Amazon Bedrock
Master the art of identifying and mitigating prompt injection vulnerabilities in generative AI systems through this hands-on workshop. Using Amazon Bedrock, you will explore both offensive and defensive prompt engineering techniques to understand the security implications of large language models in production environments. In this session, you learn how prompt injection attacks work, complete an interactive capture the flag style challenge attempting to exploit a simulated AI environment, and learn how to implement defensive controls using Amazon Bedrock Guardrails. You must bring your laptop to participate.

Data protection and compliance at scale

DAP331-R | Chalk talk | Architecting a secrets management strategy that scales
Dive deep into architectural patterns for enterprise secrets management in cloud-native environments. In this session, we dissect the implementation complexities of centralized versus decentralized secrets management and discuss the trade-offs between these patterns, including their impact on developer velocity, security, and operational overhead. You will learn how to use AWS services to implement a flexible secrets management strategy and manage secrets lifecycle that balances the needs of developers and security teams. We also cover best practices for centralized compliance and auditing regardless of your chosen architecture.

DAP202 | Breakout session | Navigating sovereignty requirements: Architectures and solutions on AWS
Evolving data protection regulations and digital sovereignty requirements mean that organizations are facing increasingly complex compliance requirements when using cloud capabilities. This breakout session explores practical architectural approaches for meeting sovereignty requirements on AWS, with a focus on European and global regulatory frameworks. We examine key architectural patterns that enable data residency control, operational transparency, and sovereign workload isolation. The session covers the AWS Sovereignty Pledge, including sovereign design best practices, as well as the upcoming AWS European Sovereign Cloud.

Advanced seat reservation

If you’re a registered attendee, you can secure your spot in sessions through reserved seating. To reserve your seat, sign in to the attendee portal, go to Event, and then select Sessions. Act quickly to make sure you get a place in your preferred sessions.

Conclusion

Whether you’re a security architect seeking to modernize your defenses or a security executive aiming to elevate your organization’s security posture to drive faster business growth, re:Inforce is your essential destination. With a roster of carefully vetted and certified AWS speakers, you can be confident that every moment at the conference will provide valuable insights and actionable strategies. Join us at re:Inforce to empower your team, protect your assets, and propel your business forward in the digital age.

If you have feedback about this post, submit comments in the Comments section below.

Application security at re:Inforce 2025

2025-05-29 Daniel Begimher

Post Syndicated from Daniel Begimher original https://aws.amazon.com/blogs/security/application-security-at-reinforce-2025/

AWS re:Inforce 2025: June 16-18 in Philadelphia, PA

A full conference pass is $1,099. Register today with the code flashsale150 to receive a limited time $150 discount, while supplies last.

Join us in Philadelphia on June 16–18, 2025, for AWS re:Inforce, where you can enhance your skills and confidence in cloud security, compliance, identity, and privacy. As an attendee, you will have access to hundreds of technical and non-technical sessions, an Expo featuring Amazon Web Services (AWS) experts and AWS Security Competency Partners, and keynote sessions led by industry leaders. AWS re:Inforce offers a comprehensive focus on key security areas, including application security (AppSec).

Key AppSec themes for 2025

The AppSec track helps you understand and implement best practices for securing your applications throughout the development lifecycle. For 2025, we’re focusing on several key themes:

Organizational strategies to ship quickly and securely

Learn about security ownership, partnerships like DevSecOps, comprehensive application security programs, and scaling application security expertise into workload teams. These sessions explore how organizations can build security into their development processes without sacrificing speed, focusing on practical approaches that distribute security responsibility effectively.

Secure by design

Make embedding security principles into the early stages of software architecture and design to mitigate vulnerabilities early, minimize risks, and recognize security as a core business requirement. Learn how leading organizations implement security as a foundational element rather than an add-on consideration.

Security of the pipeline

Security of the pipeline includes tooling, reference architectures, and best practices for securing the pipeline, including Supply chain Levels for Software Artifacts (SLSA), Supply Chain Integrity, Transparency, and Trust (SCITT), and code signing. Discover how to protect the systems and processes that build and deploy your applications.

Security in the pipeline

Security in the pipeline is achieved in part through testing methodologies including static analysis, dynamic analysis, responsible AI testing, software composition analysis, formal methods (automated reasoning), and dependency tracking. These sessions demonstrate how to integrate comprehensive security testing throughout your development lifecycle.

In the following sections, you’ll find a subset of some of the most exciting sessions happening in our AppSec track this year. For the full list, visit the re:inforce 2025 catalog.

Breakout sessions, chalk talks, lightning talks, and code talks

APS204 | Breakout session | Scaling security with Sportsbet’s Security Guardians program
The Security Guardians program helps scale security across application teams by building and embedding security expertise. We dive deep on Sportsbet’s program where you will learn how to get started, key phases to consider, and the first learning steps for new guardians. Discover lessons learned, common challenges, and how to refine the program for long-term success. By integrating security into application teams early, Sportsbet fosters a culture of shared responsibility, improving security posture without slowing down development. We provide practical insights on launching and evolving a Security Guardians program to drive real impact across your organization.

APS301 | Breakout session | Improve code quality with Amazon Q Developer
Amazon Q Developer is a generative AI assistant that goes beyond writing code—it can also improve documentation, generate unit tests, and automate code reviews. In this session, discover how to integrate Amazon Q Developer into your software development lifecycle to detect security issues using software composition analysis (SCA), static application security testing (SAST), and other code quality checks. Learn how to improve your codebase quality using the capabilities of Amazon Q Developer within the integrated development environment (IDE) and DevSecOps tooling.

APS401 | Breakout session | Build verifiable apps using automated reasoning and generative AI
Large language models (LLMs) excel at generating creative solutions, while automated reasoning tools enable rigorous verification. This session explores methodologies for combining these complementary strengths to create more reliable AI systems. In this session, we introduce automated reasoning and demonstrate how formal methods can guide and constrain generative AI. By combining probabilistic and symbolic approaches, we show you how to build hybrid systems that maintain creative capabilities while ensuring verifiable outputs. We demonstrate how Amazon Q Developer and Amazon Bedrock Guardrails use automated reasoning to generate safe and logically correct output, free from hallucinations.

APS431 | Chalk talk | DevSecOps in action with Visual Studio Code & AWS IAM Access Analyzer
Organizations face a critical balance between developer productivity and security compliance when managing AWS Identity and Access Management (IAM) policies. In this session, discover how integrating AWS IAM Access Analyzer with Visual Studio Code empowers developers to create secure IAM policies during development. Learn to implement automated policy checks that catch overly permissive permissions early, validate against organizational standards, and provide real-time feedback. This proactive approach helps security teams maintain control while giving developers the autonomy they need, ultimately reducing deployment risks and saving valuable development time.

APS341 | Code talk | Move fast and stay secure: Lessons learned from the AWS prototyping team
When building prototypes and applications with technologies such as generative AI and serverless, it’s critical to move quickly and securely. In this code talk, learn how the AWS prototyping team successfully balances these goals. To meet user demand, AWS builds prototypes over a short amount of time while meeting a high bar for security expectations. Learn pointers, tips, and tricks to build quickly and securely, from threat modeling to using AWS Cloud Development Kit (AWS CDK) features, custom constructs, and blueprints to harden the security of your infrastructure and improve productivity.

APS441 | Code talk | Supercharge IaC security with AI: From commit to auto-remediation
Dive deep into building an automated security feedback loop that combines Git commit signatures, static analysis, and generative AI to revolutionize infrastructure as code (IaC) security. Through live coding, we’ll demonstrate how to use Amazon Q Developer and Amazon Bedrock to analyze IaC templates, automatically detect and resolve issues, and generate contextual fix recommendations. Learn how to implement commit-based tracking for security findings, automate issue creation, and integrate with continuous integration and delivery CI/CD pipelines. Watch as we build a complete system that reduces the time from vulnerability detection to remediation from days to minutes.

APS442 | Code talk | Create memory safe applications using open source verification tools
Memory-safety errors pose a significant security risk, enabling various attack vectors. At AWS, we prioritize memory-safety for unmanaged code handling customer data and processes. This talk presents two efforts to reduce memory-safety errors in Rust and C code. Both efforts involve developing verification tools for Rust and C code to check memory safety at scale that you can use. Our first effort verifies the Rust standard library, a core software resource, used by millions of developers. Our second effort uses a C model checker to verify C code for safety and correctness.

APS221 | Lightning talk | Building secure development into Amazon stores
Amazon.com has long been at the forefront of investing in robust security measures to protect customer data. As the digital landscape evolves, so do our strategies. This session explores our journey of continuous improvement in security practices, focusing on integration throughout the software development lifecycle using AWS services. We’ll share the cutting-edge methods used by Amazon.com for embedding security at every development stage and discuss successes and learnings. Join us to discover how we’ve adapted our tactics to meet changing developer and customer needs and to ensure our commitment to protecting customer data remains stronger than ever.

APS222 | Lightning talk | Transform threat modeling using generative AI
Discover how CRED, one of the biggest Fintech companies in India has used generative AI to automate threat modeling across their applications. Learn architectural patterns that enabled CRED to scale security analysis, improve risk identification, and enhance decision-making. See practical examples of integrating AI into security modeling workflows using Amazon Bedrock.

SEC221 | Lightning talk | Raising the tide: How AWS is shaping the future of secure AI for all
AI security is a top priority for AWS. By building AI solutions that are secure by design, AWS helps you innovate quickly with confidence while mitigating emerging threats. But securing AI goes beyond individual organizations—it requires industry-wide standards and best practices. AWS actively contributes to global AI security efforts, including participation in industry standards bodies such as The Coalition for Secure AI (CoSAI), to help ensure that AI technologies are safe, resilient, and trustworthy. This session will explore how AWS is leading AI security innovation, protecting customers, and collaborating to help shape the future of AI security for the entire industry.

Workshops and builders sessions

APS351 | Securing generative AI agents using AWS Well-Architected Framework
Learn hands-on how to build secure generative AI agent solutions following the AWS Well-Architected Framework Generative AI Lens security best practices. Work through practical implementations of endpoint security, prompt engineering guardrails, monitoring systems, and protection against excessive agency while building a production-ready generative AI agent. Through hands-on exercises, build a secure generative AI agent solution incorporating these controls on AWS, using Amazon Bedrock, Amazon CloudWatch, IAM, and more. You must bring your laptop to participate.

APS353 | Red-teaming your LLM security at scale
Step into the shoes of an AI-powered red team adversary in the GenAI Red Team Challenge. In this intensive workshop, you’ll deploy an AI security agent to orchestrate sophisticated attack chains against generative AI applications, systematically discovering and exploiting vulnerabilities from prompt injection to boundary testing while mastering automated security testing workflows. In addition, you’ll learn how to apply countermeasures, from prompt templating to guardrails. This hands-on, gamified experience helps you think like a threat actor and equips you with practical skills in automated vulnerability testing and risk mitigation against common MITRE and OWASP vulnerabilities for LLM-based applications. You must bring your laptop to participate.

APS354 | Secure your application using AWS services and open source tooling
AWS, open source, and partner tooling work together to accelerate your software development lifecycle. Learn how to use the Automated Security Helper (ASH), an open source application security tool, to quickly integrate various security testing tools into your software build and deployment flows. AWS experts guide you through the process of security testing locally on your machines and within a simulated pipeline using a sample generative AI application. Discover how to identify potential security issues in your applications through static analysis, software composition analysis, and infrastructure-as-code testing, and use Amazon Q Developer to review the results and generate remediation. You must bring your laptop to participate.

APS271 | Threat modeling for builders
In this workshop, you will learn threat modeling core concepts and how to apply them through a series of group exercises. Key topics include threat modeling personas, key phases, data flow diagrams, STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege), and risk response strategies. We introduce a threat grammar rule and an associated tool. Exercises will have you identify threats and mitigations through the lens of each of the threat modeling personas. You will assemble in groups and walk through a case study. AWS threat modeling subject matter experts will be on hand to guide you and provide feedback. You must bring your laptop to participate.

APS371 | Securing your generative AI applications on AWS
In this workshop, discover how to secure generative AI applications using AWS services and features. Explore how to deploy a vulnerable sample generative AI application and then layer security controls to protect, detect, and respond to security issues. Learn how to apply similar controls to the generative AI applications in your organization. You must bring your laptop to participate.

APS471 | Boost developer productivity with Amazon Q Developer and Amazon Bedrock
Accelerate development and drive innovation with Amazon Q Developer and Amazon Bedrock. Discover how AI-powered automation and intelligent code assistance can reduce friction, speed up development cycles, and improve code quality. Explore real-world use cases such as AI-driven code reviews, automated testing, and smart documentation generation. Learn how to seamlessly integrate these tools into your workflows to boost efficiency, enhance collaboration, and elevate the developer experience—all while making sure of security and compliance. Whether optimizing existing processes or adopting AI for the first time, this session provides actionable insights to supercharge your development teams. You must bring your laptop to participate.

Conclusion

This post showcases a subset of the exciting AppSec sessions available at the upcoming AWS re:Inforce 2025 conference. If you’re interested in these topics, we encourage you to register for re:Inforce 2025, where you can attend these sessions and many more across the other security domain tracks. To discover the full range of sessions across all security tracks, check out the complete AWS re:Inforce catalog.

If you have feedback about this post, submit comments in the Comments section below.

Introducing new regional implementations of Landing Zone Accelerator on AWS to support digital sovereignty

2025-05-28 Max Peterson

Post Syndicated from Max Peterson original https://aws.amazon.com/blogs/security/introducing-new-regional-implementations-of-landing-zone-accelerator-on-aws-to-support-digital-sovereignty/

Customers often tell me that they want a simpler path to meet the compliance and industry regulatory mandates they have in their geographic regions. In our deep engagements with partners and customers, we have learned that one of the greatest challenges for customers is the translation of security and compliance requirements into distinct technical controls. At Amazon Web Services (AWS), security is our top priority, and we understand that protecting your data in a world with changing regulations, technology, and risks takes teamwork. As we’ve said, security is foundational to sovereignty.

AWS helps organizations to develop and evolve security, identity, and compliance into key business enablers; that’s why we’re committed to working with national cyber authorities and regulators to help define and establish how their compliance standards can be translated into security best practices in the cloud. We’re responding to customer requests to create locally tailored approaches aligned to their own regional standards and guidance as established by in-region authorities.

Architectural best practice, locally tailored

Since its launch in 2022, Landing Zone Accelerator on AWS has been instrumental in helping thousands of customers deploy cloud foundations that align with multiple global compliance frameworks and AWS best practices, including the Baseline Informatiebeveiliging Overheid (BIO) in the Netherlands, and the Esquema Nacional de Seguridad (ENS) in Spain. AWS is committed to expanding our regional implementations to help customers meet specific national and regional standards and digital sovereignty goals.

In March, I was proud to share the news of the cooperation agreement between the Federal Office for Information Security (BSI) and AWS, where AWS committed to help advance digital sovereignty and cybersecurity best practices and standards in Germany and across the European Union. With that in mind, I’m excited to share that our next regional implementation of Landing Zone Accelerator on AWS will support customers with workloads in Germany. The C5-ready Landing Zone Accelerator is designed to help customers meet their Cloud Computing Compliance Criteria Catalogue (C5) compliance objectives in the cloud. This will be available to our customers in Q3-2025, and at launch, our regional implementations will also be available in AWS European Sovereign Cloud.

The C5 attestation scheme is backed by the German government and was introduced by the BSI in 2016. AWS has adhered to the C5 requirements since their inception. C5 helps organizations demonstrate operational security against common cybersecurity threats when using cloud services through the German government’s Security Recommendations for Cloud Computing Providers.

For many customers in Germany, adherence to C5 is a requirement, and this is evidenced through a compliance assessment by an authorized assessor. Preparing for this assessment is critical for a successful outcome and is why AWS has partnered with AWS Global Security & Compliance (GSCA) Partner Schellman to provide the assessor insight as to how the C5-ready Landing Zone Accelerator can accelerate and simplify the path to C5 adoption for AWS customers.

AWS Partner Schellman: Proven Track Record in C5 Assessments

As one of the few firms with deep expertise and experience in C5 assessments, Schellman has completed several dozen evaluations across a wide range of clients—from agile startups to global enterprises. This diverse portfolio underscores Schellman’s capabilities, deep technical expertise, and unwavering commitment to security assurance.

“Our team has seen firsthand how the C5 standard fosters transparency and builds trust in cloud services. We’re proud to support our clients not just in understanding C5, but in strategically leveraging it to improve security and competitiveness on a global scale.”
Jeff Schiess, Managing Director, Schellman

Lowering the Barrier to Entry – Schellman recognizes that achieving C5 compliance can sometimes be intimidating, particularly for organizations new to the framework. To that end, Schellman has performed an assessment against the foundational infrastructure provided by LZA on AWS, designed to simplify the C5 journey. The LZA provides preconfigured infrastructure templates and security baselines that significantly reduce the complexity of establishing C5-compliant cloud environments.

“With the Landing Zone Accelerator, organizations can build on a C5-ready foundation right from the start. It’s a practical, scalable solution for companies that might otherwise find the C5 standard overwhelming.”
Kristen Wilbur, Principal, Schellman

Sovereign by design

Landing Zone Accelerator on AWS automatically implements hundreds of security capabilities that map to control requirements across geographic compliance frameworks. This saves customers hundreds of hours in planning and implementing secure networking and account configurations by providing them with a foundation based on the AWS Well-Architected Security Pillar and AWS security best practices. Meeting compliance requirements, having verifiable access controls and data transfer restrictions, independence and choice over the technology stack, and surviving large-scale disruptions are some of the key capabilities that customers require of a sovereign-by-design workload. However, for many customers, translating regulatory requirements into a set of discrete technical controls and applying them consistently across one or more AWS accounts and AWS Regions can be time-intensive and challenging.

We provide customers and partners with detailed guidance on how to configure Landing Zone Accelerator on AWS in accordance with their local security and compliance requirements, including digital sovereignty requirements. This includes control mapping to local regulations or policies that shows customers how controls implemented in a landing zone are mapped to the specific requirements, calling out where customers are required to do more to meet these as part of our shared responsibility model—this includes organizational policies and procedures where customers must implement additional controls within their application or workload to meet local requirements.

Control over the location of your data

Landing Zone Accelerator on AWS provides customers with a choice of configurable preventative, detective, and proactive controls to help customers meet their data residency, security, and compliance objectives, whether you’re a public sector customer wanting to keep data in a single Region or navigating the complex needs of multi-national organizations with operations subject to differing digital sovereignty requirements.

Verifiable control over data access

Landing Zone Accelerator on AWS goes beyond just provisioning a secure, multi-account environment. It establishes a well-structured, multi-account architecture using AWS Organizations. This logically isolates workloads, management functions, and security controls into dedicated organizational units (OUs). This not only enhances security and operational efficiency, but also helps customers to enforce consistent data residency, access management, and compliance policies across their entire cloud footprint. These powerful guardrails empower customers to quickly harness the innovative potential of cloud technologies, whilst delivering business value from an established security and compliance baseline.

By providing this automated approach, AWS empowers organizations to rapidly deploy cloud environments tailored to their specific local requirements in days instead of weeks; with robust security, compliance, and operational guardrails in place from the outset. Landing Zone Accelerator on AWS is designed to simplify the path to cloud adoption and compliance for organizations, particularly those in regulated industries or with sovereignty requirements. This approach marks a shift from the previous heavy lift required for organizations to migrate workloads to the cloud while meeting their needs.

Partners at the core

There is a lot of complexity involved with navigating the evolving digital sovereignty landscape—but you don’t have to do it alone. Our AWS Digital Sovereignty Competency connects customers with trusted partners with demonstrated expertise to advise and architect for their customers’ digital sovereignty needs while taking advantage of the full potential of the AWS Cloud. As part of the competency, AWS is supporting partners to navigate customer challenges across four pillars: data residency, data protection, access control, and survivability.

Customers have told me about how challenging it can be to architect to address their sovereignty needs, often requiring manual iteration and longer time to value. Using Landing Zone Accelerator on AWS is one of the ways AWS and AWS Partners can work together to address customers’ sovereignty needs with a repeatable approach that helps our customers and partners move faster. I’m excited by how regional implementations of Landing Zone Accelerator on AWS is helping AWS Sovereignty Partners, such as Atos and SVA, to move faster without compromise.

“Compliance with regulations like C5 is essential for customers in the public sector and regulated industries, who prioritize digital sovereignty, and this is central to our Cloud for Clinics initiative with AWS in the German Healthcare market. The availability of the C5 LZA significantly reduces the technical complexity, giving us a common technical platform to build on reducing time to market. Atos is driving the operational rollout and expanding the scope of compliance mappings to further streamline customer compliance. At the same time, we are incorporating essential managed services like SOC/SIEM which we believe will make compliant cloud adoption easier to drive innovation by the Public Sector, Healthcare institutions or customers in regulated industries like Financial Services and Utilities.”
Boris Hecker, Managing Director, ATOS Germany

“Compliance with BSI C5 criteria for customers from the public sector and regulated industries is a basic requirement for the use of public cloud services. Implementing the regulations is often complex, time-consuming and resource-intensive. For this reason, customers are looking for solutions that they can tailor to the specific requirements of their industry; while ensuring they meet compliance standards. SVA supports customers in maintaining the balance between innovation and compliance with customized, C5-certified, managed services. We rely on solutions such as the Landing Zone Accelerator on AWS to reconcile the use of market-leading public cloud infrastructure with regulatory requirements.”
Patrick Glawe, Hyperscaler Lead at SVA

For more information, see Landing Zone Accelerator on AWS and AWS Digital Sovereignty Competency Partners

How to use the new AWS Secrets Manager Cost Allocation Tags feature

2025-05-27 Jirka Fajfr

Post Syndicated from Jirka Fajfr original https://aws.amazon.com/blogs/security/how-to-use-the-new-aws-secrets-manager-cost-allocation-tags-feature/

AWS Secrets Manager is a service that you can use to manage, retrieve, and rotate database credentials, application credentials, API keys, and other secrets throughout their lifecycles. You can use Secrets Manager to replace hard-coded credentials in application source code with a runtime call to the Secrets Manager service to retrieve credentials dynamically when you need them. Storing the credentials in Secrets Manager helps avoid unintended access by anyone who inspects your application’s source code, configuration, or components.

Until today, your AWS bill would reflect the total cost of Secrets Manager in any given account, and you had no option to break out the cost per secret to a given cost center or organization.

In this post, we introduce a new feature—Secrets Manager Costs Allocation Tags—and walk through how you can use them for improved visibility into your Secrets Manager costs. Before getting into the details of this new feature, we want to give you primer about cost allocation tags.

A tag is a key-value pair that you assign to an AWS resource. In AWS Cost Explorer, you can activate tags as cost allocation tags. With tags activated, you can categorize and track costs by cost allocation tags. For example, you can create a tag named Group with value Engineering and assign it to resources owned by the engineering team of your company. After activating the Group tag as a cost allocation tag, you can track charges with this tag, filter or group by tags in Cost Explorer, and add tags to reports such as cost and usage reports for further analysis and visualization.

Cost allocation in AWS is a four step process:

Create the required cost allocation tags
Attach cost allocation tags to your resources
Activate your tags in the Cost Allocation Tags section of the AWS Billing console
Filter the tags, group by tags in Cost Explorer, and create cost categories

After you create and attach the tags to resources, they appear in the AWS Billing console Cost Allocation Tags section under User-defined cost allocation tags within 24 hours. You must activate these tags for AWS to start tracking them for your resources and for the tags to show up in Cost Explorer. When the tags appear under Tags in the Filter or Group by fields in Cost Explorer, you can start filtering or grouping by tag to view usage and charges by tag.

AWS Secrets Manager now supports cost allocation tags

Secrets Manager now supports cost allocation tags, giving you more granular control and visibility into your Secrets Manager costs. You can use this feature to categorize and track your Secrets Manager usage charges at a more detailed level, helping you to better understand and manage your AWS spending and assign costs per secret back to cost centers or organizations.

Solution overview: Enhanced cost visibility and management

With this new capability, you can:

Break down Secrets Manager costs by department, project, environment, or other dimensions important to your organization
View itemized Secrets Manager usage in Cost Explorer as well as in cost and usage reports
Improve cost allocation and chargeback processes across your business units and organizations

Prerequisites

To walk through the examples in this post, you need to have the following:

An AWS account
Access to the AWS Management Console or the AWS Command Line Interface (AWS CLI) version 2
An existing tagging and cost allocation strategy
Existing secrets inside Secrets Manager

Create user-defined tags for cost allocation purposes using the console

In this example, we assume that you want to manage the cost of your secrets by different cost centers in your organization. Here, we create a tag with CostCenter as a key and the value equal to the cost center codes of the teams that are using secrets.

You’ll walk through two examples, the first one with a cost center for Engineering and a second one with a cost center for Finance. You will reuse those examples throughout this post.

In this example, start by creating and assigning a tag called cost allocation center with the key name: CostCenter and assign a cost center value of 7263 for the engineering department to an existing or new secret.

To assign a user-defined tag to a new or existing secret:

In the Secrets Manager console, choose Secrets from the navigation pane.
In the list of available secrets, select the secret to edit the tags or choose Store A New Secret to create a new secret.
When the secret is displayed, select the Tags option and choose Edit Tags to add new or edit existing tags.

Figure 1: Assign a user-defined tag to an existing secret
Repeat the process, but assign the cost center value of 7263 for the engineering department and 1121 for the finance department to a second secret.

Create user-defined tags for cost allocation purposes using the AWS CLI

Optionally, you can use the AWS CLI to create the same tags as in the preceding example.

To use the AWS CLI to create tags:

Use the following AWS CLI command to create the first tag:

aws secretsmanager tag-resource \
    --secret-id prod/mastersecret \
    --tags Key=Role,Value=admin

Create the second tag using the following AWS CLI command:
```
aws secretsmanager tag-resource \
    --secret-id prod/mastersecret \
    --tags Key=CostCenter,Value=7263
```
This command produces no output in case of a successful execution.
Use the second AWS CLI command with a value of 1121 for the second secret.

Turn tags into cost allocation tags using the AWS Billing and Cost Management console

The next step is to activate the user-defined tags within the AWS Billing and Cost Management console so they can be used.

To activate cost allocation tags:

Go to the AWS Billing and Cost Management console and choose Cost allocation tags in the navigation pane.
Select the option for user-defined cost allocation tags.
Select the tag keys that you want to activate.
Choose Activate.

Note: After you create and apply user-defined tags to your resources, it can take up to 24 hours for the tag keys to appear on your cost allocation tags page for activation. It can then take up to 24 hours for tag keys to activate.

Figure 2: Activate cost allocation tags

Turn tags into cost allocation tags using the AWS CLI

You can also activate user-defined tags within the AWS Billing and Cost Management Console using the AWS CLI.

To activate tags using the AWS CLI:

For activation of the first user-defined tag use the following AWS CLI command:

aws ce update-cost-allocation-tags-status \
    --cost-allocation-tags-status TagKey=Role,Status=Active

To activate the second user-defined tag use the following AWS CLI command:

aws ce update-cost-allocation-tags-status \
    --cost-allocation-tags-status TagKey=CostCenter,Status=Active

View the results in Cost Explorer

The last step is to view the results for secrets in Cost Explorer. When the tag CostCenter shows up under Tags in the Filter or Group By fields in Cost Explorer, you can start filtering or grouping by the tag to view usage and charges by tag.

When applying the tag filter for Secrets Manager, Cost Explorer displays charges only for resources tagged with the selected tag values. And when grouped by a particular tag, the charges are grouped by each value of the selected tag.

To view results:

As an example, use the following parameters to view results.

In the Cost Center console, select the right arrow (>) icon to open the report parameters options to the right of the billing dashboard.
On the Report parameters window:
1. For Date Range, enter the desired time range.
2. Under Group by, for Dimension, select Tag, and for Tag select Cost Center.
3. For Filters, Service, select Secrets Manager.
  
  Figure 3: Configure report parameters

You can use the resulting report to clearly identify the cost and usage of the two secrets, broken down into the two cost centers: engineering 7263 and finance 1121. Now, you can cross-charge secrets to the corresponding cost centers in your organization and provide a report similar to Figure 4.

Figure 4: Cost and usage report

Conclusion

In this post, we introduced the AWS Secrets Manager Cost Allocation Tags feature and showed you how to use AWS Cost Explorer Costs and Usage Reports to gain secrets usage insights. You can now allocate the cost for every secret to one or more cost centers and charge them accordingly. See the AWS Secrets Manager Cost Allocation Tag documentation to learn more about how you can use Secrets Manager Cost Allocation Tags in your accounts.

Go to the AWS Secrets Manager console to get started. For more information, see AWS Secrets Manager.

To learn more about how to build an effective tagging strategy for cost allocation and financial management, see the Tags for cost allocation and financial management whitepaper.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Elevate your AI security: Must-see re:Inforce 2025 sessions

2025-05-27 Margaret Jonson

Post Syndicated from Margaret Jonson original https://aws.amazon.com/blogs/security/reinforce-2025-genai-sessions/

AWS re:Inforce 2025: June 16-18 in Philadelphia, PA

A full conference pass is $1,099. Register today with the code flashsale150 to receive a limited time $150 discount, while supplies last.

From proof of concepts to large scale production deployments, the rapid advancement of generative AI has ushered in unique opportunities for innovation, but it also introduces a new set of security challenges (and opportunities) that organizations must address. How do you protect retrieval-augmented generation (RAG) or training data while maintaining model effectiveness? What controls are needed for large language model (LLM) interactions? How can I take full advantage of AI agents and model context protocol (MCP) while minimizing risk? At AWS re:Inforce 2025, we’re bringing together security experts, practitioners, and industry leaders to answer these questions with real-world, prescriptive guidance and more.

This year, our generative AI security sessions have been specifically curated and designed to help you build and maintain secure, production AI systems at scale. Whether you’re just beginning your AI security journey or leading mature, enterprise-wide AI initiatives, you’ll find deep practical guidance, hands-on experience, and strategic insights to advance your organization’s security posture.

From foundational concepts to advanced defensive techniques, these sessions encompass critical areas including data protection, model security, identity management, and AI agent resilience. You’ll learn directly from AWS security experts, customers who have successfully implemented secure AI systems, and industry leading partners who are setting new standards in AI safety and security.

In this blog, we highlight some “can’t miss” sessions that cover how to secure AI, but also how security practitioners can leverage AI to help with their critical security missions as well! Join in on the fun, and register for re:Inforce 2025!

Innovation talk

Engage with top AWS executives in our Innovation Talks series, where you’ll gain invaluable insights into the forefront of cloud technology. Explore the latest advancements in generative AI, discover robust cloud security strategies, and uncover pioneering architectural concepts that are revolutionizing application development and expanding the possibilities of the AWS Cloud.

SEC301 | Innovation Talk | From possibility to production: A strong, flexible foundation for AI security
Speakers: Hart Rossman (AWS) & Becky Weiss (AWS)
Discover how AWS removes the heavy lifting of AI security, enabling you to accelerate from development to production. This session reveals how the proven AWS security foundation, combined with flexible controls and automated reasoning, helps organizations confidently deploy AI innovations. Through real-world examples, learn how to transform security from a potential roadblock into an innovation enabler. Leave with practical guidance for securing AI workloads today and strategic insights into addressing emerging security challenges, including data security and agentic AI. Learn how the AWS approach to AI security helps you start ahead while maintaining strong security controls.

Breakout sessions, chalk talks, and lightning talks

Breakout sessions are lecture-style, one-hour sessions delivered by AWS experts, customers, and partners—perfect for deepening your knowledge on important topics, gaining actionable insights, and connecting with industry leaders.

Chalk talks are one-hour long, highly interactive sessions with a small audience. This format is ideal for diving deep into specific topics, engaging directly with AWS experts, and getting your questions answered in real time.

Lightning talks are short (20 minute) theater presentations dedicated to a specific customer story, service demo, or AWS Partner offering.

SEC303 | Breakout session | Behind the shields: AWS and Anthropic’s approach to secure AI
Speakers: Matt Saner (AWS) & Shahzeb Jiwani (Anthropic)
Enterprise AI adoption demands robust security. In this session, Join Anthropic’s head of risk governance along with AWS security leaders to reveal how AWS and Anthropic collaborate to deliver enterprise-grade security for LLMs and the generative AI workloads they enable. Learn about the multi-layered security approach spanning infrastructure, data, and models. We’ll explore real-world security architectures, governance frameworks, and risk mitigation strategies. You will leave with a deeper understanding of how to leverage AWS and Anthropic’s security capabilities to accelerate your organization’s AI initiatives while maintaining stringent security and compliance requirements.

SEC304 | Breakout session | Amazon.com testing frameworks and tools for GenAI security and privacy
Speakers: Alex Torres (AWS), Josh Haycraft (Amazon), & Jess Clark (Amazon)
GenAI solutions are launching in a unique, rapidly-shifting security landscape: they may be trained on customer data, they may integrate with internal services or datastores, and they will provide generated content to customers or to other systems. Learn how Amazon.com creates toolkits, systems and frameworks to leverage Large Language Models and Generative AI to enrich customer interactions to promote agility and innovation.

TDR301 | Breakout session | Innovations in AWS detection and response for integrated security outcomes
Speakers: Himanshu Verma (AWS) & Ryan Holland (AWS)
Discover how the latest AWS detection and response capabilities can help secure your cloud environment more effectively. Learn practical ways to achieve integrated security outcomes through enhanced threat detection, automated vulnerability management, and streamlined response – all at scale. We’ll show you how to use AWS security services to protect workloads and data, centralize security monitoring, manage security posture continuously, and unify security data, while leveraging generative AI for security operations. Walk away with actionable insights on integrating AWS detection and response services to strengthen and simplify your security across AWS.

SEC431 | Chalk talk | Dive deep into data protection architectures for Amazon Bedrock Agents
Speakers: Andrew Kane (AWS) & Gabrielle Dompreh (AWS)
Join this chalk talk to understand how Amazon Bedrock protects your data across Agents and related features, such as Knowledge Bases and Guardrails. Learn about security considerations for cross-region deployments, multi-agent collaboration, and prompt caching. Gain deep insights into architecting secure generative AI solutions that maintain data protection, and discover architectural patterns that keep your applications safe and secure.

APS231 | Chalk talk | Using AWS services to mitigate the OWASP Top 10 for LLM threats
Speakers: Mark Keating (AWS) & Cameron Smith (AWS)
You’ve identified your generative AI use case, tested it and are creating a secure application architecture design. How do you know what generative AI specific threats you should be protecting against, and what tools or services are available that can help? You may have heard of the OWASP Top 10 for LLM Applications, but where or how do you start? Join us as we discuss the OWASP Top 10 threats, the differences between versions, and how AWS can help you mitigate these threats.

DAP332 | Chalk talk | Executive perspective: Risk management for generative AI workloads
Speakers: Jason Garman (AWS) & Mark Ryland (AWS)
Don’t let the perceived complexity of responsible AI keep you from deploying generative AI applications on AWS. In this chalk talk, we will present a framework for breaking down AI safety and security risks, introduce AWS best practices for keeping enterprise data secure in generative AI applications using zero trust principles, and mitigate safety risks using technologies such as Bedrock Guardrails. Discover as a group with fellow security leaders how to identify safety and security risks relevant to your workload, implement appropriate mitigation strategies, and measure efficacy over time.

GRC337 | Chalk talk | Build compliant AI: Implementing controls for emerging regulations
Speakers: Samuel Waymouth (AWS) & Mark Keating (AWS)
As AI adoption accelerates, organizations face increasing regulatory scrutiny and compliance requirements. In this session, learn about the evolving global regulatory landscape for AI, data privacy, and data sovereignty, then see how you can map regulatory requirements and security controls to AWS services and features. We will demonstrate how generative AI can work as a tool for assessment, risk classification and generating compliance guidance. We also show you how to use the latest threat modelling resources developed by AWS. Security professionals and AI practitioners will learn actionable strategies for building AI systems aligned with compliance standards while also maintaining innovation velocity.

SEC221 | Lightning talk | Raising the tide: How AWS is shaping the future of secure AI
Speakers: Matt Saner (AWS)
AI security is a top priority for AWS. By building AI solutions that are secure by design, AWS helps customers innovate quickly with confidence while mitigating emerging threats. But securing AI goes beyond individual organizations—it requires industry-wide standards and best practices. AWS actively contributes to global AI security efforts, including its participation industry standards bodies such as CoSAI (The Coalition for Secure AI), to make sure AI technologies are safe, resilient, and trustworthy. This session will explore how AWS is leading AI security innovation, protecting customers, and collaborating to help shape the future of AI security for the entire industry.

SEC322 | Lightning talk | Managing digital identity in the age of generative AI
Speakers: Arthur Mnev (AWS) & Lily Ashidam (AWS)
In this session, we will explore the challenges and solutions for managing identities in generative AI workloads. This session covers securing API access for LLMs, implementing proper authentication for, in, and with AI services, and maintaining data lineage. Learn practical approaches towards securing generative AI applications while maintaining compliance and governance requirements.

SEC323 | Lightning talk | A practical guide to generative AI agent resilience
Speakers: Yiwen Zhang (AWS) & Jennifer Moran (AWS)
As generative AI agents dominate headlines and technological discussions, enterprise adoption remains in its infancy. GenAI agent resilience is a crucial factor in successful implementation and building user trust. While traditional workload resilience practices—such as database availability, workload capacity, observability, and disaster recovery—remain relevant, GenAI agents present unique challenges. This session delves into the critical dimensions of GenAI agent resilience, including LLM model adaptability, latency management, tool availability, observability, and financial sustainability. We will share practical strategies for building robust, reliable GenAI agents that enterprises can trust and maintain.

SEC326 | Lightning Talk | Secure remote MCP server deployment for Gen AI on AWS
Speakers: Aaron Brown (AWS) & James Ferguson (AWS)
Discover how to securely build and deploy remote Model Context Protocol (MCP) servers on AWS that implement the protocol’s security and trust principles. This session demonstrates OAuth 2.1 authorization patterns that enforce user consent, data privacy, and tool safety requirements. Learn to implement robust security controls using Amazon Cognito, API Gateway, and Lambda while maintaining protocol compliance. Explore practical examples of authorization flows, access controls, and security monitoring that align with MCP specifications.

TDR322 | Lightning talk | How AWS uses generative AI to advance native security services
Speakers: Marshall Jones (AWS) & Himanshu Verma (AWS)
Discover how AWS leverages generative AI to enhance native security services. This session demonstrates how AWS implements AI capabilities across its security portfolio to improve threat detection, investigation, and response. Explore practical implementations in Amazon GuardDuty and Amazon Inspector that enable automated analysis and natural language security queries. Leave with insights into how AWS makes security more intelligent and efficient through generative AI.

Interactive sessions (builders’ sessions, code talks, and workshops)

Interact with small groups led by an AWS expert providing interactive learning about how to build on AWS. Each builders’ session begins with a short explanation or demonstration of what attendees are building—then it’s your turn to build! The expert will guide you end-to-end through this hands-on experience. Or join Code Talks, our code-focused interactive sessions where AWS experts lead a discussion featuring live coding or code samples as they illuminate the “why” behind AWS solutions. Attendees are encouraged to ask questions and follow along.

Workshops are two-hour interactive sessions where you collaborate in teams or work individually to solve real-world challenges by using AWS services, making them perfect for hands-on learning. Each workshop begins with a brief lecture, followed by dedicated time to work through the problem.

Note: Don’t forget to bring your laptop to build alongside AWS experts.

SEC351 | Builders’ session | Accelerating incident response, compliance & auditing using generative AI
Speakers: Snehal Nahar (AWS), Ravindra Kori (AWS), Rayette Toles-Abdullah (AWS), & Abhijit Barde (AWS)
In this session, we will learn how to use AWS native generative AI capabilities to reduce time to recovery after an incident using enterprise communication tools such as Slack. We will also learn how to use detective controls to identify events that may result in an incident, and also how to use preventive controls to mitigate the risk of an incident occurring. We will use services like Amazon Q Developer, AWS Config, AWS CloudTrail Lake, Amazon CloudWatch and other observability features.

SEC352 | Builders’ session | Agentic AI for security: Building intelligent egress traffic controls
Speakers: Ranjith Rayaprolu (AWS), Anil Nadiminti (AWS), Michael Leighty (AWS), & Dwaragha Sivalingam (AWS)
Learn to build AI-powered security agents that protect your cloud infrastructure. This hands-on session shows you how to use Amazon Bedrock and Bedrock Agents to create intelligent systems that watch over your network. You’ll build Generative AI agents that monitor egress traffic, spot potential threats, and automatically update network firewall to block malicious traffic. Walk away with the skills to implement AI-powered security agents that can reason, decide, and act to protect your cloud infrastructure.

SEC353 | Builders’ session | Threat modeling for generative AI applications
Speakers: Laura Verghote (AWS), Isabelle Mos (AWS), Samuel Waymouth (AWS), & Omar Zoma (AWS)
In this builders’ session, you will learn how to systematically identify and analyze security threats specific to generative AI applications. As organizations rapidly adopt large language models and other generative AI capabilities, understanding the unique security challenges – from prompt injection to data poisoning – becomes critical. You will be guided through the process of creating threat models for common generative AI architectures, with a particular focus on applications built using AWS services like Amazon Bedrock.

SEC451 | Builders’ session | From logs to defense: Generative AI for security automation
Speakers: Ravindra Kori (AWS), Siavash Iran (AWS), Lily Ashidam (AWS), & Yiwen Zhang (AWS)
In this technical session, we’ll demonstrate how to transform traditional operating system log analysis into an intelligent, automated defense system using AWS native services and generative AI. We’ll explore how to build a comprehensive solution that captures security-relevant logs from Windows and Linux systems.

APS351 | Builders’ session | Securing generative AI agents using AWS Well-Architected Framework
Speakers: Krupanidhi Jay (AWS), Ryan Dsouza (AWS), Birender Pal (AWS), & Omkar Mukadam (AWS)
Learn hands-on how to build secure generative AI agent solutions following the AWS Well-Architected Framework’s Generative AI Lens security best practices. Work through practical implementations of endpoint security, prompt engineering guardrails, monitoring systems, and protection against excessive agency while building a production-ready generative AI agent. Through hands-on exercises, build a secure generative AI agent solution incorporating these controls on AWS, involving Amazon Bedrock, Amazon CloudWatch, AWS Identity and Access Management (IAM), and more. You must bring your laptop to participate.

APS353 | Builders’ session | Red teaming your LLM security at scale
Speakers: Otto Kruse (AWS), Owen Hawkins (AWS), Aaron Brown (AWS), & Jeff Lombardo (AWS)
Step into the shoes of an AI-powered red team adversary in the GenAI Red Team Challenge. In this intensive workshop, you’ll deploy an AI security agent to orchestrate sophisticated threat chains against GenAI applications, systematically discovering and exploiting vulnerabilities from prompt injection to boundary testing while mastering automated security testing workflows. In addition, you’ll learn to apply countermeasures, from prompt templating to guardrails. This hands-on, gamified experience helps you think like a threat actor and equips you with practical skills in automated vulnerability testing and risk mitigation against common MITRE and OWASP vulnerabilities for LLM-based applications. You must bring your laptop to participate.

GRC354 | Builders’ session | Best practices for using generative AI to manage cloud compliance
Speakers: Adnan Bilwani (AWS), Ali Maaz (AWS), Artur Rodrigues (AWS), & Peter Pereira (AWS)
Learn how to leverage Amazon Q Developer to streamline cloud compliance management using AWS Config. This hands-on builders’ session demonstrates how to create intelligent compliance checks, automate remediation workflows, and generate detailed compliance reports using generative AI capabilities. Through practical exercises, learn to implement automated compliance monitoring that combines the power of generative AI with AWS Config’s robust compliance framework. You must bring your laptop to participate.

IAM451 | Builders’ session | Securing GenAI apps: Fine-grained access control for Bedrock Agents
Speakers: Edward Sun (AWS), Pravin Nair (AWS), Dustin Ellis (AWS), & Kevin Hakanson (AWS)
Want to secure generative AI applications accessing your organizational data? Learn how to implement intelligent access controls for Amazon Bedrock-powered applications accessing your organizational data. In this builders’ session, you’ll build a defense-in-depth approach that combines authentication using Amazon Cognito and fine-grained authorization with Amazon Verified Permissions to secure access for Bedrock AI agents. Implement layered permissions that protect sensitive data without limiting your GenAI capabilities. You must bring your laptop to participate.

TDR251 | Builders’ session | Build your first AI security assistant with Amazon Q
Speakers: Scott Taggart (AWS), Joe Wagner (AWS), Laura Verghote (AWS), & Riggs Goodman III (AWS)
Discover how to build your first AI-powered security assistant using Amazon Q Business – no AI expertise required. In this hands-on session, you’ll create three practical security workflows: an automated Amazon GuardDuty incident investigator that contextualizes security findings, an AWS Security Hub compliance report generator that streamlines policy assessments, and an Amazon Inspector-based vulnerability management helper that accelerates remediation. Perfect for security practitioners who want to enhance AWS security operations with generative AI while mastering core AWS security services through practical application. You must bring your laptop to participate.

IAM441 | Code talk | The right way to secure AI agents with code examples
Speakers: Jeff Lombardo (AWS) & Fei Yuan (AWS)
Generative AI agents run tasks on behalf of human users and often interact with each other across on-premises environments and different cloud providers. This brings new challenges in identity authentication, propagation, delegation, and resource authorization in the overall agentic AI solution. Learn how Amazon Cognito’s OAuth2-based identity management, machine-to-machine authentication, combined with Amazon Verified Permissions fine-grained authorization can enable secure delegation patterns for AI agents, while preserving human identity and consent, agent machine identity, and other request context throughout the agent chain. We will walk through real-world examples with agents built on Amazon Bedrock or other frameworks.

TDR341 | Code talk | Build AI security agents with Amazon Bedrock and Amazon Security Lake
Speakers: Chris Lamont-Smith (AWS) & Pratima Singh (AWS)
In this code talk, explore how to enhance security operations by creating AI agents using Amazon Bedrock and Amazon Security Lake. Through live coding demonstrations, learn to build automated workflows that combine autonomous decision-making capabilities with generative AI for security analysis and response. See how to implement agents that analyze logs, provide contextual insights, and execute response procedures. Discover practical approaches for integrating custom tools and leveraging large language models in your security workflows.

SEC371 | Workshop | Red Team approaches to practical generative AI defenses
Speakers: Mac Stevens (AWS) & Cameron Smith (AWS)
This workshop takes a hands-on approach to Generative AI security, focusing on Amazon Bedrock, Amazon SageMaker, and related services. We’ll begin by examining Bedrock’s core security principles, including data protection during inference and in features like Agents, Guardrails, and Knowledge Bases. Participants will gain insights into the internal architectures and security implications of context windows, system prompts, agent orchestration, and more. The session then transitions into hands-on red teaming exercises using SageMaker. We’ll subsequently explore defensive strategies against these threat vectors and discuss methods for integrating these practices into development workflows. Participants will leave equipped with a holistic understanding of Generative AI security, from individual model protection to safeguarding complex, multi-component systems.

APS371 | Workshop | Securing your generative AI applications on AWS
Speakers: Mark Keating (AWS) & Maitreya Ranganath (AWS)
In this workshop, discover how to secure generative AI applications using AWS services and features. Explore how to deploy a vulnerable sample generative AI application and then layer security controls to protect, detect, and respond to security issues. Learn how to apply similar controls to the generative AI applications in your organization. You must bring your laptop to participate.

DAP371 | Workshop | Defend your AI: Mitigate prompt injection with Amazon Bedrock
Speakers: Mark Keating (AWS) & Maitreya Ranganath (AWS)
Master the art of identifying and mitigating prompt injection vulnerabilities in generative AI systems through this hands-on workshop. Using Amazon Bedrock, participants will explore both offensive and defensive prompt engineering techniques to understand the security implications of large language models in production environments. In this session you will understand how prompt injection attacks work, complete an interactive ‘capture the flag’ style challenge attempting to exploit a simulated AI environment, and learn to implement defensive controls using Amazon Bedrock Guardrails. You must bring your laptop to participate.

Register now

Don’t miss this opportunity to learn from industry experts and AWS leaders about securing your AI implementations. Register for AWS re:Inforce 2025 today to reserve your spot in these sessions. Browse the full re:Inforce catalog to learn more about sessions in other tracks, plus partner sessions and code talks.

If you have feedback about this post, submit comments in the Comments section below.

Navigating the threat detection and incident response track at re:Inforce 2025

2025-05-27 Nisha Amthul

Post Syndicated from Nisha Amthul original https://aws.amazon.com/blogs/security/navigating-the-threat-detection-and-incident-response-track-at-reinforce-2025/

AWS re:Inforce 2025: June 16-18 in Philadelphia, PA

A full conference pass is $1,099. Register today with the code flashsale150 to receive a limited time $150 discount, while supplies last.

We’re counting down to AWS re:Inforce, our annual cloud security event! We are thrilled to invite security enthusiasts and builders to join us in Philadelphia, PA June 16–18, 2025, for an immersive three-day journey into cloud security learning. At AWS re:Inforce, you’ll have the chance to explore the breadth of the Amazon Web Services (AWS) security landscape, learn how to operationalize security services, and enhance your skills and confidence in cloud security to improve your organization’s security posture. As an attendee, you will have access to over 250 sessions across multiple topic tracks, including data protection; identity and access management; threat detection and incident response; network and infrastructure security; generative AI; governance, risk, and compliance; and application security. Plus, get ready to be inspired by our lineup of customer speakers, who will share their firsthand experiences of innovating securely on AWS.

In this post, we provide an overview of the key sessions that include lecture-style presentations featuring real-world use cases from our customers and interactive small-group sessions led by AWS experts that guide you through practical problems and solutions.

The threat detection and incident response track is designed to demonstrate how to detect and respond to security risks to help protect workloads at scale. AWS experts and customers will present key topics such as unified cloud security, threat detection, vulnerability management, cloud security posture management, integrated detection-to-response, threat intelligence, operationalization of AWS security services, container security, effective security investigation, security analytics, and incident response best practices. We’ll also explore both strengthening security through the use of generative AI and securing generative AI workloads.

Breakout sessions, chalk talks, and lightning talks

TDR301 | Breakout session | Innovations in AWS detection and response for integrated security outcomes
Discover how AWS’s latest detection and response capabilities can help secure your cloud environment more effectively. Learn practical ways to achieve integrated security outcomes through enhanced threat detection, automated vulnerability management, and streamlined response—all at scale. We’ll show you how to use AWS security services to protect workloads and data, centralize security monitoring, manage security posture continuously, and unify security data, while leveraging generative AI for security operations. Walk away with actionable insights on integrating AWS detection and response services to strengthen and simplify your security across AWS.

TDR302 | Breakout session | Multi-stage threat detection using GuardDuty and MITRE
Enhance your threat detection capabilities by leveraging Amazon GuardDuty Extended Threat Detection alongside MITRE frameworks. In this session, Shane Steiger Esq. from MITRE Corp demonstrates how to effectively identify and respond to multi-stage security events in your AWS environment. Learn practical strategies for implementing detection controls, developing response procedures, and building resilient cloud architectures. Discover how integrating GuardDuty with MITRE frameworks can strengthen your event detection and response strategy.

TDR303 | Breakout session | Building secure generative AI security tools, featuring Trellix
Learn how to build enterprise-grade generative AI security tools that unify security data and enable natural language investigations. This session demonstrates practical approaches for developing secure generative AI solutions, including implementation patterns for data privacy and compliance controls. Explore real-world architectures combining AWS foundation models with security orchestration. Hear how Trellix achieved 23x cost savings while maintaining 95% accuracy using Amazon Bedrock models. Leave with strategies to build secure AI assistants that support your security teams.

TDR304 | Breakout session | Scaling AWS threat intelligence to protect customers
Discover how AWS builds and operates threat intelligence at unprecedented scale to protect millions of customers. In this session, dive deep into two critical security functions: Amazon Threat Intelligence, which tracks and defends against sophisticated threats, and Active Defense, our security data processing architecture that analyzes over 4 billion records per second. Learn how these capabilities work together to power AWS security services and provide automated protection for your applications. See how AWS uses this intelligence to continuously enhance security services that help keep your workloads safe.

TDR305 | Breakout session | Scale Vulnerability Management Using Amazon Inspector
Want to strengthen Lambda security and streamline vulnerability management? Learn how Amazon Inspector uses generative AI to provide in-context code patches and automate SBOM management. Discover practical techniques for CI/CD integration, cross-account scanning, and automated remediation workflows. Explore built-in integrations with Security Hub and EventBridge to enhance security operations across your AWS environment.

TDR306 | Breakout session | Enterprise Security at Scale: SAP’s AWS Blueprint
How does SAP protect thousands of AWS accounts? Learn their blueprint for implementing Amazon GuardDuty protection plans alongside Extended Threat Detection to identify sophisticated threat patterns. Discover their framework for standardizing AWS Security Hub controls and automated remediation workflows at scale. Walk away with practical strategies to enhance enterprise security operations across AWS Organizations.

TDR331 | Chalk talk | Ask AWS: Your ransomware questions answered
Get answers to your most critical ransomware questions in this interactive Q&A session. Learn how AWS security features and best practices can help you detect, respond to, and recover from ransomware threats. Our experts will share practical guidance on identifying early warning signs, implementing effective incident response, and strengthening your overall ransomware resilience. Bring your toughest questions about emerging ransomware tactics and cloud protection strategies. Walk away with actionable insights to help secure your data and operations using AWS security capabilities.

TDR332 | Chalk talk | Decoding AWS CIRT tactics & techniques for proactive defense
Learn directly from AWS Customer Incident Response Team (CIRT) experts who help customers respond to critical security events. Discover real-world insights about emerging threat tactics and techniques observed across AWS environments. We’ll share practical detection and mitigation strategies that align with the Shared Responsibility Model, helping you strengthen your security posture. Walk away with actionable best practices from CIRT’s frontline experience defending against evolving threats, and learn how to apply these insights to protect your AWS workloads.

TDR333 | Chalk talk | Strategy for prioritization and response
Join this session to discuss managing security posture and risk across multiple accounts, regions, and resources. We will explore the decision-making process around how you prioritize security alerts and risk using AWS security services. After prioritization, we will discuss a framework for responding to and remediating security findings. We will talk through the decision-making process of responding to findings, considerations for auto-remediation, and how to facilitate a quick and thorough response to the most critical security findings.

TDR334 | Chalk talk | Strengthen Security: Making GuardDuty Protection Plans Work for You
Discover how to maximize your threat detection capabilities by selecting the right Amazon GuardDuty protection plans for your environment. Learn to evaluate protection features that matter most for your AWS workloads and understand the value each plan brings to your security strategy. Through practical scenarios, explore cost-effective implementation strategies across your AWS accounts. Leave with actionable insights for optimizing your Amazon GuardDuty deployment to enhance protection of your AWS workloads and data.

TDR431 | Chalk talk | Best practices for containing AWS resources during incident response
Learn best practices for implementing isolation controls for AWS resources and accounts during security events. Through practical scenarios, discover effective approaches for isolating Amazon EC2 instances, AWS Lambda functions, and Amazon ECS containers. Explore comprehensive strategies for account-level isolation including identity, resource, and network controls. This session provides guidance on implementing and safely removing isolation controls as part of your response procedures. Leave with actionable patterns for strengthening your AWS incident response capabilities. To help businesses move faster and deliver security outcomes, modern security teams need to identify opportunities to automate and simplify their workflows. One way of doing so is through generative AI. Join this chalk talk to learn how to identify use cases where generative AI can help with investigating, prioritizing, and remediating findings from Amazon GuardDuty, Amazon Inspector, and AWS Security Hub. Then find out how you can develop architectures from these use cases, implement them, and evaluate their effectiveness. The talk offers tenets for generative AI and security that can help you safely use generative AI to reduce cognitive load and increase focus on novel, high-value opportunities.

TDR336 | Chalk talk | Secure generative AI models and agents on AWS
Learn how to strengthen security controls for generative AI models and Amazon Bedrock agents in your AWS environment. This session explores implementation patterns for protecting API endpoints and securing agent interactions. Discover practical approaches for implementing protective controls and maintaining data security for your AI/ML workloads. Leave with actionable strategies for building secure generative AI implementations using AWS services.

TDR337 | Chalk talk | Implementing AWS security best practices: Insights & strategies
Learn how to optimize your AWS security services implementation including Amazon GuardDuty, AWS Security Hub, and AWS WAF. AWS security experts share practical insights and proven patterns derived from thousands of customer deployments. This session provides actionable strategies for operationalizing security services effectively in your environment. Discover implementation best practices and architectural approaches that help you maximize the value of your AWS security services.

TDR338 | Chalk talk | Building cloud-native forensic investigation architectures on AWS
Join this chalk talk to explore the advantages of cloud-native digital forensics and incident response on AWS. Engage in interactive discussions on best practices for establishing secure forensic investigation environments. We’ll explore architectural patterns for safely collecting and storing forensic artifacts, leveraging ephemeral resources to enhance security, and implementing effective network, account, and organizational designs. Bring your questions and scenarios as we collaboratively examine how to build scalable, standardized investigation processes using AWS services. Leave with practical strategies for enhancing your forensic and incident response capabilities in the cloud.

TDR231 | Chalk talk | Resilient security teams: Reduce burnout and boost performance
Learn strategies for building resilient security and incident response teams that prioritize wellbeing while maintaining high performance. This session explores approaches for implementing regular team check-ins, data-informed wellbeing initiatives, and a supportive team culture. Discover practical methods for fostering open communication, maintaining team engagement, and recognizing team contributions. Through real-world examples, develop actionable plans to enhance team resilience, improve retention, and sustain security excellence. Leave with strategies to build and maintain high-performing security teams.

TDR321 | Lightning talk | From Incidents to Insights: Creating a Security Learning Organization
Learn how to transform security events into organizational improvements. This session demonstrates practical approaches for building effective feedback loops, preserving institutional knowledge, and implementing sustainable enhancements to security operations. Discover AWS strategies for measuring the impact of improvements and fostering a culture of continuous learning. Leave with actionable frameworks for strengthening your security program through systematic learning and adaptation.

TDR322 | Lightning talk | How AWS uses generative AI to advance native security services
Discover how AWS leverages generative AI to enhance native security services. This session demonstrates how AWS implements AI capabilities across its security portfolio to improve threat detection, investigation, and response. Explore practical implementations in Amazon GuardDuty and Amazon Inspector that enable automated analysis and natural language security queries. Leave with insights into how AWS makes security more intelligent and efficient through generative AI.

TDR323 | Lightning talk | How Autodesk scales threat detection with Amazon GuardDuty
Learn how Autodesk elevated their threat detection strategy using Amazon GuardDuty. This lightning talk explores their implementation approach, operational insights, and best practices for leveraging the advanced detection capabilities of GuardDuty, including malware protection. Discover how they maintain robust security while efficiently managing their growing cloud footprint.

TDR421 | Lightning talk | Accelerating Incident Response with AWS Security Incident Response
Learn how AWS Security Incident Response helps security teams streamline investigation and response procedures. This session demonstrates service integration capabilities with Amazon GuardDuty, AWS CloudTrail, and AWS Security Hub to provide centralized incident management. Through customer examples and implementation patterns, discover practical approaches for building automated response strategies. Leave with actionable insights for enhancing your security operations using AWS services.

Interactive sessions (builders’ sessions, code talks, and workshops)

TDR251 | Builders’ session | Build your first AI security assistant with Amazon Q
Discover how to build your first AI-powered security assistant using Amazon Q Business—no AI expertise required. In this hands-on session, you’ll create three practical security workflows: an automated Amazon GuardDuty incident investigator that contextualizes security findings, an AWS Security Hub compliance report generator that streamlines policy assessments, and an Amazon Inspector-based vulnerability management helper that accelerates remediation. Perfect for security practitioners who want to enhance AWS security operations with generative AI while mastering core AWS security services through practical application.

TDR252 | Builders’ session | Detect ransomware events in Amazon S3 using Amazon GuardDuty
In this builders’ session, join the AWS Customer Incident Response Team (CIRT) to implement Amazon S3 ransomware detection using Amazon GuardDuty. Through hands-on scenarios, learn to identify unauthorized encryption operations and implement effective response procedures. Build detection patterns using AWS CloudTrail, Amazon Athena, Amazon GuardDuty, and Amazon CloudWatch. Practice investigating events and implementing preventive measures aligned with AWS Security’s latest guidance for Amazon S3 object protection. You must bring your laptop to participate.

TDR351 | Builders’ session | Build an OCSF security log pipeline with AWS
Build a complete security log pipeline that leverages the Open Cybersecurity Schema Framework (OCSF) in this hands-on session. Work alongside AWS experts to ingest, transform, and enrich your security data. Learn practical techniques to standardize security logs, whether using your own schema or our provided examples. Walk away with implementable solutions to enhance your threat detection capabilities through normalized security data flows. Bring your laptop and optional custom log samples to create solutions tailored to your use cases.

TDR451 | Builders’ session | Automate incident response for Amazon EC2 and Amazon EKS
Learn how to streamline incident response using the Automated Forensics Orchestrator solution for Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Kubernetes Service (Amazon EKS). This session demonstrates how to implement automated workflows triggered by AWS Security Hub findings. Explore implementation prerequisites, customization options, and best practices for enhancing your security operations through automated forensics capabilities. Discover how to standardize response procedures across your Amazon EC2 and Amazon EKS environments.

TDR452 | Builders’ session | Build generative AI security runbooks with Amazon Bedrock
In this builders’ session, learn how to enhance security operations using generative AI-powered runbooks with Amazon Bedrock and Bedrock Agents. Create intelligent workflows that analyze AWS Security Hub findings and provide contextual remediation guidance. Through hands-on exercises, build Bedrock Agents that leverage AWS documentation and implement natural language interfaces for security investigations. Learn how to configure knowledge bases with organization-specific content and implement appropriate guardrails. Leave with a practical solution for streamlining security operations using generative AI. You must bring your laptop to participate.

TDR341 | Code talk | Build AI security agents with Amazon Bedrock and Security Lake
In this code talk, explore how to enhance security operations by creating AI agents using Amazon Bedrock and Amazon Security Lake. Through live coding demonstrations, learn to build automated workflows that combine autonomous decision-making capabilities with generative AI for security analysis and response. See how to implement agents that analyze logs, provide contextual insights, and execute response procedures. Discover practical approaches for integrating custom tools and leveraging large language models in your security workflows.

TDR342 | Code talk | Operationalizing Amazon Security Lake with analytics and generative AI
Roll up your sleeves for this hands-on coding session where we’ll build modern security analytics tools on top of Amazon Security Lake. Through interactive demos, we’ll craft queries and visualizations to operationalize your security data using AWS services like Amazon OpenSearch Service, Amazon QuickSight, Amazon Athena, and Amazon Bedrock. Leave with practical code samples and architectures to analyze security data. Get inspired with ideas on how to transform your threat detection and incident response stack.

TDR343 | Code talk | From detection to code: GuardDuty attack sequences with Amazon Q
In this code talk, explore how Amazon GuardDuty attack sequence detection capabilities work alongside Amazon Q to enhance security operations. Through live coding demonstrations, learn hoGuardDuty machine learning models identify connected security events and create comprehensive event sequences. See how to build automated response procedures using Amazon Q AI-assisted development capabilities. Discover practical approaches for implementing context-aware security automation. Leave with implementation patterns for enhancing your security operations using generative AI tools.

TDR371 | Workshop | Hands-on Threat Detection & Response using AWS Security
Get hands-on experience with AWS security services in this interactive workshop. Learn to detect and respond to simulated threats using Amazon GuardDuty, Amazon Inspector, AWS Security Hub, and Amazon Detective. Practice both manual and automated response techniques with AWS Lambda as you investigate security events across different resource types. Walk away with practical skills to operationalize threat detection and response in your AWS environment. Bring your laptop to participate in this hands-on workshop.

TDR372 | Workshop | Secure container workloads with AWS security services
In this workshop, learn how to implement AWS security services to protect container workloads end-to-end from code to operations. Gain hands-on experience with static code analysis, detective controls, threat detection, vulnerability management, and incident response for Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon Elastic Container Service (Amazon ECS). Through guided scenarios, discover how to use AWS security services to enhance your container security posture. Leave with practical strategies for implementing security controls in your container environments. You must bring your laptop to participate.

TDR471 | Workshop | AWS Security Incident Response Challenge: Defense in action
Put your AWS security incident response skills to the test in this interactive session. Assume the role of an AWS Security Engineer responding to a time-sensitive scenario. Using provided intelligence, you’ll have a limited time to implement security controls in your AWS environment. Learn to prioritize actions and leverage AWS security services effectively under realistic conditions. This hands-on exercise helps you practice rapid decision-making and security implementation in AWS environments. Leave with practical experience in incident response strategies. You must bring your laptop to participate.

TDR472 | Workshop | Active defense strategies using AWS AI/ML services
This workshop will help you learn how to develop and deploy active defense strategies, such as deception, using Amazon Bedrock and Amazon SageMaker. Gain hands-on experience developing AI-driven responses for security operations. You will learn how to develop adaptive responses that mimic what an actor may be trying use against you. You will Learn implementation patterns for prompt engineering, deployment strategies, and monitoring methodologies. You must bring your laptop to participate.

Browse the full re:Inforce catalog to learn more about sessions in other tracks, plus gamified learning, innovation sessions, partner sessions, and labs. Discover how to optimize your re:Inforce journey with our attendee guides—your essential resource for selecting perfect learning sessions and getting the greatest value from your experience.

Our comprehensive track content is designed to help arm you with the knowledge and skills needed to securely manage your workloads and applications on AWS. Don’t miss out on the opportunity to stay updated with the latest best practices in threat detection and incident response. Join us in Philadelphia for re:Inforce 2025 by registering today. We can’t wait to welcome you!

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Protect against advanced DNS threats with Amazon Route 53 Resolver DNS Firewall

2025-05-14 Lawton Pittenger

Post Syndicated from Lawton Pittenger original https://aws.amazon.com/blogs/security/protect-against-advanced-dns-threats-with-amazon-route-53-resolver-dns-firewall/

Every day, millions of applications seamlessly connect users to the digital services they need through DNS queries. These queries act as an interface to the internet’s address book, translating familiar domain names like amazon.com into the IP addresses that computers use to appropriately route traffic. The DNS landscape presents unique security challenges and opportunities in Amazon Virtual Private Cloud (Amazon VPC) environments. First, DNS resolution acts as an early checkpoint that you can use to control network traffic before it even begins. Second, DNS queries in your VPC follow a distinct path through the Amazon Route 53 Resolver that operates independently from your standard internet gateway, bypassing other network security controls.

To address this, Amazon Route 53 Resolver DNS Firewall provides protection for DNS traffic, starting with traditional domain lists where you can explicitly allow or deny DNS resolution of specific domains. Also, included are AWS Managed Domain Lists, which automatically block known malicious domains identified through Amazon Threat Intelligence and our trusted security partners. While this approach works effectively to help prevent known threats, sophisticated bad actors are increasingly using techniques that traditional blocklists can’t catch.

Instead of relying solely on static lists, Amazon Route 53 Resolver DNS Firewall Advanced provides intelligent protection alongside these traditional controls. These advanced rules work like a skilled security analyst, watching for suspicious patterns in DNS queries in real time. By examining characteristics such as query length, entropy, and frequency, the service can spot potentially malicious activity even when encountering previously unknown domains. This approach enables detecting and blocking advanced threats like DNS tunneling and domain generation algorithms (DGAs)—techniques that bad actors use to establish hidden communication channels or connect malware to their control servers.

In this post, we take you on a practical journey exploring these DNS-based threats and tools to help prevent them. You’ll learn how to set up effective Route 53 Resolver DNS Firewall Advanced rules, and we provide a ready-to-deploy CloudFormation template with our recommended configurations. Finally, we demonstrate an example of real-world threat detection and show you how the service integrates with AWS Security Hub to improve visibility of alerts. By the time you finish reading this post, you’ll have a clear understanding of how to deploy Route 53 Resolver DNS Firewall rules to add an intelligent, proactive layer of security to your AWS environment.

Understanding the risks of DNS tunneling and DGAs

As mentioned earlier, the Route 53 Resolver provides a service-managed path to the internet that operates independently from your VPC’s internet gateway. While this architecture enables efficient DNS resolution, it can be exploited through techniques such as DNS tunneling. Let’s explore how these techniques work and why they present unique challenges.

DNS tunneling takes advantage of the DNS protocol’s basic function—asking questions about domain names and receiving answers from the authoritative nameserver for the domain. But instead of using DNS for its intended purpose of domain name resolution, tunneling encodes other types of data within DNS queries and responses. For example, rather than asking simply what is the IP address for example.com?, a tunneling exploit might embed data within a query like secretdata123.attacker.com, where secretdata123 contains encoded information. This can lead to DNS being used as a two-way communications command and control channel. Detecting and blocking DNS tunneling is a vital control for stopping data exfiltration and command and control (C2) communications.

DGAs represent a different challenge for DNS security. Rather than using a fixed, predictable domain name that can be quickly blocked, DGAs automatically create many possible domain names using mathematical formulas, which are then used as a destination for C2 traffic. For instance, a DGA might generate domains like xkt7py.com today and mn9qrs.com tomorrow. This makes it difficult to maintain effective blocklists, because the domains change frequently and appear random. Traditional threat intelligence feeds, which rely on identifying and blocking known malicious domains, struggle to keep pace with DGA-generated domains.

How DNS Firewall Advanced works

When examining a domain name, Route 53 Resolver DNS Firewall Advanced looks at multiple characteristics that help distinguish between legitimate and suspicious domains. For example, legitimate domain names typically use real words and follow predictable patterns that are designed to facilitate a human’s ability to recall and enter them accurately. In contrast, domains used for tunneling or generated by DGAs often contain random-looking strings of characters or unusual patterns.

Route 53 Resolver DNS Firewall Advanced builds its intelligence on extensive analysis of real-world domain usage patterns. It learns what legitimate domain names look like by studying the most resolved domains on the internet, combined with actual domain resolution patterns from across AWS. This real-world training data helps establish a baseline for normal domain name characteristics. DNS Firewall Advanced then contrasts these patterns against known techniques used in DNS tunneling and domain generation to identify suspicious activity.

The service analyzes various aspects of each domain name, including:

How the domain name is structured and broken into parts
The patterns of letters and numbers used
How closely the domain resembles natural language
The presence of common words versus random character combinations

The service analyzes queries in real time, processing each one in less than a millisecond, which maintains strong security controls without affecting your applications’ performance.

Route 53 Resolver DNS Firewall Advanced has customized protection levels that you can use to choose how aggressively you want to detect and respond to suspicious domains through confidence thresholds:

High confidence: This setting focuses on the most obvious threats, minimizing false positives. It’s ideal for production environments where blocking legitimate traffic could be disruptive.
Medium confidence: Provides balanced protection, suitable for most environments.
Low confidence: Offers the most detection but might require more tuning to avoid false positives. This setting is useful for high-security environments or for initial monitoring to understand traffic patterns.

You can combine these confidence levels with different actions (block or alert) to create a defense strategy that matches your security needs.

Manually create a DNS Firewall Advanced rule:

To start, we show you how to manually create a Route 53 Resolver DNS Firewall Advanced rule in the AWS Management Console. This rule will block DNS queries that it has detected to be DNS tunneling with high confidence.

To manually create a rule:

In the Route 53 console, choose Rules in the navigation pane, and then choose Add rule.

Figure 1: Rules in the Route 53 console
Enter a name for the rule and select DNS Firewall Advanced protections.

Figure 2: Add a rule
Under DNS Firewall Advanced protection:
1. Select DNS tunneling detection.
2. For Confidence threshold, select High.
3. Leave the Query type empty so that the rule applies to all query types.
Figure 3: Select DNS protection options
Under Action:
1. Select Block.
2. For the response, select OVERRIDE.
3. For the Record value, enter dns-firewall-advanced-block.
4. For the Record type, select CNAME.
5. Choose Add rule.
Figure 4: Configure actions for the rule

Deploy the recommended DNS Firewall Rule group using CloudFormation (OPTIONAL)

We’ve created an AWS CloudFormation stack that deploys the following recommended Route 53 Resolver DNS Firewall rules in a DNS Firewall rule group. We recommend this configuration because it provides a balanced security approach—blocking high-confidence threats immediately while generating alerts for lower-confidence detections.

The inclusion of the AWS Managed Aggregate Threat List is particularly valuable because it combines domains from multiple threat categories (malware, ransomware, botnet, spyware, and DNS tunneling) into a blocklist. This consolidated list includes the domains from other AWS Managed Domain Lists, including those identified by GuardDuty threat intelligence systems, giving you broad protection against known malicious domains while the Route 53 DNS Firewall Advanced rules catch previously unseen threats.

For enterprise environments, you can scale this protection across your entire organization by using AWS Firewall Manager to automatically deploy and manage this rule group configuration consistently across the VPCs in your organization.

BLOCK – Aggregate Threat List (domains associated with multiple DNS threat categories including malware, ransomware, botnet, spyware, and DNS tunneling to help block multiple types of threats)
BLOCK – DNS Tunneling | Confidence: HIGH
BLOCK – DGAs | Confidence: HIGH
ALERT – DNS Tunneling | Confidence: LOW
ALERT – DGAs | Confidence: LOW

To deploy this rule group using a CloudFormation stack:

Navigate to the CloudFormation console, choose Stacks from the navigation pane. Choose Create Stack in the upper right and select With new resources (standard).

Figure 5: Create a stack
Download the CloudFormation template. Select Choose an existing template and then select Upload a template file and upload the CloudFormation stack. Choose Next.

Figure 6: Use the CloudFormation template
Enter a stack name and choose Next.

Figure 7: Enter a stack name
Leave the default values for all options, select Next, and then choose Submit.
Navigate to the Route 53 Resolver DNS Firewall by visiting the Amazon VPC console, scroll down to the DNS firewall section, and select the Rule groups tab.
Select the newly created rule group.
Select the Associated VPCs tab, choose Associate VPC, and then associate a VPC you want to protect and choose Associate.

Figure 8: Associate a VPC

Observability

Route 53 Resolver query logging provides detailed visibility into DNS queries made from resources associated with your VPCs, enabling you to monitor and analyze your DNS traffic for security and compliance purposes. By configuring query logging, you can capture essential information about each DNS request, including the domain name being queried, the record type, the response code, and the originating VPC and instance. Query logging is particularly valuable when used in conjunction with Route 53 Resolver DNS Firewall, because it helps you track blocked queries and fine-tune your security rules based on actual DNS traffic patterns in your environment. The following are examples of log entries generated when DNS Firewall detects and responds to suspicious activities, showing the detailed information available for security analysis and incident response.

Example log entry: DNS tunneling block

The following is an example of a DNS tunneling block.

{
    "version": "1.100000",
    "account_id": "11111111111",
    "region": "us-west-2",
    "vpc_id": "vpc-0fcc85bd45b791d5a",
    "query_timestamp": "2025-02-05T03:54:12Z",
    "query_name": "1WTE4CyL4Vf1LQDDAToimuqFBEtMXyYMsYP8zPgVyTagzSh5PvinuQcL6N8at4A.REZv3VqKU4x43DPcCKAzQk4UKoZjB3nDMukHAuKTtDckTqZ8SDDZ1iXRey6a5sD.mEDMdrzPocS9exqoBQ1xfSuKfvW.1.dnstunnel.com.",
    "query_type": "A",
    "query_class": "IN",
    "rcode": "NXDOMAIN",
    "answers": [
        {
            "Rdata": "dns-firewall-advanced-block.",
            "Type": "CNAME",
            "Class": "IN"
        }
    ],
    "srcaddr": "10.1.0.122",
    "srcport": "41859",
    "transport": "UDP",
    "srcids": {
        "instance": "i-0c738190f19db9a2c"
    },
    "firewall_rule_action": "BLOCK",
    "firewall_rule_group_id": "rslvr-frg-63efa138b43f428b",
    "firewall_protection": "DNS_TUNNELING"
}

Example log entry: DNS tunneling alert

The following is an example of a DNS tunneling alert.

{
    "version": "1.100000",
    "account_id": "11111111111",
    "region": "us-west-2",
    "vpc_id": "vpc-0fcc85bd45b791d5a",
    "query_timestamp": "2025-02-05T04:00:02Z",
    "query_name": "1WTEc8GwFH3qHY8XKjbhXuj43yGShMrhacqwJYSZkSqRQ95sagz64NUpnuj4R8R.S79aru2KRB8d9nCHEPdXWJxGT4aUjVMqtCRSq9EZXRCo8NH5cmLvmcho3hh1mbK.NqGY1X6M4qpMGX6dnTSHuCsZFbf.1.dnstunnel.com.",
    "query_type": "A",
    "query_class": "IN",
    "rcode": "NOERROR",
    "answers": [
        {
        "Rdata": "202.92.34.217",
        "Type": "A",
        "Class": "IN"
        }
    ],
    "srcaddr": "10.1.0.122",
    "srcport": "35116",
    "transport": "UDP",
    "srcids": {
        "instance": "i-0c738190f19db9a2c",
        "resolver_endpoint": "rslvr-out-e20639d3666748f58"
    },
    "firewall_rule_action": "ALERT",
    "firewall_rule_group_id": "rslvr-frg-63efa138b43f428b",
    "firewall_protection": "DNS_TUNNELING"
}

Integration with Security Hub

Security Hub provides you with a view of your security state in AWS and helps you to check your environment against security industry standards and best practices. Security Hub collects security data from across AWS accounts, AWS services, and supported third-party partner products, and helps you to analyze security trends and identify the highest priority security issues. It enables findings from both the Amazon: Route 53 Resolver DNS Firewall – AWS List and Amazon: Route 53 Resolver DNS Firewall Advanced list by default, so you’ll automatically receive these alerts without additional configuration. You only need to manually enable Amazon: Route 53 Resolver DNS Firewall – Custom List findings if you’re using custom domain lists in your rule groups. See Sending findings from Route 53 Resolver DNS Firewall to Security Hub for more information.

The following figure is an example of how Route 53 Resolver DNS Firewall Advanced findings appear in the Security Hub console, providing you with actionable security intelligence directly in your centralized dashboard.

Figure 9: DNS Firewall Advanced findings in Security Hub

Select a finding to view details such as Finding ID, Types, Workflow status, and so on.

Figure 10: Findings details

Conclusion

Amazon Route 53 Resolver DNS Firewall Advanced represents a significant step forward in protecting organizations against sophisticated DNS-based threats. As mentioned, DNS queries sent to the Route 53 Resolver follow a unique path that bypasses traditional AWS security controls like security groups, NACLs, and even AWS Network Firewall—creating a security gap in many environments. Throughout this post, we’ve explored how DNS tunneling and DGA-based exploits take advantage of this blind spot, and how you can use Route 53 Resolver DNS Firewall Advanced to protect from these threats through real-time pattern analysis and anomaly detection. You learned how to configure the service in the AWS console and use the provided CloudFormation template with recommended rules that balance blocking high-confidence threats while alerting on potential issues. And you saw how query logging provides valuable visibility into your DNS traffic and how Security Hub integration centralizes your security findings. Implementing these capabilities helps you protect your infrastructure from sophisticated DNS-based exploits that traditional domain blocklists cannot catch, strengthening your cloud security posture while maintaining operational efficiency.

If you have feedback about this post, submit comments in the Comments section below.

AI lifecycle risk management: ISO/IEC 42001:2023 for AI governance

2025-05-13 Abdul Javid

Post Syndicated from Abdul Javid original https://aws.amazon.com/blogs/security/ai-lifecycle-risk-management-iso-iec-420012023-for-ai-governance/

As AI becomes central to business operations, so does the need for responsible AI governance. But how can you make sure that your AI systems are ethical, resilient, and aligned with compliance standards?

ISO/IEC 42001, the international management system standard for AI, offers a framework to help organizations implement AI governance across the lifecycle. In this post, we walk through how ISO/IEC 42001 enables effective AI governance, review the risk management requirements, and explore how you can use threat modeling as a practical technique to meet those expectations.

AI governance

AI governance refers to the organizational structures, policies, and controls that enable AI systems to be used responsibly, ethically, and safely. Governance spans the entire AI lifecycle and includes the following activities:

Setting the intended purpose and stakeholder alignment
Managing data, models, and deployment risks
Designing in explainability, bias mitigation, and traceability
Establishing accountability, monitoring, and decommissioning practices

These activities are the foundation of a formal framework that you can use to establish governance processes, identify and manage risk, and implement processes for continuous improvement

AI lifecycle

While ISO 42001 provides a framework for AI governance, ISO/IEC 22989:2022 describes what an AI system is and how it evolves. Governance should be implemented at every stage of the AI lifecycle to manage AI risks effectively. According to the ISO/IEC 22989:2022 standard, an organization’s AI life cycle might include these stages:

Inception: Identifying needs, goals, and feasibility
Design and development: Defining system architecture, data flows, and training models
Verification and validation: Testing and confirming that the system meets requirements and performs as intended
Deployment: Releasing the system into its operational environment
Operation and monitoring: Running the system, logging activity, and monitoring performance and outcomes
Re-evaluation: Assessing whether the system continues to meet objectives under changing conditions
Retirement: Decommissioning the system and addressing long-term data and access risks

Understanding the AI lifecycle, shown in Figure 1 that follows, is critical for identifying and mitigating AI risks. While these seven stages are provided directly in ISO 22989:2022, your organization might define its AI lifecycle stages differently to suit its business context. We refer to these stages as we explore the components of an AI management system, from initial AI system scoping, through threat monitoring and risk assessment, to monitoring the established governance program.

Figure 1: Example of AI system lifecycle model stages and high-level processes based on ISO/IEC 22989:2022

Risk management in ISO/IEC 42001:2023

After an organization has identified and assessed AI risks (Clause 6.1 of ISO/IEC 42001:2023), operational controls to mitigate those risks must be implemented (Clause 8.2), and those controls and the AI system itself should be continuously monitored, documented, and improved (Clauses 9 and 10). AI impact assessments (AIIAs) are critical in high-risk use cases, complementing baseline risk assessments by focusing on societal, ethical, and legal impacts. AIIAs are like data protection impact assessments (DPIAs) for high-risk personal data processing under many privacy regulations. DPIAs are specifically designed to assess risks to individuals’ privacy and data protection rights under laws such as the GDPR. While AIIAs help organizations maintain responsible AI governance, DPIAs can be used in parallel to help verify that AI systems comply with data protection laws, together providing a holistic view of risks and safeguards across both ethical and legal dimensions.

You are free to select the AIIA tools or methodologies that best fit your use case. Two widely accepted frameworks are:

ISO 31000: A general-purpose enterprise risk management standard that helps identify, evaluate, and treat risks in a structured and repeatable way. It aligns well with organizations seeking to embed AI risk into their broader enterprise risk management (ERM) programs.

NIST AI Risk Management Framework (AI RMF): A NIST framework specifically designed for AI systems. It introduces tailored concepts such as explainability, robustness, fairness, and accountability, with actionable guidance organized into four core functions: Map, measure, manage, and govern.

ISO 42001 provides structured methods to conduct risk and impact assessments. Threat modeling tools such as:

STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege). STRIDE aims to make sure that a system meets security requirements for confidentiality, integrity and availability.
DREAD (damage potential, reproducibility, exploitability, affected users, and discoverability) is a framework that can assess severity of individual threats.
OWASP (Open Worldwide Application Security Project) for machine learning (ML) enables analysis of AI system vulnerabilities, adversarial risks, and privacy threats.

Trustworthy AI is the result of strategic governance, structured methodologies, and technical analysis.

Figure 2 that follows shows the tiered structure of AI risk governance, moving from high-level governance to detailed technical assessments. On the left side, there’s a downward flow representing the increasing depth of controls, while the right side shows an upward scale indicating escalating AI risks.

At the top layer, ISO/IEC 42001:2023 defines formal requirements for AI governance, including risk assessment mandates, control implementation, and lifecycle oversight.
The middle layer features widely adopted risk assessment methodologies and frameworks, such as ISO 31000 and the NIST AI Risk Management Framework (RMF), which provide structured methods to identify, evaluate, and mitigate AI risks.
At the base, are detailed threat modeling tools—including STRIDE, DREAD, PASTA, LINDDUN, and OWASP for ML—that support deep analysis of AI systems for vulnerabilities related to security, privacy, data protection, and adversarial threats.

Together, these layers form a comprehensive approach to AI risk governance, aligning strategic oversight with operational and technical defenses.

Figure 2: A layered approach to AI risk management aligned with ISO/IEC 42001. ISO/IEC 42001 defines AI governance for responsible AI

Threat modeling for AI risk identification

Threat modeling identifies AI lifecycle technical risks such as exploit surfaces, adversarial threats, and misuse scenarios that complement organizational risk analysis and impact assessments. This post takes a broader AI lifecycle view, showing you how threat modeling complements other risk strategies within the context of ISO/IEC 42001:2023. Additionally, AWS has published AI threat modeling guidance, such as:

The following table is an example STRIDE threat model for a generative AI resource using AWS services by AI lifecycle stage and risk type. This illustrates technical threat remediation through AWS cloud native governance features.

STRIDE category	Example threat	Lifecycle stage	Risk type	AWS feature for governance
Spoofing	A fake identity uses the AI system to generate phishing emails or misinformation	Inception	Security	AWS IAM Identity Center and Amazon Cognito for multi-factor authentication (MFA), Amazon GuardDuty for threat detection
Tampering	A malicious prompt injection or API injection alters the model behavior or bypasses filters	Design development	Integrity	Amazon Bedrock Guardrails, Amazon API Gateway and AWS WAF rules, AWS CloudTrail for input auditing
Repudiation	Users deny prompt activity or content creation, and there’s no logging	Verification and validation	Accountability	CloudTrail, Amazon Bedrock invocation logs, Amazon SageMaker ML Lineage Tracking for traceability
Information disclosure	Sensitive internal data—such as code or personally identifiable information (PII)—accidentally learned and reproduced by the large language model (LLM)	Operation and monitoring	Privacy, Security	SageMaker Clarify, AWS VPC PrivateLink, AWS Key Management Service (AWS KMS) encryption, Amazon Bedrock data handling commitments
Denial of service	Bad actors overload the AI endpoint with prompt spam, degrading service	Deployment	Availability	AWS Shield, API rate limiting using API Gateway, auto scaling with SageMaker endpoints
Elevation of privilege	An internal user modifies system prompts or updates to override content filters	Reevaluation	Ethics and access control	AWS Identity and Access Management (IAM) roles, Amazon Bedrock Guardrails, AWS Config, service control policies (SCPs)

While STRIDE is used here for illustrative clarity, it’s just one of several threat modeling approaches that can be applied depending on the system context. Other widely recognized methods include:

OWASP Top 10 for LLMs: A threat-focused list targeting large language models
MITRE ATLAS: A framework for adversarial threat modeling in AI/ML systems
NIST AI Risk Management Framework (AI RMF): A United States standards-based approach focusing on trustworthy and responsible AI development
PASTA (Process for Attack Simulation and Threat Analysis): A risk-centric threat modeling methodology
LINDDUN: A privacy threat modeling framework addressing data protection risks

By integrating these threat modeling practices into ISO/IEC 42001’s risk-based approach, organizations are not just “checking compliance boxes” they’re operationalizing trustworthy, secure, and accountable AI governance throughout the full system lifecycle.

Threat modeling touchpoints across the AI lifecycle

ISO 42001:2023 uses the STRIDE threat modeling framework to align specific security threats to each stage. Each lifecycle stage is associated with particular threat types, relevant Annex references from the ISO standard, and examples of what to monitor.

Inception (Annex A.8.1): Focuses on spoofing and fake identity input risks.
Design and Development (Annex A.9.1): Linked to tampering threats.
Verification and Validation (Annex A.7.1): Concerns around repudiation, such as lack of model decision logs.
Deployment (Annex A.5.1): Addresses information disclosure vulnerabilities.
Operation and Monitoring (Annex A.10.3): Maps to denial-of-service attacks.
Re-evaluation (Annex A.8.6): Highlights risks of privilege escalation.

AI threat modeling isn’t a one-time task but must be applied continuously across each lifecycle stage, supported by ISO 42001’s annexes and STRIDE categories.

Figure 3: An illustration of how organizations can use ISO/IEC 42001:2023 as a structured framework for AI risk management, using threat modeling as a key technique across the AI lifecycle

AWS tools for AI governance and risk management

AWS governance service capabilities support the controls required in the Statement of Applicability (SoA) under ISO/IEC 42001. These services and features help organizations operationalize responsible AI practices at scale and align with ISO/IEC 42001’s emphasis on structured, accountable AI lifecycle management.

Amazon SageMaker Model Cards: Provides standardized documentation for ML models including purpose, performance, and limitations. In the governance context, model cards help maintain transparency, accountability, and auditability of model behavior and use.
Amazon SageMaker Clarify: Detects bias in datasets and models and supports explainability of predictions. This directly supports governance controls related to fairness, non-discrimination, and explainability.
Amazon SageMaker Ground Truth: Provides high-quality, human-in-the-loop data labeling workflows. It supports data governance by making sure labeled datasets are accurate, consistent, and traceable.
Amazon Bedrock Guardrails: Can be used to define safety filters for generative AI, such as avoiding toxic content or harmful outputs. This facilitates alignment with ethical and content governance policies.
AWS CloudTrail and AWS Config: Enable audit logging and continuous monitoring of system changes. These are essential for accountability, traceability, and compliance reporting within AI governance frameworks.
AWS Identity and Access Management (IAM), AWS Key Management Service (AWS KMS), and AWS PrivateLink: IAM controls access, AWS KMS provides encryption and key management, and PrivateLink enables private connectivity. These features are critical for enforcing access governance, securing data, and maintaining privacy standards.
AWS Generative AI Lens: A part of the AWS Well-Architected Framework tool. It provides structured guidance for evaluating and improving the design of generative AI systems. It helps organizations implement responsible AI practices, manage risks

Conducting AI impact assessments for high risk use cases

While general risk assessments (Clause 6.1 of ISO/IEC 42001) are required for AI systems, ISO/IEC 42001 also calls for AIIAs in situations where the AI system poses high potential impact to individuals, groups, or society. AIIAs should result in a documented report of identified risks associated with the target AI activity, in addition to the severity of potential negative outcomes. These risks should be integrated into the AI management system (AIM) and monitored over time. Several stakeholders and specialists might need to provide input in the assessment process, such as legal, risk, compliance, data management, and security teams. Identified risks should be mitigated where possible, and a determination made about whether the residual risk is acceptable.

AIIAs help answer questions such as:

Is the AI use justifiable, ethical, and proportionate?
Could the system cause discrimination, exclusion, or loss of rights?
What safeguards should be built to protect affected people?

AIIA is required:

If the system makes or informs decisions that materially affect people
If the system is deployed in sensitive domains (such as healthcare, finance, or public services)
If risks to fundamental rights, fairness, or trust are flagged during initial risk assessments

AIAA should cover:

Purpose and scope of the AI system
Stakeholder and impact mapping
Legal, ethical, and social risk evaluation
Transparency and recourse mechanisms
Recommendations for mitigation

AIIA process workflow

Figure 4 that follows illustrates a generic AIIA workflow that includes initiating, scoping, assessing impact, planning mitigation, and documenting the outcome to evaluate how an AI system can affect individuals, groups, and society. Organizations can tailor this process to the AI system context, business objectives, and compliance requirements for their use case.

Figure 4: Sample prescriptive process with key phases on conducting an AIIA

AIIA outcome

AIIA reports should capture the core purpose of the exercise: to evaluate how an AI system might affect individuals, communities, and society at large and to make sure that potential risks are addressed through appropriate mitigation strategies. While formats might vary across industries, an AIIA outcome typically includes key sections such as summary of system purpose, a mapping of affected stakeholders, a contextual analysis of legal and social factors, an evaluation of likely impacts (including fairness, bias, and autonomy risks) and a plan for a mitigation, oversight, and monitoring. Governance details such as sign off responsibility and reassessment triggers should also be included.

Whether you’re starting from scratch or adapting an existing template, these foundational elements will help make sure that your documentation supports transparency, accountability, and ethical AI deployment.

Templates:

Mapping AI lifecycle risks to ISO/IEC 42001 controls

After you have identified risks through techniques such as threat modeling and impact assessments, the next step is to make sure that they’re mitigated through the appropriate ISO/IEC 42001 controls. Using the lifecycle stages defined in ISO/IEC 22989:2022, you can map AI risks identified during the threat hunting process to the corresponding ISO/IEC 42001:2023 clauses and Annex A controls. This mapping helps you align your AI development and governance efforts with a standards-based risk framework.

AI lifecycle stage	Identified risk	Relevant ISO/IEC 42001 clauses	Risk mitigation – Annex A controls
Inception	Spoofing: Impersonation	Clause 4, Clause 5	A.6.1 (Governance roles), A.5.1
Design and development	Tampering: Unauthorized changes	Clause 6.1, Clause 8.2	A.8.2, A.9.1
Verification and validation	Repudiation: No traceability	Clause 8.2	A.8.5, A.7.1
Deployment	Elevation of privilege: Unauthorized model tweaks	Clause 8.2, Clause 9.1	A.10.2, A.6.1
Operation and monitoring	Denial of service: System overload	Clause 9.1, Clause 10.1	A.8.3, A.10.3
Re-evaluation	Drift and new threat vectors	Clause 9.3, Clause 10.2	A.10.2, A.6.4
Retirement	Information disclosure: Residual risks	Clause 8.3, Clause 10.2	A.9.4, A.5.2

Maintaining AI governance

Like most technology risk and governance programs, AI management must be continuously monitored and maintained. ISO 42001 requires an organization to have leadership support and sufficient resources to operate effectively over time. This means that AI governance should be built into every process in the AI development and maintenance journey. AIIAs and threat modeling should be conducted at least annually on existing systems, and prior to the deployment of any new AI function. Policies should be reviewed at least annually and after major change to the AI system. Internal audits should review and monitor compliance with controls continuously, and organizations seeking ISO certification will require annual external audits. Progress toward governance goals and metrics on the status of known AI risks should be reported to the highest level of leadership in a live dashboard, and incidents of negative outcomes related to AI use should be tracked and analyzed to improve the AI system.

Conclusion

Managing AI risk effectively means aligning technical, organizational, and ethical considerations throughout the AI system lifecycle. ISO/IEC 42001 provides structure and accountability. Threat modeling techniques such as STRIDE, MITRE ATLAS, and OWASP for LLM surface deep technical risks. AWS services and features such as SageMaker Model Cards, SageMaker Clarify, and Amazon Bedrock Guardrails help embed governance into layers of AI development.

By combining technical tools, structured assessments, and standards-driven controls, you can build AI systems that are trustworthy, resilient, and aligned with societal expectations.

For additional guidance on achieving, maintaining, and automating compliance in the cloud, contact AWS Security Assurance Services (AWS SAS) or their account team. AWS SAS is a PCI QSAC and HITRUST Assessor Firm that can help by tying together applicable audit standards to AWS service specific features and functionality. They help you build on frameworks such as ISO 42001, PCI DSS, HITRUST CSF, NIST-CSF and Privacy Framework, SOC 2, HIPAA, ISO 27001 and 27701, and more. In addition, AWS Professional Services can also help you plan and map your compliance journey.

Disclaimer: The risk strategies and threat modeling guidance shared in this blog are intended to provide general direction and practical insight into implementing AI risk management under ISO/IEC 42001:2023. However, organizations are responsible for conducting their own context-specific risk assessments, as mandated by the standard. This blog should not be interpreted as an exhaustive approach to or guarantee of compliance with ISO/IEC 42001.

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Mapping AWS security services to MITRE frameworks for threat detection and mitigation

2025-05-13 Pratima Singh

Post Syndicated from Pratima Singh original https://aws.amazon.com/blogs/security/mapping-aws-security-services-to-mitre-frameworks-for-threat-detection-and-mitigation/

In the cloud security landscape, organizations benefit from aligning their controls and practices with industry standard frameworks such as MITRE ATT&CK^®, MITRE Engage^TM, and MITRE D3FEND^TM. MITRE frameworks are structured, openly accessible models that document threat actor behaviors to help organizations improve threat detection and response.

Figure 1: Interaction between the various MITRE frameworks

Figure 1 showcases how the frameworks interact with each other to identify threatening behavior and provide actionable defensive measures. MITRE ATT&CK provides insights into threat actor behavior while D3FEND translates insights from ATT&CK into actionable defensive measures. MITRE Engage uses both ATT&CK and D3FEND to plan proactive engagement strategies that disrupt threat actor activity. As organizations use AWS to enhance their operational capabilities, implementing comprehensive security strategies becomes an important part of cloud adoption.

This blog post explores how AWS security services align with the MITRE frameworks to provide a systematic approach for threat detection and mitigation. We’ll examine how organizations can use AWS security tools such as Amazon GuardDuty, Amazon Security Lake, and AWS Security Hub in conjunction with MITRE frameworks to implement security controls across different stages of their cloud security operations.

Understanding MITRE frameworks

Today’s security teams face increasingly sophisticated threats, with actors continuously evolving their tactics, techniques, and procedures (TTPs). To help organizations strengthen their security posture, industry frameworks such as MITRE ATT&CK, D3FEND, and Engage provide structured methodologies for understanding and responding to these threats.

Understanding these threats through a risk lifecycle approach is crucial for security teams. This structured methodology enables teams to detect anomalies early, map threats to known risk stages, and implement proactive defense mechanisms. By following a risk lifecycle approach, organizations can enhance threat intelligence, improve incident response, and minimize dwell time, ultimately strengthening their security posture against evolving cyber threats.

The integration of MITRE ATT&CK, D3FEND, and Engage frameworks offers organizations a comprehensive approach across the security operations lifecycle. At the foundation, MITRE ATT&CK provides a common language for describing threat actor TTPs. This knowledge base is invaluable during threat modeling and risk assessment, helping teams identify potential vulnerabilities and threat vectors.

Building upon ATT&CK, MITRE D3FEND complements the tactical knowledge with a framework for defensive countermeasures. It suggests proactive security controls, such as implementing least privilege access or securing system configurations. This allows organizations to align their defenses directly with known exploit patterns.

MITRE Engage then adds a layer of active defense capabilities. It guides security teams in planning and implementing strategies that can help in three different ways and potentially simultaneously. Defenders can expose threat actors by detecting them as they attempt to access or operate on infrastructure. Defenders can use Engage to help impose costs by causing threat actors to focus on fake infrastructure rather than legitimate assets. Finally, defenders can set up enticing fake targets to lure threat actors into exploiting them and thereby revealing tradecraft.

A MITRE operation that was run in conjunction with a partner might clarify how this is valuable. MITRE worked with a partner to set up a fake network to appear as a specific type of entity. The goal was to elicit TTPs from a specific advanced persistent threat (APT) for which MITRE and the partner had a recent malware sample. MITRE ran the sample on the fake network and observed the APT’s activities. From that operation, MITRE gathered a list of specific TTPs that were executed by a script in a particular order that helped the partner develop a novel analytic. Plus, in reviewing event traces, MITRE found a flaw in a well-known security tool that missed a specific type of process-tampering event. This was disclosed to the vendor, who fixed that in later versions. Finally, every minute of operating in this environment imposed a cost on the APT by diverting resources from real victims. Full details of the exercise were presented at Shmoocon 2022.

As we move through the security operations lifecycle, these three MITRE frameworks continue to work in concert:

During detection and monitoring, ATT&CK informs threat hunting and log analysis and correlation, D3FEND strengthens real-time detection and anomaly tracking, and Engage enables strategic detection through deception techniques.
When responding to incidents, ATT&CK helps map incident progression, D3FEND automates response actions, and Engage provides methods to gather additional intelligence about threat activities.
In the post-incident phase, ATT&CK helps map the incident chain for better detection tuning, D3FEND refines security controls, and Engage expands deception tactics based on lessons learned. By integrating these efforts, organizations can implement a systematic approach to security operations that combines tactical knowledge, defensive measures, and strategic engagement capabilities.

Aligning AWS to MITRE frameworks

AWS offers a broad set of cloud services with high security at global scale, and has proven experience helping businesses innovate faster. Customers use AWS services in various configurations to build solutions for their bespoke business needs. A fundamental aspect of using AWS is understanding the Shared Responsibility Model, shown in Figure 2 that follows.

Figure 2: AWS Shared Responsibility Model

AWS is responsible for security of the cloud, while customers are responsible for security in the cloud. This means that AWS is responsible for protecting the infrastructure that runs the services offered in the AWS Cloud, while customer responsibility is determined by the AWS Cloud services that a customer selects. As customers embark on their cloud security journey, we help them understand two important concepts of cloud-scale environments:

Interconnected resources and configurations: Cloud architectures consist of interconnected entities—ranging from virtual machines using Amazon Elastic Compute Cloud (Amazon EC2) to serverless functions using AWS Lambda. To help customers maintain visibility and control, AWS offers native tools designed for cloud-scale management.
Dynamic access management and least privilege: Cloud environments require robust authentication mechanisms and fine-grained permissions. AWS provides comprehensive identity and access management tools to implement least privilege access and manage dynamic workloads effectively.

To support our customers’ security needs, AWS offers native security services that align with industry-standard frameworks like MITRE ATT&CK, D3FEND, and Engage. Here’s how these services map across the security lifecycle:

For threat modeling and risk assessment, Security Lake aggregates logs for MITRE ATT&CK-based analytics, while Amazon Inspector scans for vulnerabilities mapped to threat actor techniques. Amazon Macie detects sensitive data exposure across AWS resources.

When implementing preventive controls, implementing least privilege for access is fundamental. AWS Identity and Access Management (IAM) and AWS Organizations provide capabilities to enforce least privilege across your AWS environment. You can use IAM permissions and service control policies (SCPs) to build an identity perimeter. AWS Web Application Firewall (AWS WAF) provides application-layer protections, while you can use AWS Secrets Manager to store honey tokens. Secrets Manager is an AWS service that you can use to centrally manage the lifecycle of secrets. Honey tokens act as digital decoys that simulate legitimate credentials or sensitive data, enticing threat actors to reveal their presence when they interact with them. When triggered, these tokens generate real-time alerts and detailed event logs, enabling swift investigation and deeper insights into threat actor tactics. Deploying honey tokens on AWS involves creating decoy credentials or sensitive data entries that serve no legitimate purpose yet are closely monitored for unauthorized access attempts. One common approach is to use Secrets Manager to store fake secrets that mimic real credentials. When such tokens, stored in Secrets Manager, are accessed, the service generates detailed event logs with AWS CloudTrail and Amazon CloudWatch. You can continuously monitor these logs and events and configure them to alert you if the decoys are ever accessed.

During the detection and monitoring phase, GuardDuty identifies unusual activity patterns across your AWS accounts and workloads, Amazon Detective helps investigate these anomalies by analyzing root causes and plotting out the incident scope in an interactive way, while Security Hub centralizes security alerts and enables automated responses across your environment.

For incident response, containment, and recovery, Lambda and Step Functions help automate responses when security events occur. AWS Shield and WAF work together to provide real-time threat mitigation against denial-of-service type threats like distributed denial of service (DDoS), while Security Lake and Detective provide the necessary data and tools for conducting thorough forensic analysis. In 2024, AWS announced the AWS Security Incident Response service that uses automated monitoring and investigation through the AWS Customer Incident Response Team to prepare for, respond to, and recover from security events. You can use the service to augment your cloud-based security response function aligned with AWS security best practices.

By blocking malicious traffic, Shield and WAF provide real-time DDoS mitigation. AWS deception tactics could include redirecting threat actors to honeypots or deploying decoy Amazon Simple Storage Service (Amazon S3) files to enhance engagement strategies, like the honey token deployment and storage using Secrets Manager explained earlier in this post. Post incident, Security Lake and Detective assist in forensic analysis, while Security Hub and IAM policies refine security controls based on past exploit trends. MITRE Engage tactics can further evolve by analyzing honeypot interactions. By integrating these AWS security services, you can detect, prevent, and deceive threat actors effectively, strengthening your organization’s overall security posture. The following table maps MITRE lifecycle stages to AWS services and tools.

Lifecycle stage	AWS tools for MITRE ATT&CK (detect and map)	AWS tools for MITRE D3FEND (prevent and contain)	AWS tools for MITRE Engage (deceive and disrupt)
Threat modeling and risk assessment	Security Lake, Amazon Inspector, Macie, and Security Hub	IAM policies and AWS WAF	Secrets Manager and honey tokens
Detection and monitoring	GuardDuty, CloudTrail, and Security Hub	Detective, auto-remediation using AWS services such as Amazon EventBridge, Lambda, and Step Functions.	Fake IAM users, and decoy Amazon S3 files
Incident response and containment	Step Functions, Lambda, GuardDuty, AWS Security Incident Response, and Detective	Auto-block using AWS WAF, multi-factor authentication (MFA) enforcement, and AWS Security Incident Response	Redirect exploits to honeypots
Post-incident and intelligence	Analyze and correlate logs with Security Lake, Amazon Athena, and Detective	IAM hardening and AWS Config	Adaptive deception traps

You can use Table 1 as a guide to understand how AWS services map to the various lifecycle stages in the incident response lifecycle. We will now demonstrate how GuardDuty, an AWS security service that continuously monitors your AWS accounts and workloads to provide automated threat detection, works in line with the MITRE ATT&CK framework.

GuardDuty: MITRE framework integration in action

In 2024, AWS worked extensively with MITRE to create new techniques and sub-techniques, and to update some of the existing detection objects in the MITRE ATT&CK cloud matrix. The work that AWS did with MITRE drew from real-world threat actor techniques performed against AWS customers and helped to provide more detailed information and specific detections on how threat actors abuse AWS services. For example, AWS threat detection teams observed a new tactic in the cloud environment (T1485.001 | Data Destruction: Lifecycle-Triggered Deletion) where threat actors could modify lifecycle policies for S3 buckets to delete all objects stored in the bucket. This technique, along with associated mitigations, detection, and references was submitted back to the MITRE ATT&CK framework.

AWS security services such as AWS Security Incident Response and GuardDuty use MITRE ATT&CK to provide threat intelligence and detailed information on threats identified in an AWS account. You can examine how these AWS security services integrate with MITRE ATT&CK through a specific example. GuardDuty Extended Threat Detection helps customers with contextual threat detection in their AWS environment and aligns the signals with the MITRE ATT&CK lifecycle. GuardDuty automatically detects and correlates individual findings with connected resources to produce an attack sequence finding. Consider an attack sequence finding generated by GuardDuty detecting data compromise in your AWS account. We will use this as an example in this post.

To begin, the finding summary includes a textual description of the sequence of events and the TTPs detected, as shown in Figure 3. It also shows a summary of the observed TTP identifiers, AWS API calls, and IP addresses.

Figure 3: GuardDuty finding summary visible in the service console

As seen in Figure 4, every attack sequence finding highlights the signals and the MITRE tactic associated with the activity. The finding shown in Figure 4 shows the full lifecycle of the threat from discovery to impact.

Figure 4: Signals and MITRE tactics alignment

Diving deeper into each signal reveals the specific MITRE tactic associated with the activity and the technique identifier. Another interesting feature is that you can see the correlation between the AWS API call associated with the resources involved in the attack sequence and the user agent.

Figure 5 shows one of the signals associated with the attack sequence in the previous finding. A data exfiltration activity has been reported because of the nature of the AWS API call (s3:GetObject) and the user agent (Kali Linux) that was used to perform the activity. The level of detail for each signal is contextual based on the type of activity and tactic.

Figure 5: Details for a single signal within a GuardDuty attack sequence finding

Figure 6 shows another signal from the same finding, but in this case the level of detail includes the malicious IP lists and suspicious network activity detected in relation to the signal and associated resources.

Figure 6: Details of TTPs associated with an indicator within a GuardDuty attack sequence finding

This information can be downloaded in a JSON-formatted file. The information from the JSON document can be used to automate responses and remediations for the detections.

Conclusion

AWS security services work together to support the implementation of MITRE frameworks—ATT&CK for threat detection, D3FEND for preventative security, and Engage for threat actor engagement across the cybersecurity lifecycle. As demonstrated through the GuardDuty Extended Threat Detection example, these integrations provide customers with practical, actionable security capabilities across their AWS environment. The alignment of AWS security services with MITRE frameworks helps you build security operations using industry-standard methodologies, implement automated detection and response capabilities, maintain visibility across your AWS environment, and continuously enhance your security controls.

Through this integration of AWS security services with MITRE frameworks, you can implement comprehensive security operations that evolve with your organization’s business needs. To get started, visit the GuardDuty console to enable Extended Threat Detection, and explore our documentation to learn more about implementing these security capabilities in your AWS environment. Join us at AWS re:Inforce 2025 to learn more about AWS security services, including deep dives into the integration of Amazon GuardDuty with MITRE frameworks and hands-on workshops with AWS security experts.

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Monitoring and optimizing the cost of the unused access analyzer in IAM Access Analyzer

2025-05-12 Oscar Diaz

Post Syndicated from Oscar Diaz original https://aws.amazon.com/blogs/security/monitoring-and-optimizing-the-cost-of-the-unused-access-analyzer-in-iam-access-analyzer/

AWS Identity and Access Management (IAM) Access Analyzer is a feature that you can use to identify resources in your AWS organization and accounts that are shared with external entities and to identify unused access. In this post, we explore how the unused access analyzer in IAM Access Analyzer works, dive into the cost implications, and share practical approaches to manage and optimize how you use it with a primary focus on cost optimization.

Note: While security best practices for managing AWS Identity and Access Management (IAM) resources are critical, this post emphasizes cost-saving strategies rather than detailed security guidance. We don’t cover step-by-step implementation details for the recommendations here; instead, we provide links to resources that you can use as guides for the process.

Understanding the unused access analyzer in IAM Access Analyzer

IAM Access Analyzer has two capabilities to generate findings:

External access analysis (no additional charge): Identifies resources shared with external entities. It requires one analyzer per AWS Region where you have resources.
Unused access analysis (paid): Detects unused roles, access keys, and permissions. It requires only one analyzer per AWS account and analyzes IAM roles and users across Regions from a single analyzer.

Both external access analysis and unused access analysis support AWS Organizations and you can create a single analyzer per organization (in the case of external access analysis, per organization per Region).

IAM Access Analyzer unused access analysis costs $0.20 per IAM role or user analyzed each month. The charges for existing roles and users happen at the beginning of the month. As new roles and users are added throughout the month, they are analyzed and charged at a rate of $0.20 per role or user. To help avoid duplicate charges, create only one unused access analyzer per account if using an account-level analyzer, or one unused access analyzer for the entire organization if using an organizational-level analyzer. You should avoid deleting and recreating an analyzer. If you recreate an analyzer, you will be charged again for the analysis.

Reviewing and optimizing your usage

Before taking any actions to reduce costs, it’s crucial to understand your current usage. You can use the AWS Cost and Usage Report (AWS CUR) to identify how many unused access analyzers you have in your environment. To learn more, see Querying Cost and Usage Reports using Amazon Athena.

Use the following Athena query on your CUR data to identify the unused access analyzers within your organization. Replace <CUR_TABLE> with the name of your CUR table.

SELECT
line_item_usage_type,
product_region,
line_item_resource_id,
bill_payer_account_id,
line_item_usage_account_id,
SUM(line_item_unblended_cost)
FROM <CUR_TABLE>
WHERE line_item_product_code = 'AWSIAMAccessAnalyzer'
AND line_item_line_item_type = 'Usage'
GROUP BY
line_item_usage_type,
product_region,
line_item_resource_id,
bill_payer_account_id,
line_item_usage_account_id

This query will give you a comprehensive view of your IAM Access Analyzer usage across your organization, including the cost per analyzer.

Now, let’s walk through four things that you can do today to optimize your IAM Access Analyzer unused access analysis costs.

Consolidate unused analyzers

Review your AWS CUR analysis results to identify opportunities for consolidation. If you’re using an organizational unused access analyzer, you should use a single analyzer. If you’re using an unused access analyzer per account, make sure a single account doesn’t have more than one analyzer.

Use tags to exclude some roles or users

Consider using tags to exclude certain roles or users from analysis. This approach can help scope your analysis and reduce costs by avoiding roles and users that you don’t want to analyze. To do this, you’ll need to implement a tagging strategy for your IAM roles and users, identifying principals that might not require regular access analysis. Then, when creating or modifying an analyzer, use exclusion to skip analysis of tagged IAM roles and users. Regularly review your exclusion strategy to validate that it aligns with your organization’s security policies and compliance requirements.

For a deeper dive into this process, including step-by-step guidance and practical examples, see Customize the scope of IAM Access Analyzer unused access analysis.

Regular clean-up of IAM roles and users

Periodically review and remove unnecessary IAM roles and users. Because IAM Access Analyzer unused access analysis charges are based on the number of roles and users analyzed, removing unused roles and users will help reduce unused access findings cost. This is also a security best practice for IAM.

Monitor and adjust

Set up AWS Budgets or AWS Cost Anomaly Detection to track your IAM Access Analyzer unused access analysis costs. Create alerts for when costs exceed expected thresholds. By using the proactive approach, you can quickly identify and address unexpected cost increases.

Conclusion

IAM Access Analyzer is a valuable tool for improving your organization’s security posture by detecting unused IAM roles, unused access keys for IAM users, unused passwords for IAM users, and unused services and actions for active IAM roles and users. You can then act based on those findings and support your effort to achieve least privilege access. By understanding the billing model and implementing these cost optimization strategies, you can maximize benefits while keeping costs under control. Remember, cost optimization is an ongoing process. Regularly review your usage and adjust your strategy as your needs evolve.

To learn more about IAM Access Analyzer and its pricing, see Getting started with AWS Identity and Access Management Access Analyzer. We’re here to help you optimize your AWS environment, so reach out to AWS Support and your AWS account team if you need further assistance.

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