Cloud expansion, SaaS sprawl, identity complexity, and shadow IT are continuously reshaping organizational risk. For security leaders, visibility isn’t the challenge anymore, but actually operationalizing that visibility is.
Surface Command was built to unify asset and identity intelligence across your external attack surface. But translating that intelligence into executive-ready dashboards or operational reporting has often required knowledge of Cypher queries.
Today, that changes: We’re introducing filter-based dashboard widgets in Surface Command, enabling teams to build meaningful attack surface management (ASM) dashboards in minutes, without writing a single query.
And for CISOs focused on advancing continuous threat exposure management (CTEM), this is more than a usability enhancement. It’s an operational accelerator.
From filters to dashboards, instantly
Security teams already use saved asset and identity filters to answer critical questions:
Which internet-facing assets are high risk?
Where do privileged identities intersect with exploitable exposures?
Which business units own unmanaged cloud infrastructure?
What third-party SaaS applications expand our attack surface?
Now, those same saved filters can be converted directly into live dashboard widgets. If your team can build a filter table, they can now build a dashboard.
There’s no need to understand query syntax or rely on specialized expertise for common reporting needs. With just a few clicks, exposure views become shareable, persistent dashboards built on the same unified data model that powers Surface Command.
Figure 1: Creating dashboard “widgets” in the Rapid7 Command Platform
Reducing friction in exposure reporting
For many organizations, the barrier to effective exposure management isn’t visibility, it’s friction. When dashboard creation requires query expertise, reporting slows down, operational teams depend on a small group of power users, executive visibility lags behind exposure reality, and CTEM initiatives stall under complexity.
Filter-based widgets remove that bottleneck. Security teams can now spin up exposure dashboards in minutes, empower analysts and vulnerability teams to self-serve, deliver consistent reporting to leadership, and standardize exposure views across business units.
This lowers the barrier to building and maintaining exposure intelligence across the organization, and that matters when “continuous” is the goal.
A practical enabler for continuous threat exposure management (CTEM)
Beyond a framework, CTEM is a discipline. One that treats exposure management as an ongoing cycle, not a point-in-time project. CTEM is commonly organized into five continuous steps:
Scope – Define what you’re focusing on (systems, business services, exposure themes, time horizons).
Discover – Identify the assets, identities, and exposures within scope.
Prioritize – Determine what matters most based on risk and impact.
Validate – Confirm exploitability and real-world likelihood.
Mobilize – Drive remediation and measure progress.
The challenge isn’t describing these steps. It’s making them repeatable in day-to-day operations, and that’s where filter-based dashboard widgets help.
Making “scope” real, not a slide deck
CTEM often succeeds or fails at the first step: scope. If “scope” lives in a document, teams interpret it differently. If it lives on the platform, it becomes operational.
Saved filters are an effective way to define scope in a way teams can actually use. Let’s take a look at some examples:
“Internet-facing assets owned by customer-facing business units”
With filter-based widgets, you can turn those scoped views into dashboards that make CTEM focus areas visible and persistent. This helps teams stay aligned on what you’re measuring and why.
Operationalizing discovery and prioritization
Once scope is defined, CTEM demands continuous discovery and prioritization. Filter-based widgets support that by making key exposure views always available, such as:
Newly discovered external assets in a critical business unit
High-risk exposures on internet-facing systems
Identity-driven exposure hotspots (where access and exposure intersect)
Business-unit risk breakdowns for ownership and accountability
Instead of rebuilding reports each cycle, teams can use dashboards to maintain ongoing awareness of what has changed.
Supporting validation and mobilization with “always-on” views
Validation and mobilization are where CTEM becomes measurable. While advanced workflows still benefit from deeper investigation and custom analysis, filter-based dashboards help teams maintain consistent operational pressure: Are the highest priority exposures shrinking week over week? Are the same teams repeatedly accumulating unmanaged assets? Are privileged identity risks trending in the right direction?
Dashboards don’t replace validation, but they make it easier to target validation where it matters, and to keep remediation efforts aligned to the scoped CTEM goals.
Built on the Command Platform: unified data, real-time context
These filter-based widgets aren’t layered on top of a separate reporting engine. They’re instead powered directly by the Command Platform’s unified asset and identity graph, which is the same continuously updated data model that drives Surface Command.
That means widgets reflect real-time exposure state, asset and identity relationships stay connected, context holds across domains, and dashboards scale as your attack surface evolves.
For CISOs, this is what turns reporting into decision support: consistent data, consistent definitions, and visibility that doesn’t lag behind reality.
Accessibility without sacrificing power
Most reporting can now be built from easy-to-use filter tables, without the learning curve associated with Cypher.
For advanced correlation, custom logic, and complex investigations, teams can still leverage custom queries. The result is balance: Accessibility for most users and flexibility for advanced practitioners – all via one unified platform.
Turning exposure intelligence into executive clarity
Surface Command was built to give organizations a unified view of their external attack surfaces across assets, identities, and exposures.
With filter-based dashboard widgets, that intelligence becomes easier to operationalize, easier to share, and easier to scale, especially for CTEM programs that rely on repeatability.
Because continuous threat exposure management shouldn’t depend on who knows how to write a query. It should be built into the way your platform works.
Cloud and AI are transforming industries and societies at unprecedented speed, from accelerating research and enhancing customer experiences to optimizing business processes and enriching public services. At Amazon Web Services (AWS), we believe that for the cloud and AI to reach their full potential, customers need control over their data and choices for how and where they run their workloads. In 2022, we formalized our commitment to control and choice—offering all AWS customers the most advanced set of sovereignty controls and features available in the cloud with the AWS Digital Sovereignty Pledge. As AI adoption accelerated, we’ve been working with customers to help them embrace AI innovation while meeting sovereignty requirements. We’re committed to ensuring customers can continue to harness AI’s transformative capabilities without compromising on the capabilities, performance, innovation, security, and scale of the AWS Cloud to meet their sovereignty needs, including AI sovereignty. Our approach to AI sovereignty is grounded in a deep understanding of these needs and the real-world implementation challenges that come with them.
Through discussions with customers, partners, analysts, and regulators, we’ve learned that digital sovereignty—and AI sovereignty—means different things to different stakeholders. Each country and region has unique, evolving sovereignty requirements, with no uniform guidance on which workloads or sectors must comply. Despite this variation, we’ve identified consistent themes: data sovereignty (including data residency and operator access restrictions) and operational sovereignty (including resilience, survivability, and independence). AI sovereignty builds on these foundations, adding emerging considerations such as preserving cultural norms, values, and local languages in AI outputs. Ultimately, meeting digital and AI sovereignty requirements comes down to providing customers with more control and choice.
Enabling customer control and choice across the AI stack
AI sovereignty requires control and choice across the AI stack—comprehensive cloud infrastructure that combines compute, networking, data management, security controls, specialized application services, and talent. This includes the ability to make deliberate choices across the stack such as location, dependencies, services, and partners that align with customers’ unique needs, regulatory requirements, and innovation objectives. With AWS, customers can develop AI on a trusted foundation where their data remains secure and under their control. Customers have the freedom to choose from a comprehensive range of AI optimized chips—including purpose-built AWS silicon and chips from NVIDIA, AMD, and Intel—so they can select the right chip for the right workload. AWS applies two decades of learned expertise to our comprehensive AI stack, enabling organizations to maintain complete control over their data and operations while accessing cutting-edge capabilities to solve local challenges.
AWS provides customers with the infrastructure and tools to embed AI across the full value chain—not just in isolated use cases, but as a foundational capability enabling them to train and deploy models and build sophisticated AI and generative AI applications with exceptional performance. This enables customers to focus on innovation instead of their infrastructure, bringing the cloud to where they need it most with a range of options including AWS AI Factories, AWS Outposts, AWS Local Zones, AWS Dedicated Local Zones, and AWS Regions including the AWS European Sovereign Cloud. For example, customers who require dedicated deployments to meet their sovereignty requirements for their mission-critical AI workloads can use AWS AI Factories. These physically isolated, dedicated deployments built exclusively for the customer combine the latest AI infrastructure, including AWS Trainium accelerators, NVIDIA GPUs, dedicated networking, and storage. AWS AI Factories address AI sovereignty needs by delivering on-premises AI capabilities to securely perform training, fine tuning and real-time inference.
The AWS AI portfolio offers a comprehensive range of services—from foundation models (FMs) through Amazon Bedrock, to machine learning offerings like Amazon SageMaker, application services like Amazon Q, and developer tools like Kiro—designed to give customers control over their data and choice in how they deploy AI. With Amazon Bedrock, customers can choose from hundreds of models from leading providers like AI21 Labs, Anthropic, Amazon, Cohere, Mistral AI, and OpenAI. Customers can evaluate and select the most suitable FMs for their specific needs and choose where they deploy them, and fine-tune models privately with their own data. Customers are always in control of their data. Critically, no customer inputs to or outputs from Amazon Bedrock are used to train Amazon Nova or any third-party models.
Supporting national AI strategies
Successful AI strategies require building a holistic environment nurturing local talent, supporting startups, developing industry-specific applications, and fostering public-private partnerships. The cloud has transformed AI from an exclusive technology requiring massive investment into an accessible tool for innovation across all sectors and organization sizes. While technical infrastructure gets much of the attention when considering AI sovereignty, the cultural and strategic dimensions of national FMs are equally critical. These FMs aren’t merely computational tools, they can encode elements of cultural knowledge, linguistic nuance, and societal context, making local relevance a design consideration rather than an afterthought. These FMs serve purposes that extend beyond technical capabilities. Locally trained FMs can reflect national educational curricula and cultural values while understanding local legal systems, business practices, and regulatory frameworks. Models trained on local languages, dialects, and cultural contexts support linguistic diversity and help underrepresented languages gain representation in AI products and services.
AWS supports vital national priorities and customers’ missions, such as the preservation of culture norms, values, and local languages development of regional and local language model capabilities. To customize models, customers can use Amazon SageMaker AI for voice, domain specialization, and to evaluate models for accuracy. For example, the first Greek LLM made available in March 2024 was Meltemi—built on top of Mistral-7B, running on AWS infrastructure, and continually pretrained to extend its proficiency in the Greek language using a dataset of 28.5 billion Greek tokens. Meltemi is available on HuggingFace. SEA-LION—a family of open source, multilingual LLMs for Southeast Asia—was trained entirely on AWS with managed GPU clusters. Their team completed a 3B-parameter model in only 3 months—a 60% faster timeline than comparable on-premises projects.
Verifiable control over data access
Sovereignty isn’t only about where data resides—it’s about who can access it and under what conditions. In the AI context, access restriction extends beyond infrastructure to cover model inputs, outputs, training processes, and the operational environments in which AI runs. Unlike traditional infrastructure, AI workloads introduce new access surfaces: the model itself, the data used to train it, and the inference pipeline through which sensitive inputs flow. This furthers the need for verifiable governance and identity propagation in IT systems.
To help ensure the confidentiality and integrity of customer data, all modern Amazon Elastic Compute Cloud (Amazon EC2) instances including those that offer AI accelerators, such as AWS Inferentia and AWS Trainium, are backed by the industry-leading security capabilities of the AWS Nitro System. By design, there is no mechanism for anyone at AWS to access customer data on Nitro EC2 instances that customers use to run their workloads. AWS services—including those with AI capabilities built on Amazon EC2—inherit these same protections. These protections apply to AI data running in the AWS Nitro System so that they’re protected at every stage—from model training to inference. The NCC Group, an independent cybersecurity firm, has validated the design of the Nitro System. We believe providing this level of transparency is critical in building and sustaining trust.
As AI agents increasingly take actions across systems on behalf of users, controlling who and what can access resources—and ensuring appropriate human oversight—becomes critical. AWS Identity and Access Management (IAM) helps ensure that only authorized users and applications can access AI resources through fine-grained permissions and comprehensive audit trails. For AI agents and automated workloads, Amazon Bedrock AgentCore Identity provides identity and credential management, so agents operate with the right permissions and nothing more.
Transparency and assurance
Transparency is at the core of our digital sovereignty commitment. We provide comprehensive industry-leading technical measures, operational controls, and contract protections that give customers control over where they locate their data, who can access it, and how it’s used. To give greater assurance on how AWS services are designed and operated, we continue to seek out and secure third-party attestations, accreditations, and certifications that help our customers meet their compliance needs.
We continue to deepen our assurances and transparency to customers—such as updating our AWS Service Terms to reflect our technical protections commitments (e.g. AWS Nitro System), providing detailed commitments as to our handling of thirid-party requests for customer data in our agreements, and providing supplemental explanations and resources (e.g. CLOUD Act blog) to empower customers to make informed choices on sovereignty matters. These efforts extend into our commitment to responsible AI, providing customers the confidence to build and operate AI applications responsibly using AWS Services. ISO/IEC 42001 is an international management system standard that outlines requirements and controls for organizations to promote the responsible development and use of AI systems. AWS is the first major cloud service provider to achieve ISO/IEC 42001 accredited certification for AI services, covering Amazon Bedrock, Amazon Q Business, Amazon Textract, and Amazon Transcribe. In November 2025, AWS successfully completed its first surveillance audit for ISO 42001:2023 with no findings, reiterating the continual commitment of AWS to responsible AI practices.
Innovative technology requires a secure and trustworthy foundation. AWS supports more than 140 security standards and compliance certifications that our customers and partners can inherit to help comply with local laws and regulations. For two decades, we’ve deeply engaged with regulators and cybersecurity authorities to align our offerings with national priorities and ensure our solutions support both innovation and control. We actively contribute to frameworks that respond to new developments without stifling progress.
Sustained commitment to helping customers achieve their sovereignty goals
AWS is committed to giving customers the same control and choice over their AI systems as they have over their data. We help customers harness AI’s transformative power while maintaining the capabilities, performance, innovation, security, and scale of AWS Cloud. As cloud and AI evolve, AWS will continue offering the most advanced sovereignty controls and features available.
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Cloud environments have changed how security teams detect and respond to threats. Signals come from more places, identities are harder to track, and attacks rarely stay within a single system. For many teams, the challenge is no longer visibility. It is having the risk context to understand what matters and act on it quickly. This shift is reflected in the conversations shaping this year’s Rapid7 Global Cybersecurity Summit.
Taking place May 12-13, the summit explores how detection and response are evolving across cloud, identity, and endpoint environments. The focus is practical: how attacks actually unfold, how teams respond under pressure, and how detection strategies need to adapt.
Detection is no longer just about coverage
One of the clearest themes across the agenda is that traditional detection models are struggling to keep pace with attackers. Environments are more dynamic, and attackers are more targeted. Catching everything is no longer realistic, and in many cases it is not useful.
Sessions like The New Rules of Detection Engineering will examine this shift in detail. The focus moves away from volume and toward precision. It will ask questions like: What makes a detection meaningful? How should teams prioritize signals? And how can detection strategies support real outcomes rather than just generate alerts? This is especially important in cloud environments, where context changes quickly and signals are often incomplete.
Understanding how attacks actually unfold
To improve detection, teams need to understand how attacks behave in practice. Several sessions across the summit focus on this directly.
The Reality of Running a SOC in 2026 will explore how modern attacks begin — from identity misuse to cloud misconfigurations— and how they evolve over time. Rather than following a predictable path, attacks move across systems, taking advantage of gaps in visibility and delayed decisions.
This theme continues in sessions like Inside the Modern SOC, where attendees follow a real investigation from first alert to outcome. These walkthroughs show how signals are correlated across environments and how decisions are made when time and clarity are limited.
From exposure to runtime risk
Cloud security also requires a closer connection between exposure and detection. In many cases, incidents begin long before an alert is triggered.
Sessions such as From Cloud Exposure to Runtime Attack explore how misconfigurations, permissions, and overlooked risks lead to active threats. The focus is on how teams connect exposure insights with runtime behavior to improve prioritization and respond earlier in the attack lifecycle.
This is a practical shift. Detection is no longer a separate function but part of a broader process that starts with understanding exposure and continues through to response.
What this means for security teams
Across these sessions, a consistent message emerges: Detection strategies need to be grounded in how environments actually behave, not how they are expected to behave.
This means focusing on signal quality rather than volume, connecting data across cloud, identity, and endpoint, and building workflows that support faster decisions. It also means accepting that not all alerts have equal weight, and that prioritization is a core part of modern detection.
A preview of what’s to come
Cloud detection is just one part of a broader shift happening across the summit. Sessions on MDR, AI, and exposure management all connect back to the same idea. Security operations must move earlier, reduce noise, and act with greater confidence.
If you are rethinking how your team detects and responds to threats in cloud and hybrid environments, this is where those conversations come together.
Join us May 12–13 and see how security teams are evolving their detection strategies for 2026.
If you’re a security leader operating in Germany, Austria, or Switzerland, you already know that compliance isn’t a checkbox. It’s a competitive differentiator. Rapid7 has completed BSI C5 Type 2 attestation for the Rapid7 Command Platform, including Threat Command, and it’s a milestone worth unpacking.
This isn’t just a badge on a webpage. It’s proof that our security controls work, not just on paper, but in practice, over time.
What is BSI C5 and why does it matter?
The Cloud Computing Compliance Criteria Catalogue (C5) was developed by Germany’s Federal Office for Information Security (BSI). It sets some of the most rigorous cloud security standards in the world, covering everything from data protection to operational transparency.
A Type 2 attestation is the gold standard within that framework. Unlike a point-in-time audit, Type 2 validates that security controls aren’t just well-designed, but that they’re actively working consistently over a sustained period. It’s the difference between a security promise and a security proof.
For organizations in the DACH region, C5 is more than a nice-to-have. It’s a procurement requirement for German federal agencies, critical infrastructure operators, healthcare institutions, and financial services firms. If you’re operating in any of these sectors, your cloud providers need to meet this bar. Rapid7 now does.
BSI C5 Type 2 and your cloud security strategy
Whether you’re evaluating security vendors, managing compliance obligations, or looking to strengthen your organization’s risk posture, the question is the same: How do you know your cloud security provider actually does what it says?
BSI C5 Type 2 attestation answers that question. It’s independent, rigorous, and sustained over time. While rooted in German regulatory requirements, C5 is increasingly recognized as a benchmark for secure cloud operations across Europe. It’s one of the clearest signals that a cloud provider has the operational maturity to handle sensitive environments.
The Rapid7 Command Platform unifies exposure management with detection and response, giving security teams clear visibility across their attack surface. Threat Command extends that protection further, identifying and helping remediate threats across the clear, deep, and dark web. Both are now independently validated against one of the world’s toughest cloud security frameworks.
Why independent validation of security controls matters
Trusting a security vendor shouldn’t require a leap of faith. Independent validation exists so you have the evidence to make that call with confidence. This attestation reflects our continued investment in meeting the highest security standards for customers across Germany and the wider European market. Rapid7 has achieved a milestone that speaks directly to the conversations had every day with public sector and enterprise organizations who need more than a promise.
They need proof that a security provider’s controls have been tested, verified, and proven to hold up over time. That’s the kind of assurance that matters when the stakes are high.
Ready to see the Command Platform in action? Visit Rapid7.com for a free trial.
We are excited to share Rapid7’s recognition in The Forrester Wave™: Cloud Native Application Protection Solutions (CNAPP), Q1 2026 [1]. We see this acknowledgment as a milestone that highlights our strategic evolution and continued drive to help security teams shift from reactive defense to proactive, preemptive response.
Threat actors today know that organizations with static, moment-in-time snapshots of their environments struggle to identify misconfigurations, overprivileged identities, and vulnerabilities in cloud environments. It’s why effective cloud security has shifted from isolated tools that lock down a single container or run a standalone scanner, to platforms that are an integral part of a broader continuous exposure management and threat detection and response (TDR) strategy.
Leading with outstanding integration
As noted, one of the most critical challenges for modern security teams is protecting their technology stacks with fragmented security tools. That’s why integrations are so important. The Forrester report states: “Rapid7’s outstanding third-party solution integration includes asset management, third-party solutions, bidirectional integration with ticketing systems, SIEM integration, and SOAR and ASPM tool integrations.”
To us, this recognition reflects our belief that by integrating deeply with remediation workflows, whether it’s automated ticketing or advanced application security posture management (ASPM), we eliminate the silos that prevent cloud security from becoming a seamless part of an organization’s security operations.
Cloud security does not live in a vacuum; security leaders need to understand the potential impact of cloud or container vulnerabilities and misconfigurations within the wider business and cybersecurity program. Security operations teams need cloud alerts with the relevant context delivered to their tools and workflows. This is why bidirectional integrations and automation are critical in modern security platforms.
From intelligence to proactive remediation
Forrester’s evaluation notes: “[Rapid7’s] solid innovation focuses on delivering a unified CNAPP [platform] that helps users protect cloud workloads using temporal intelligence and trending.”
We believe this finding underscores our ability to arm security teams with threat and business-centric context. We show how exposures and misconfigurations evolve over time. This empowers organizations to go beyond static snapshots of risk to achieve more proactive and effective remediation. At the core of this capability is our ability to deliver customers an expansive, continuous view of their attack surfaces. Whether an organization monitors their environment with Rapid7 or they utilize third-party scanners, our Command Platform ingests these findings and translates them into actionable remediation plans that set the foundation for automated mobilization.
Rapid7 delivers new cloud innovations
Since our participation in Forrester’s evaluation process for this report, we have continued to introduce several important new features and innovations. These updates support our customers’ cloud security requirements. In January 2026, we announced a strategic partnership with ARMO, the creators of Kubescape, to integrate runtime cloud and application security into the Rapid7 Exposure Command Platform.
Security teams are tired of seeing attacks only after the damage is done. By integrating ARMO’s continuous kernel-level observability (eBPF) into our platform, teams now have visibility into cloud behavior, enabling them to differentiate normal cloud activity from legitimate threats at runtime. They can then automatically terminate malicious processes or pause compromised containers to prevent lateral movement.
With Rapid7 cloud security, organizations can shift from seeing ‘potential’ risk to mitigating ‘active’ threats, fully completing the loop between preemptive security and proactive response.
[1] The Forrester Wave™: Cloud Native Application Protection Solutions (CNAPP), Q1 2026, Forrester Research, Inc., February 17, 2026.
Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here .
At Rapid7, we track a wide range of threats targeting cloud environments, where a frequent objective is hijacking victim infrastructure to host phishing or spam campaigns. Beyond the obvious security risks, this approach allows threat actors to offload their operational costs onto the target company, often resulting in significant, unwanted bills for services the victim never intended to use.
Rapid7 recently investigated a cloud abuse incident in which threat actors leveraged compromised AWS credentials to deploy phishing and spam infrastructure using AWS WorkMail, bypassing the anti-abuse controls normally enforced by AWS Simple Email Service (SES). AWS SES is a general-purpose, API-driven email platform intended for application-generated email such as transactional notifications and marketing messages. This allows the threat actor to leverage Amazon’s high sender reputation to masquerade as a valid business entity, with the ability to send email directly from victim-owned AWS infrastructure. Generating minimal service-attributed telemetry also makes threat actor activity difficult to distinguish from routine activity. Any organization with exposed AWS credentials and permissive Identity and Access Management (IAM) policies are potentially at risk, particularly those without guardrails or monitoring around WorkMail and SES configuration.
In this post, we analyzed a real-world incident observed by our MDR team in which threat actors abused native AWS email services to build phishing and spam infrastructure inside a compromised cloud environment. We will reconstruct the attacker’s progression from credential validation and IAM reconnaissance to bypassing Amazon SES safeguards by pivoting to AWS WorkMail. Along the way, we highlight how legitimate service abstractions can be leveraged to evade detection, examine the resulting logging and attribution gaps, and outline practical detection and prevention strategies defenders can use to identify and disrupt similar cloud-native abuse.
Background: AWS WorkMail and its key components
AWS WorkMail is a fully managed business email and calendaring service that allows organizations to operate corporate mailboxes without deploying or maintaining their own mail servers. It supports standard email protocols such as IMAP and SMTP, as well as common desktop and mobile clients, making it a lightweight, pay-as-you-go alternative for teams already operating within AWS.
To understand the activities performed by threat actors in the incident, it’s important to first introduce several core concepts within AWS WorkMail.
Organization
An Organization is the top-level container in WorkMail. It represents an isolated email environment that holds all users, groups, and domains. Each WorkMail organization is region-specific and operates independently, which allows attackers to create disposable, self-contained email infrastructures with minimal setup.
Users
Users represent individual mail-enabled identities within a WorkMail organization. After a user is created using the “workmail:CreateUser” API call, a mailbox can be assigned via a “workmail:RegisterToWorkMail”API call. Once registered, the user can authenticate to the AWS WorkMail web client or connect via standard email protocols and immediately begin sending and receiving email.
Groups
Groups are collections of users that can receive email on behalf of multiple members. They are typically used for distribution lists or shared inboxes and can simplify bulk message delivery or internal coordination within a WorkMail organization.
Domains
Domains define the email address namespace used by a WorkMail organization ([email protected]). Before a domain can be used, ownership must be verified. This verification process leverages the standard domain verification mechanism of Amazon Simple Email Service, typically via DNS records. Once verified, the domain can be actively used for sending and receiving email, enabling threat actors to operate from attacker-controlled, but seemingly legitimate, domains.
Attack analysis
The diagram below contains a graphical representation of the key events carried out by the attackers throughout the attack, starting with initial access actions, continuing through privilege escalation, and ending with the achievement of objectives.
Figure 1: Graphical visualization of the attack
Initial access
The compromise began with the exposure of long-term AWS access keys. The first indication of malicious activity was an “sts:GetCallerIdentity” API call with the User-Agent set to “TruffleHog Firefox.” This strongly suggests the use of TruffleHog, a tool commonly leveraged by adversaries to discover and validate leaked credentials from sources such as GitHub, GitLab, and public S3 buckets. Rapid7 has frequently observed TruffleHog usage in active campaigns, including activity attributed to groups such as the Crimson Collective.
Several days after this initial credential validation, we observed suspicious activity involving a second IAM user authenticated via long-term access keys. While we cannot conclusively prove that both users were accessed by the same operator, multiple factors suggest they were part of the same intrusion activity. Notably, both authentications originated from the same geographic region, which was anomalous for the victim’s normal operating patterns. Throughout the incident window, access to both accounts was conducted through a rotating set of IP addresses associated primarily with cloud service providers such as Amazon and DigitalOcean. This infrastructure choice is consistent with common adversary tradecraft used to obfuscate true origin and blend into legitimate cloud-to-cloud traffic.
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Figure 2: Example TruffleHog output showing discovered credentials for Google Cloud Platform (GCP)
Discovery phase and privilege escalation
Following initial access, the first compromised user was used to perform basic environment discovery via native AWS APIs. These attempts repeatedly resulted in AccessDenied errors, indicating that the exposed credentials were constrained by limited permissions. The activity was conducted using the AWS command-line interface (CLI), suggesting hands-on, interactive exploration by the threat actor rather than automated tooling.
After encountering these limitations, the adversary shifted activity to the second set of compromised credentials, which possessed significantly broader permissions. With this user, enumeration became more deliberate and structured. The actor began with iam:ListUsers API calls to understand the identity landscape and then used a technique of intentionally triggering API errors to confirm specific permissions without making persistent changes.
As part of this broader discovery effort, the actor also queried Amazon SES to assess its current configuration and readiness for abuse. Specifically, they executed ses:GetAccountand ses:ListIdentities. These calls allowed the adversary to quickly map the operational status of SES within the account. The ses:ListIdentities API call was used to determine whether any verified identities (domains or email addresses) already existed that could be immediately leveraged for sending mail; none were present at the time. In parallel, ses:GetAccountwas used to identify whether the account was operating in the SES sandbox, which would impose strict sending limits and require additional steps before large-scale email campaigns could be launched.
This SES-focused reconnaissance indicates early intent to abuse email-sending capabilities and demonstrates how attackers can efficiently evaluate service readiness using only a small number of low-noise management API calls.
For example, the actor attempted to create an IAM user that already existed. The resulting error response confirmed possession of iam:CreateUserpermissions without successfully creating a new entity:
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{
"userAgent": "aws-cli/1.22.34 Python/3.10.12 Linux/5.15.0-113-generic botocore/1.23.34",
"errorCode": "EntityAlreadyExistsException",
"errorMessage": "User with name xxxx already exists."
}
Listing 1: Part of the iam:CreateUser CloudTrail log
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A similar validation was performed using iam:CreateLoginProfile. By supplying a password that violated the account’s password policy, the actor received a PasswordPolicyViolationException, confirming their ability to create console login profiles:
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{
"userAgent": "aws-cli/1.22.34 Python/3.10.12 Linux/5.15.0-113-generic botocore/1.23.34",
"errorCode": "PasswordPolicyViolationException",
"errorMessage": "Password should have at least one uppercase letter"
}
Listing 2: Part of the iam:CreateLoginProfile CloudTrail log
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After validating the scope of their privileges, the adversary created a new IAM user, attached the AWS managed policy “AdministratorAccess”, and established a login profile to enable AWS Management Console access. This marked a transition from CLI-based reconnaissance to full GUI-based control, providing unrestricted access and setting the stage for subsequent operational activity.
Action on objectives: Preparing email infrastructure for abuse
By the end of the discovery phase, the threat actor had established two critical facts:
No verified identities existed in Amazon Simple Email Service (SES).
The account remained restricted by the SES sandbox.
The SES sandbox is explicitly designed to limit fraud and abuse, and its restrictions effectively prevent meaningful phishing or spam campaigns. While an account remains in the sandbox, the following controls apply:
Emails can only be sent to verified identities (email addresses or domains) or the SES mailbox simulator.
A maximum of 200 messages per 24-hour period.
A maximum sending rate of 1 message per second.
These constraints made SES unsuitable for immediate abuse at scale. Rather than abandoning the service, the attacker initiated a process to legitimize higher-volume email sending.
First, they opened a support case with AWS requesting removal from the SES sandbox. In parallel, they requested a substantial increase to the daily sending quota— setting it to 100,000 emails per day —using the servicequotas:RequestServiceQuotaIncreaseAPI call.
Listing 3: Request parameters from RequestServiceQuotaIncrease API call
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During this waiting period, the actor focused on persistence and stealth. Multiple IAM users were created.. These usernames were deliberately chosen to resemble region- or service-scoped automation accounts rather than human operators. To further reduce suspicion during IAM audits, the attacker attached narrowly scoped, SES-only policies to these users instead of broad administrative permissions. This approach allowed them to preserve operational access while minimizing obvious indicators of compromise such as over-privileged identities.
At this stage, the attacker had effectively prepared the account for large-scale email abuse-but they did not wait for AWS approval to proceed.
Bypassing SES controls by abusing AWS WorkMail
Rather than remaining idle while SES sandbox removal and quota increases were pending, the attacker pivoted to AWS WorkMail, which offers an alternative email-sending pathway with significantly fewer upfront restrictions.
Using the workmail:CreateOrganizationAPI, the threat actor created multiple WorkMail organizations. They then initiated domain verification workflows for domains designed to appear legitimate and business-like, including:
cloth-prelove[.]me
ipad-service-london[.]com
Domain verification was performed through ses:VerifyDomainIdentity and ses:VerifyDomainDkim, with the calls originating from workmail.amazonaws.com. This highlights an important nuance for defenders: although SES APIs are involved, the activity is driven by WorkMail provisioning rather than traditional SES email campaigns.
Once domain verification was completed, the actor created multiple mailbox users directly within WorkMail, such as:
service@ipad-service-london[.]com
marketing@ipad-service-london[.]com
These accounts served two purposes. First, they established persistence at the application layer, independent of IAM. Second, they provided credible sender identities for phishing and spam operations, closely resembling legitimate corporate email addresses.
There were also AWS directory service events logged by CloudTrail that show new aliases created for the new sender domains, using the victim’s directory tenant:
CreateAlias
AuthorizeAppication
This pivot is particularly impactful because AWS WorkMail does not implement a sandbox model comparable to SES. Emails can be sent immediately to external, unverified recipients. Additionally, WorkMail supports significantly higher sending volumes than SES sandbox limits. While Rapid7 has not empirically validated the maximum throughput, AWS documentation cites a default upper limit of 100,000 external recipients per day per organization, aggregated across all users.
Email sending methods and logging gaps
The attacker had two viable options for sending email through WorkMail:
1. Web interface Emails sent through the AWS WorkMail web client may surface indirectly in CloudTrail as “ses:SendRawEmail” events. These events are generated because WorkMail uses Amazon Simple Email Service (SES) as its underlying mail transport, even though the messages are composed and sent entirely through the WorkMail application.
While these events are not attributed to an IAM principal, they do expose several pieces of valuable metadata within the “requestParameters” field — most notably the sender’s email address and associated SES identity. This allows defenders to link outbound email activity to specific WorkMail users and recently verified domains, even in the absence of traditional application or message-level logs.
One notable limitation of these “ses:SendRawEmail” events is the absence of a true client source IP address. Because emails sent via the WorkMail web interface are executed by an AWS-managed service on behalf of the mailbox user, CloudTrail records the “sourceIPAddress” as “workmail.<region>.amazonaws.com” rather than the originating IP address of the actor’s browser session. This effectively obscures the attacker’s true network origin and prevents defenders from correlating email-sending activity with suspicious IP ranges, TOR exit nodes, or previously observed intrusion infrastructure.
Listing 4: SendRawEmail event logged after an email is sent via AWS WorkMail web interface⠀
⠀
While limited, this telemetry can still be valuable for correlating suspicious sending behavior with recently created WorkMail users or newly verified domains.
2. SMTP access Alternatively, the attacker can authenticate directly to WorkMail’s SMTP endpoint and send messages programmatically. Emails sent via SMTPdo not generate CloudTrail events, even when SES data events are enabled, creating a significant blind spot for defenders.
An example Python script used to send email through WorkMail SMTP is shown below:
⠀
import smtplib
from email.message import EmailMessage
# Configuration
SMTP_SERVER = "smtp.mail.us-east-1.awsapps.com"
SMTP_PORT = 465
EMAIL_ADDRESS = "[email protected]"
EMAIL_PASSWORD = "****"
# Create the message
msg = EmailMessage()
msg["Subject"] = "WorkMail SMTP"
msg["From"] = EMAIL_ADDRESS
msg["To"] = "<unverified_email>"
msg.set_content("Email Delivered to an Unverified Email via AWS WorkMail")
# Send the email
try:
with smtplib.SMTP_SSL(SMTP_SERVER, SMTP_PORT) as smtp:
smtp.login(EMAIL_ADDRESS, EMAIL_PASSWORD)
smtp.send_message(msg)
print("Email sent successfully!")
except Exception as e:
print(f"Error: {e}")
Listing 5: Example script sending messages via AWS WorkMail via SMTP
⠀
From an attacker’s perspective, this method is ideal: higher volume, immediate external reach, and minimal centralized logging. From a defender’s perspective, it underscores the importance of monitoring WorkMail organization creation, domain verification events, and mailbox provisioning, as these actions often precede phishing activity that will never be visible in CloudTrail.
Conclusion
This incident illustrates how threat actors can abuse higher-level AWS services to deploy phishing and spam infrastructure closely resembling legitimate enterprise usage. While AWS WorkMail is not designed to support bulk email operations, attackers can still leverage it as an interim capability alongside Amazon SES. By abusing WorkMail’s authenticated mailboxes and relaxed upfront controls, adversaries can begin sending lower volumes of email immediately — well before SES is moved out of the sandbox and higher sending quotas are approved. This staged approach allows attackers to establish sender reputation, validate infrastructure, and maintain operational momentum while bypassing many of the friction points intentionally built into SES.
To mitigate this class of abuse, organizations should combine preventive guardrails with focused detection. Where AWS WorkMail is not required, its use should be explicitly blocked using AWS Organizations Service Control Policies (SCPs) to prevent organization creation and mailbox provisioning. In environments where WorkMail is needed, IAM policies should enforce strict least-privilege access and treat WorkMail and SES administration as privileged operations subject to monitoring and approval. Finally, organizations should reduce the likelihood of initial access by implementing secure development and operational practices — such as secret scanning in code repositories, regular key rotation, and minimizing long-term access keys — to limit the impact of credential leakage and prevent attackers from converting compromised credentials into scalable email abuse.
MITRE ATT&CK techniques
Tactic
Technique
Details
Initial Access
Valid Accounts: Cloud Accounts (T1078.004)
The attacker authenticated to AWS using exposed long-term access keys validated with sts:GetCallerIdentity
Persistence
Create Account: Cloud Account (T1136.003)
The attacker created multiple IAM users and AWS WorkMail mailbox users to maintain persistent access
The attacker attached the AdministratorAccess managed policy to a newly created IAM user
Discovery
Cloud Infrastructure Discovery (T1580)
The attacker enumerated IAM users and assessed Amazon SES configuration and sandbox status via API calls
Impact
Resource Hijacking: Cloud Service Hijacking (T1496.004)
The attacker abused AWS WorkMail and SES to send high-volume phishing and spam emails from the victim account
Indicators of compromise (IOCs)
139.59.117[.]125
3.0.205[.]202
54.151.176[.]0
Note: IP addresses 3.0.205[.]202 and 54.151.176[.]0 are Amazon owned IP addresses so care should be taken when applying IP blocks.
Rapid7 customers
InsightIDR and Managed Detection and Response (MDR) customers have existing detection coverage through Rapid7’s expansive library of detection rules. These detections are deployed and will alert on the behaviors described in this technical analysis.
Rapid7 has partnered with ARMO, a leader in cloud infrastructure and application security based on runtime data, to offer Cloud Runtime Security. The new offering, currently in beta, extends our vulnerability and exposure management solution, Exposure Command, into the moment where cloud risk becomes real: while applications and workloads are running. The solution does this with several differentiators that map directly to what security leaders need most: signal accuracy and response speed.
Introducing Rapid7 Cloud Runtime Security
Rapid7 Cloud Runtime Security combines kernel-level observability with AI-powered behavioral analysis to create a continuous, threat-aware defense layer within all cloud environments.
The solution provides:
AI-driven behavioral baselines for container activity. Because services, teams, and software releases create constant change, static policies can quickly become irrelevant and overly noisy. Cloud runtime security augmented by AI helps establish a behavioral baseline of what “normal” looks like for workload activity. This baseline becomes the standard for identifying deviations that indicate active exploits. This becomes even more critical for AI workloads in which runtime is the only place to understand behavior.
Root-cause in every risk finding. When a threat is detected, the platform does not just create noise by firing an alert. Instead, it reconstructs the entire event with root-cause insights by linking application-layer activity (like a SQL injection) to infrastructure-level changes (like a container escape). It also provides a natural-language narrative of the attack, showing exactly what happened, which credentials were used, and which resources were accessed.
Connected dots across the entire cloud ecosystem. From cloud and Kubernetes events, clusters APIs, container and workload processes, to individual lines of code, the solution displays the entire attack story. Instead of sifting through siloed, disparate security tools that each present different alerts, teams gain a single source of objective truth for faster forensic analysis.
Deep application-layer visibility. Instantly detect and respond to common attacks, including SQL injections, command injections, local file inclusion (LFIs), and server-side request forgery (SSRF) that regular endpoint detection and response (EDR) tools overlook because their visibility is limited to the host and process level.
Orchestrated automated response to detected anomalies. Detection is only part of the full battle. Speed is the difference between a contained event and a disruptive, expensive data breach. The solution automatically terminates malicious processes, pauses compromised containers, isolates namespaces, or blocks egress to prevent an attacker’s lateral movement.
Rapid7 Cloud Runtime Security enables orchestrated automated response when anomalies are detected, enabling teams to quickly mobilize and contain threats.
Security amidst the chaos
Chaos is the natural state of cloud environments, where instances frequently shut down and containers constantly change. In these environments, chaos isn’t a deficiency, but an inherent characteristic of distributed systems. Containers spin up and down constantly, deployments change multiple times per day, images get rebuilt and redeployed, identities and permissions drift, and workloads inherit misconfigurations at scale
Traditional vulnerability management (VM) was designed to protect static, on-prem technology architectures. Periodic scans, CVSS scores, and reactive patching have been effective here, but point-in-time snapshots and reactive remediation strategies collapse in dynamic, highly-distributed cloud environments for the following reasons:
Blind spots. Ephemeral cloud resources can spin up, perform a task, and disappear in minutes. If a vulnerable container exists for only 10 minutes between a scheduled scan, traditional VM tools will miss it and an automated attacker script will find and exploit it in seconds.
Missing context. Network scanners find CVEs, but they often lack contextual awareness. For instance, a ‘critical’ vulnerability may represent a low risk in a library that exists on an isolated container with no internet access. Conversely, a ‘medium’ vulnerability on a public-facing server with an over-privileged IAM role can be a catastrophic exploit.
Misconfigurations. In the cloud, vulnerabilities can live on unpatched software, but also arise from misconfigured systems. Consider a fully patched server that is compromised because of an open S3 bucket or a broad IAM policy. According to Gartner, “through 2026, nonpatchable attack surfaces will grow from less than 10% to more than half of the enterprise’s total exposure, reducing the impact of automated remediation practices1.”
AI-driven complexity. AI is accelerating innovation cycles, and as organizations push out more code, AI has introduced several new dimensions to the attack surface. These can include vulnerabilities that trick LLM models into revealing sensitive data or bypassing security controls.
The new baseline for modern cloud security
As modern cloud environments are constantly changing, security teams need to know in real time when exposures become active threats. Rather than toiling over a ‘high’ or ‘critical’ vulnerability, they prioritize remediation actions based on the paths that lead to compromise. This is because a vulnerability can become a critical exposure when the conditions around it make it reachable, exploitable, and high impact. Savvy security teams use exposure management solutions to assess whether they are likely to get compromised, then lean on cloud runtime platforms to identify, in real-time, whether they are actively compromised. As a result, the best security programs now run on a “two-engine” model:
Predictive and preemptive with exposure management. This risk-forecasting layer discovers, prioritizes, and guides action on the exposures most likely to lead to material impact. Organizations utilize exposure management solutions to identify which exposures should be addressed first, the shortest paths to breach, and the remediation activities that most reduce risk.
Real-time and proactive with runtime security. This threat-reality layer detects anomalous behavior as it happens and supports immediate containment actions. Organizations use runtime security solutions to assess whether an exposure is actively being exploited, the configuration changes that may have led to the exposure, and the actions that need to be taken to contain the threat.
On their own, each part of the engine is valuable, but exposure management without runtime can cause teams to overlook active threats; runtime without exposure context can drown teams in noisy alerts. Together, these solutions enable teams to prioritize what matters most and respond instantly when it becomes active.
Visit our cloud security pages to learn more about how Rapid7 empowers teams to proactively manage risk, accelerate DevSecOps, and enforce compliance across multi-cloud environments.
At re:Invent 2025, AWS unveiled its latest AI- and automation-enabled innovations to strengthen cloud security for customers to grow their business. Organizations are likely to increase security spending from $213 billion in 2025 to $377 billion by 2028 as they adopt generative AI. This 77% increase highlights the importance organizations place on securing their AI investments as they expand their digital footprints.
AWS uses artificial intelligence, machine learning, and automation to help you secure your environments proactively. These advancements include AI security agents, machine-learning and automation-driven threat detection, and agent-centric identity and access management. Together, they unify defense-in-depth across the application, infrastructure, network, and data layers to protect organizations from a wide spectrum of threats, vulnerabilities, and misconfigurations that could disrupt business operations.
AI security agents
AWS is embedding AI agents directly into security workflows to perform code reviews, collate incident response signals, and secure agentic access.
AWS Security Agentis a frontier agent that proactively secures applications throughout the development lifecycle. It conducts automated security reviews tailored to organizational requirements and delivers context-aware penetration testing on demand. By continuously validating security from design to deployment, it helps prevent vulnerabilities early in development.
AWS Security Incident Response delivers agentic AI-powered investigation capabilities designed to help enhance and accelerate security event response and recovery.
AgentCore Identitynow offers authentication that provides enhanced access controls for AI agents, which restricts their interactions to authorized services and data based on specific user permissions and attributes. Enabling granular boundaries for how AI agents interact with enterprise applications reduces the risk of unauthorized access or data exposure.
ML and automation-driven threat detection
Machine learning models and automation now accelerate threat detection across more AWS environments, surfacing otherwise hard to see correlations, such as for sophisticated multistage attacks, at scale. These latest advancements save time by automatically correlating signals into consolidated sequences.
GuardDuty extended threat detection for EC2 and ECSuses advanced AI and ML algorithms to identify sophisticated, multi-stage attacks targeting AWS accounts, workloads, and data in virtual machine, container, and serverless workloads.
GuardDuty malware protection for AWS Backup automatically scans EC2, EBS, and S3 backups for malware. It helps you identify your last known clean backup to minimize business disruption during recovery and supports incremental scanning of net new data in between backups.
Intelligent access controls are redefining how organizations manage identities and permissions. These controls automate policy generation and improve your zero trust maturity level, making it easier for you to use AWS services.
IAM policy autopilot helps AI coding assistants quickly create baseline IAM policies that teams can refine as the application evolves, so organizations can build faster.
Outbound identity Federation helps IAM customers to securely federate their AWS identities to external services, making it easy to authenticate AWS workloads with cloud providers, SaaS platforms, and self-hosted applications.
Private access sign-in routes 100% of console traffic through VPC endpoints instead of public internet, using intelligent routing to maintain security without compromising performance.
These AI and ML advancements transform security from reactive manual processes to proactive, scalable protection. You can use them to operationalize threat hunting and advance your security posture, even as you grow your digital real estate.
The confidence organizations place in cloud-native security validates this approach. The AWS-sponsored report of 2,800 IT and security decision makers and practitioners revealed that 81% agree that their primary cloud provider’s native security and compliance capabilities exceed what their team could deliver independently. Additionally, 56% responded that the public cloud was better positioned to deliver security as opposed to 37% that selected on-premises, and 51% believe the public cloud is better positioned to meet regulations versus 41% that responded on-premises.
Cloud is the foundation on which customers build their businesses, and AWS continues to deliver security innovations that reinforce that foundation.
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In this post, we introduce the AWS provider for the Secrets Store CSI Driver, a new AWS Secrets Manager add-on for Amazon Elastic Kubernetes Service (Amazon EKS) that you can use to fetch secrets from Secrets Manager and parameters from AWS Systems Manager Parameter Store and mount them as files in Kubernetes pods. The add-on is straightforward to install and configure, works on Amazon Elastic Compute Cloud (Amazon EC2) instances and hybrid nodes, and includes the latest security updates and bugfixes. It provides a secure and reliable way to retrieve your secrets in Kubernetes workloads.
Amazon EKS add-ons provide installation and management of a curated set of add-ons for EKS clusters. You can use these add-ons to help ensure that your EKS clusters are secure and stable and reduce the number of steps required to install, configure, and update add-ons.
Secrets Manager helps you manage, retrieve, and rotate database credentials, application credentials, OAuth tokens, API keys, and other secrets throughout their lifecycles. By using Secrets Manager to store credentials, you can avoid using hard-coded credentials in application source code, helping to avoid unintended or inadvertent access.
New EKS add-on: AWS provider for the Secrets Store CSI Driver
We recommend installing the provider as an Amazon EKS add-on instead of the legacy installation methods (Helm, kubectl) to reduce the amount of time it takes to install and configure the provider. The add-on can be installed in several ways: using eksctl—which you will use in this post—the AWS Management Console, the Amazon EKS API, AWS CloudFormation, or the AWS Command Line Interface (AWS CLI).
Security considerations
The open-source Secrets Store CSI Driver maintained by the Kubernetes community enables mounting secrets as files in Kubernetes clusters. The AWS provider relies on the CSI driver and mounts secrets as file in your EKS clusters. Security best practice recommends caching secrets in memory where possible. If you prefer to adopt the native Kubernetes experience, please follow the steps in this blog post. If you prefer to cache secrets in memory, we recommend using the AWS Secrets Manager Agent.
IAM principals require Secrets Manager permissions to get and describe secrets. If using Systems Manager Parameter Store, principals also require Parameter Store permissions to get parameters. Resource policies on secrets serve as another access control mechanism, and AWS principals must be explicitly granted permissions to access individual secrets if they’re accessing secrets from a different AWS account (see Access AWS Secrets Manager secrets from a different account). The Amazon EKS add-on provides security features including support for using FIPS endpoints. AWS provides a managed IAM policy, AWSSecretsManagerClientReadOnlyAccess, which we recommend using with the EKS add-on.
Solution walkthrough
In the following sections, you’ll create an EKS cluster, create a test secret in Secrets Manager, install the Amazon EKS add-on, and use it to retrieve the test secret and mount it as a file in your cluster.
Prerequisites
AWS credentials, which must be configured in your environment to allow AWS API calls and are required to allow access to Secrets Manager
With the prerequisites in place, you’re ready to run the commands in the following steps in your terminal:
Create an EKS cluster
Create a shell variable in your terminal with the name of your cluster:
CLUSTER_NAME="my-test-cluster”
Create an EKS cluster:
eksctl create cluster $CLUSTER_NAME
eksctl will automatically use a recent version of Kubernetes and create the resources needed for the cluster to function. This command typically takes about 15 minutes to finish setting up the cluster.
Create a test secret
Create a secret named addon_secret in Secrets Manager:
Create an AWS Identity and Access Management (IAM) role that the EKS Pod Identity service principal can assume and save it in a shell variable (replace <region> with the AWS Region configured in your environment):
Note: AWS provides a managed policy for client-side consumption of secrets through Secrets Manager: AWSSecretsManagerClientReadOnlyAccess. This policy grants access to get and describe secrets for the secrets in your account. If you want to further follow the principle of least privilege, create a custom policy scoped down to only the secrets you want to retrieve.
Attach the managed policy to the IAM role that you just created:
aws iam attach-role-policy \
--role-name nginx-deployment-role \
--policy-arn arn:aws:iam::aws:policy/AWSSecretsManagerClientReadOnlyAccess
Deploy your SecretProviderClass (make sure you’re in the same directory as the spc.yaml you just created):
kubectl apply -f spc.yaml
To learn more about the SecretProviderClass, see the GitHub readme for the provider.
Deploy your pod to your EKS cluster
For brevity, we’ve omitted the content of the Kubernetes deployment file. The following is an example deployment file for Pod Identity in the GitHub repository for the provider—use this file to deploy your pod:
In this post, you learned how to use the new Amazon EKS add-on for the AWS Secrets Store CSI Driver provider to securely retrieve your secrets and parameters and mount them as files in your Kubernetes clusters. The new EKS add-on provides benefits such as the latest security patches and bug fixes, tighter integration with Amazon EKS, and reduces the time it takes to install and configure the AWS Secrets Store CSI Driver provider. The add-on is validated by EKS to work with EC2 instances and hybrid nodes.
AWS re:Invent 2025, the premier cloud computing conference hosted by Amazon Web Services (AWS), returns to Las Vegas, Nevada, December 1–5, 2025. At AWS, security is our top priority, and re:Invent 2025 reflects this commitment with our most comprehensive security track to date. With more than 80 security aligned sessions spanning breakouts, workshops, chalk talks, and hands-on builders’ sessions, we’re bringing together the brightest minds to share insights, best practices, and innovative solutions. For security professionals, developers, and cloud architects, the event offers valuable insights into the latest security innovations at AWS, advanced threat protection capabilities, and defense strategies that scale. While attending re:Invent, you can visit the Security kiosk and AI Security kiosk at the expo hall to engage directly with AWS security experts about your specific needs.
The security track session selection process was driven by our extensive analysis of customer needs and real-world implementation challenges. We specifically focused on security areas where customers seek the most guidance and coalesced the sessions around four major themes: Securing and Leveraging AI, Architecting Security and Identity at scale,Building and scaling a Culture of Security, and Innovations in AWS Security. Our goal with the sessions is to address immediate security challenges and help you achieve broader business outcomes. In the following sections, we highlight a few key sessions in each of the four themes. You can visit the re:Invent catalog for a view of all sessions.
Securing and leveraging AI
Securing and using AI emerges as a dominant theme for the Security and Identity track, reflecting both the opportunities and challenges AI presents. From protecting AI workloads to harnessing AI for enhanced security operations, sessions span multiple AI topics to help organizations navigate this transformative technology safely and effectively. Here are a few key sessions on each of the AI topics.
Securing AI workloads
Breakout SEC410 – Advanced AI Security: Architecting Defense-in-Depth for AI Workloads: Dive deep into advanced security architectures for AI workloads, exploring how to protect your workload against sophisticated attack vectors. Through technical examples, we’ll implement secure architectures for AI workloads, covering identity, fine-grained access policies, and secure foundation model deployment patterns. Learn how to harden generative and agentic AI applications using AWS security capabilities, implementing least-privilege controls, and building secure architectures at scale.
Workshop SEC406 –Red teaming your generative AI and MCP applications 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 threat chains against Model Context Protocol (MCP) applications, systematically discovering vulnerabilities. Master countermeasures from prompt templating and guardrails to OAuth-enhanced MCP security configurations that prevent unauthorized access. 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.
Security for Agentic AI
ChalkTalk SEC408 –Securing Agentic AI: OWASP, MAESTRO, and Real-World Defense Strategies: Explore the latest in Agentic AI security with OWASP’s updated Threats and Mitigations Guide and Agentic Security Initiative. We will also explore MAESTRO, a specialized threat modeling approach for AI systems, offering a layered methodology to identify and mitigate risks throughout the AI lifecycle. Through a real-world case study, we’ll demonstrate security best practices for agentic AI, including robust governance, continuous monitoring, and least-privilege access. Learn how to confidently deploy autonomous AI agents while minimizing risks. Gain practical insights for building secure, trustworthy, and resilient agentic AI applications that can transform industries safely.
Workshop SEC307 – Design authentication, authorization, and logging logic in Agentic AI apps: This hands-on workshop addresses the critical challenge of managing identities and permissions for generative AI agents. Learn to implement user and machine authentication, along with fine-grained authorization mechanisms, tailored for AI agents, tools, and LLMs. Explore consent management and permission delegation in AI contexts. Participants will gain practical experience using AWS’s latest services, including Strands SDK, Amazon Bedrock AgentCore Identity, Amazon Cognito for identity management, and Amazon Verified Permissions for authorization decisions. By the end, you’ll have the skills to enhance security and compliance in your AI operations using AWS’s cutting-edge identity and access management solutions.
Using AI for security
Builders SEC318– Strengthen your network security with generative AI: Transform how you manage network security using the power of generative AI. See how Amazon Q Developer helps you explore AWS Shield Network Security Director findings through natural language conversations. Learn to quickly identify misconfigured resources, understand security issues, and implement guided fixes across your AWS environment.
Chalktalk SEC304 – Building an AI-Powered security guardian for your Cognito applications: Elevate your application security with an intelligent AI-Powered security guardian to protect your Amazon Cognito-authenticated applications. In this interactive session, we’ll explore identity best practices and building an AI agent using Amazon Bedrock AgentCore to help verify best practices, perform detective analysis, and take automated preventative actions to mitigate risks. We’ll talk through how an AI agent can perform dynamic WAF rule adjustments, modify authentication flows, and perform security operations center (SOC) actions. Bring your questions and scenarios as we deep dive into how to implement AI-driven security controls for your Cognito protected applications.
Building and Scaling Culture of Security
This theme is woven throughout the re:Invent 2025 security track, reflecting the belief that technological solutions alone cannot ensure robust security outcomes. Enterprises with a Culture of Security become security-first organizations, after which they can accelerate secure digital transformations. Some of the sessions that showcase this theme are:
Breakout SEC319 – Climbing the AI Mountain With Your Security Team: Navigate the intersection of AI and security culture in this practical session. Learn how security teams can effectively embrace AI innovation through incremental steps and validation techniques. Using real-world examples, we’ll demonstrate how security practitioners can adapt their skills to AI challenges regardless of their level of specialized expertise and share strategies for building security-aware AI practices. From understanding generative and agentic AI-specific security risks to creating engaging team exercises, discover how to transform security from a potential bottleneck into an enabler of responsible AI innovation. Attendees will leave with actionable insights for building a security-first approach to AI adoption.
Chalktalk SEC343 – Fostering a Resilient Incident Response Culture: Discover how to combine human expertise with intelligent automation in security incident response. Learn how AWS Security Incident Response, auto-triaging capabilities, and generative AI work together to augment—not replace—your team’s decision-making. We’ll explore how integrating AWS Security Incident Response and generative AI into your workflows can reduce alert fatigue, accelerate accurate incident classification, and enable responders to focus on critical analysis. See how leading organizations balance automation with human oversight, creating more efficient and resilient incident response processes while maintaining the crucial elements of human judgment and institutional knowledge. Uncover practical strategies for integrating AI-driven insights with human expertise in your incident response culture.
Chalktalk SEC227 – Translating Security Metrics into Business Outcomes: Today CISOs face the challenge of translating complex security data into business value. This session reveals proven frameworks for transforming security metrics into strategic insights that drive boardroom decisions. Learn how leading organizations leverage AWS Security Hub, OpenSearch and Security Analytics and automation to build real-time risk dashboards that demonstrate security’s business impact. Walk away with practical strategies for evolving your security program from operational metrics to business outcomes, enabling data-driven investment decisions and measurable risk reduction that resonates with executives.
Architecting Security and Identity at scale
This theme explores how you can use the comprehensive toolset and proven patterns provided by AWS to implement enterprise-grade security controls that scale from individual workloads to global organizations. Some key sessions on this theme include:
ChalkTalk SEC333 – From Static to Dynamic: Modernizing AWS Access Management: Building a robust AWS identity foundation requires moving beyond static credentials. This session deep dives into proven patterns for implementing dynamic, temporary access across your AWS organization. We’ll explore real-world challenges of access key dependencies and share practical approaches to transition towards ephemeral credentials using IAM roles and SAML federation. Through practical examples and lessons learned, discover how to implement secure authentication patterns that scale while reducing operational overhead. Walk away with actionable strategies to strengthen your identity perimeter and modernize your access management approach.
Workshop SEC401 – Active defense strategies using AWS Al/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. Discover implementation patterns for prompt engineering, deployment strategies, and monitoring methodologies. You must bring your laptop to participate.
Workshop SEC303 –Advanced AWS Network Security: Building Scalable Production Defenses: In this hands-on workshop, master AWS network security techniques to defend against today’s most critical threats. Learn to implement layer 7 capabilities and deep packet inspection using AWS Network Firewall and Route 53 Resolver DNS Firewall, securing both internet-bound and internal traffic flows. Gain practical experience in configuring scalable, reliable filtering to combat zero-day attacks and ransomware, while also implementing sophisticated east-west traffic controls to prevent lateral movement. Through real-world scenarios, you’ll learn to leverage IDS/IPS filtering, domain-based controls, and principle of least privilege using fully managed AWS services. Leave equipped to build resilient network defenses against modern cyber threats.
Innovations in AWS Security
AWS innovation in security capabilities is designed to help organizations outpace evolving threats. From advanced threat detection powered by machine learning to revolutionary data protection mechanisms, these innovations demonstrate the AWS commitment to keeping customers secure in an evolving landscape. Some of the innovation-focused sessions are:
Breakout SEC203–State of the Art: AWS data protection in 2025 (ft. Vanguard): Join AWS Cryptography leaders for a comprehensive tour of 2025’s groundbreaking security innovations. Discover the latest launches across Cloudfront, KMS, Private CA, and Secrets Manager, showcasing AWS’s implementation of NIST-standardized post quantum cryptography. Learn how we’re revolutionizing cloud security through quantum-resistant algorithms, advanced certificate management, and automated secrets handling. Get an inside look at Vanguards enterprise-wide PQC migration and how they made it a strategic business priority. See firsthand how AWS continues raising the bar on data protection for your most sensitive workloads.
Breakout SEC323 – AWS detection and response innovations that drive security outcomes: 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 for integrating AWS detection and response services to strengthen and simplify security across your AWS environment.
Breakout SEC310 – Innovations in Infrastructure Protection to strengthen your network: In this session, learn about new capabilities in infrastructure protection services like AWS Network Firewall, Amazon Route 53 DNS Firewall, AWS WAF, and AWS Shield, to simplify your application protection, streamline robust egress protections and gain insight into your network. Dive deep into how new visibility investments can give insight into misconfigurations, possible threats, and proactive identification of network configuration issues.
Conclusion
Don’t miss this opportunity to enhance your cloud security knowledge and connect with AWS security experts and industry peers. For a full view of the Security and Identity sessions, explore the AWS re:invent catalog where you can filter sessions by topic, areas of interest, role, and so on.
When you register, you’ll gain access to the session reservation system where you can reserve your seats. Popular security sessions, especially hands-on sessions, fill up quickly because of limited capacity, so we recommend reserving your preferred sessions as soon as scheduling opens. See you are re:Invent!
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Security and governance teams across all environments face a common challenge: translating abstract security and governance requirements into a concrete, integrated control framework. AWS services provide capabilities that organizations can use to implement controls across multiple layers of their architecture—from infrastructure provisioning to runtime monitoring. Many organizations deploy multi-account environments with AWS Control Tower, or Landing Zone Accelerator to implement a foundational baseline of controls and security architecture. Once their environment is provisioned, organizations typically look to add additional detective controls from services such as AWS Security Hub and AWS Config based on security, compliance, and operational requirements. While this sequence is a great start, there are more opportunities during this time to implement layered defense-in-depth coverage to enhance your security posture.
Highly regulated industries such as fintech and financial services are often viewed as the gold standard for governance and security controls. While these sectors have established robust frameworks, there’s consistently room for improvement and valuable lessons for other industries looking to enhance their control environments. However, many organizations struggle to move beyond a basic compliance-focused approach. In our experience working with customers across various sectors, this limited perspective often stems from multiple factors, including:
Immediate compliance pressures
Resource constraints
Limited understanding of control maturity pathways
Focus on detection rather than prevention
A tendency to prioritize technology-agnostic controls over bult-in AWS capabilities, leading to unnecessarily complex implementations
The good news? A more comprehensive approach that uses AWS preventative, proactive, detective, and responsive controls can significantly reduce risk while decreasing operational overhead through automation.
In this post, we outline a practical framework that you can adopt to evolve your security and governance controls strategy. We explore how your organization can mature from a detection-focused security posture to a multi-layered control framework, using real-world examples across the resource lifecycle, including infrastructure-as-code testing and preventative controls such as service control policies (SCPs), resource control policies (RCPs), and declarative policies (DPs).
Drawing from best practices in highly regulated industries while incorporating modern cloud capabilities through services such as AWS Organizations and AWS Control Tower, we provide a structured framework that you can use to elevate your organization’s control environment beyond basic compliance requirements.
Customer challenges in implementing controls
Organizations face several significant challenges when attempting to implement a comprehensive control framework in AWS. Let’s explore the main obstacles:
Resource constraints and expertise gaps
Security teams often find themselves caught between limited resources and expanding responsibilities in the cloud. With constrained budgets and personnel, teams typically gravitate toward quick wins through detective controls, which appear straightforward to implement initially. While this provides immediate visibility, it can leave critical gaps in security posture. Many teams lack comprehensive expertise across all control types, particularly in implementing preventative, proactive, and responsive controls effectively. The pressure to demonstrate immediate security improvements, combined with day-to-day operational demands, frequently results in tactical solutions rather than strategic, layered security approaches.
Analysis paralysis
Deciding which tools to prioritize can be a challenge; the breadth of options and extensive capabilities available across AWS security services and third-party tools can feel overwhelming at times. Security teams struggle to determine the optimal mix of controls for their environment and where to begin implementation. This challenge is compounded by the complexity of mapping technical compliance requirements to cloud-focused capabilities and maintaining visibility into emerging threats as the security landscape evolves. The layers of abstraction created by proliferating security controls can further obscure clear decision-making, leading teams to delay critical security improvements while seeking perfect solutions.
Misunderstanding of defense in depth
Defense in depth as a concept is good, but it can be misunderstood and difficult to achieve, leading to vulnerabilities in the security architecture. A common misconception is that a single strong control, separation of duties in AWS Identity and Access Management (IAM) roles, least permission in IAM policies, and so on, provide sufficient protection. This overlooks the crucial value of implementing controls at multiple points and how different control types can be combined to create a robust security posture. Teams often miss how organizational controls like SCPs can work in harmony with workload-specific controls to achieve greater protection. The role of preventative controls in guiding technical implementations is frequently under appreciated.
Maturity journey challenges
The path to security maturity presents numerous obstacles. Many organizations remain stuck in the early stages, implementing detective controls but never progressing to preventative measures. Security controls are often implemented in isolation, without consideration for the broader security landscape. Organizations struggle to create and follow a clear roadmap for evolving their security posture, and measuring improvement over time proves challenging.
Scale and consistency issues
As AWS environments grow, maintaining consistent governance and security becomes increasingly complex. Organizations face mounting challenges in managing exceptions and special cases across their expanding infrastructure. These interrelated challenges often result in controls implementations that fail to achieve their intended risk reduction goals. You need a structured approach to overcome these obstacles and implement comprehensive security controls, which we explore in the following sections.
Strategic investment in security
While implementing comprehensive controls requires an initial investment in time and resources, the long-term benefits fundamentally transform how organizations operate.
The foundation for this transformation begins with establishing baseline controls through proven starting points such as AWS Control Tower and its customization options. AWS Control Tower provides building blocks for secure multi-account architectures with hundreds of security capabilities and proactive controls already built in. Rather than trying to create baselines from scratch by wrangling vast amounts of account-level or resource-specific controls, you can use these accelerators to rapidly establish a strong security foundation. With these baseline controls in place, this transformation extends beyond security teams to enable the entire organization to operate more efficiently. Development and operations teams can deploy faster with confidence when security guardrails are in place. Security becomes an enabler rather than a bottleneck, so that teams across the organization can innovate while maintaining a strong security posture.
As you mature your organization’s control framework through automation and layered defenses, a security transformation occurs. Security teams shift from constant firefighting to proactive risk management. Automated policy enforcement replaces manual reviews, and the time previously spent on routine tasks can be redirected to strategic initiatives.
Preventative controls establish the foundation of a secure environment by defining the policies, standards, and requirements that guide security implementations. At their core, these controls encompass corporate security policies that outline acceptable resource configurations across the organization. They work in conjunction with compliance requirements and frameworks to help maintain regulatory alignment, while architectural standards and guidelines provide technical direction for implementations. Data classification policies play a crucial role by determining specific security requirements based on data sensitivity.
To illustrate how preventative controls work in practice, consider a common S3 bucket security requirement. A typical preventative control might establish a corporate policy stating All S3 buckets must be private by default, with public access granted only through an approved exception process. This simple but effective policy sets clear expectations and requirements before a technical implementation begins.
Organization level: SCPs blocking public S3 bucket creation.
Resource level:
RCPs enforcing network access controls, such as requiring authenticated access or limiting requests to your organization’s network range.
SCPs to stop malicious overwrites of S3 objects using SSE-C encryption by blocking s3:PutObject requests with customer-provided keys unless explicitly allowed, paired with AWS IAM Roles Anywhere for short-term credential enforcement.
Proactive controls act as an early warning system, identifying and addressing potential security issues before they manifest in your environment. These controls work by validating configurations and changes against established security requirements during the development and deployment phases. Through automated validation and policy enforcement at build and deploy time, proactive controls help prevent misconfigurations from reaching production environments, reducing the operational overhead of fixing security issues after the fact. Think of proactive controls as your first line of defense in maintaining a secure cloud environment. In AWS, these can be implemented at multiple levels:
Amazon S3 Block Public Access settings at the account level.
Policy-as-code checks in continuous integration and delivery (CI/CD) pipelines (such as CFN-Nag, or AWS Config proactive rules).
AWS CloudFormation hooks for pre-deployment validation and policy enforcement.
AWS Config rules in proactive mode to evaluate resources before creation.
At the resource level, you can use:
IAM policies restricting bucket policy modifications
CloudFormation Guard rules
#####################################
## Gherkin ##
#####################################
# Rule Identifier:
# S3_BUCKET_SERVER_SIDE_ENCRYPTION_ENABLED
# Description:
# Checks if your Amazon S3 bucket either has the Amazon S3 default encryption enabled or that the Amazon S3 bucket policy
# explicitly denies put-object requests without server side encryption that uses AES-256 or AWS Key Management Service.
# Reports on:
# AWS::S3::Bucket
# Evaluates:
# AWS CloudFormation
# Rule Parameters:
# NA
# Scenarios:
# a) SKIP: when there are no S3 resource present
# b) PASS: when all S3 resources Bucket Encryption ServerSideEncryptionByDefault is set to either "aws:kms" or "AES256"
# c) FAIL: when all S3 resources have Bucket Encryption ServerSideEncryptionByDefault is not set or does not have "aws:kms" or "AES256" configurations
# d) SKIP: when metadata includes the suppression for rule S3_BUCKET_SERVER_SIDE_ENCRYPTION_ENABLED
#
# Select all S3 resources from incoming template (payload)
#
let s3_buckets_server_side_encryption = Resources.*[ Type == 'AWS::S3::Bucket'
Metadata.cfn_nag.rules_to_suppress not exists or
Metadata.cfn_nag.rules_to_suppress.*.id != "W41"
Metadata.guard.SuppressedRules not exists or
Metadata.guard.SuppressedRules.* != "S3_BUCKET_SERVER_SIDE_ENCRYPTION_ENABLED"
]
rule S3_BUCKET_SERVER_SIDE_ENCRYPTION_ENABLED when %s3_buckets_server_side_encryption !empty {
%s3_buckets_server_side_encryption.Properties.BucketEncryption exists
%s3_buckets_server_side_encryption.Properties.BucketEncryption.ServerSideEncryptionConfiguration[*].ServerSideEncryptionByDefault.SSEAlgorithm in ["aws:kms","AES256"]
<<
Violation: S3 Bucket must enable server-side encryption.
Fix: Set the S3 Bucket property BucketEncryption.ServerSideEncryptionConfiguration.ServerSideEncryptionByDefault.SSEAlgorithm to either "aws:kms" or "AES256"
>>
}
Detective controls provide continuous visibility into your security posture by monitoring for and identifying potential security violations or unauthorized changes within your environment. While preventative controls aim to stop issues before they occur, detective controls help you maintain awareness of your security state and can identify when preventative controls have been bypassed or failed. These controls form a critical layer of defense by enabling rapid identification of security issues and providing the visibility needed for effective incident response and compliance reporting. While many organizations start and stop here, detective controls are only part of the solution:
AWS Config rules monitoring for public buckets
Security Hub findings to flag non-compliant resources
Responsive controls complete the security lifecycle by providing automated and manual mechanisms to address security issues after they’re detected. These controls define and implement the actions taken when security violations are identified, ranging from automated remediation of common misconfigurations to coordinated incident response procedures for complex security events. By establishing clear response patterns and using automation where appropriate, responsive controls help facilitate consistent and timely handling of security issues while reducing the mean time to remediation. Responsive controls address violations when they occur:
The power comes not from implementing these controls in isolation, but from using them together in a coordinated way. This layered approach begins with preventative controls to establish the requirements, followed by proactive controls to block most potential violations at the source. Issues that manage to slip through are caught by detective controls, while responsive controls automatically remediate identified problems. Throughout this process, comprehensive documentation tracks issues, remediation plans, and progress, such as through a plan of action and milestones (POAM), helping to make sure that compliance requirements are met and improvements can be measured over time.
Implementation lifecycles: Ideal compared to reality
You can follow one of two paths when implementing security controls: starting fresh with a comprehensive approach or evolving from an existing detective-focused implementation. Let’s examine both scenarios.
Starting fresh: The ideal approach
When starting from scratch, you have a unique opportunity to build your security and governance following an ideal approach. Your team can take advantage of this clean slate to architect controls and processes methodically, free from legacy constraints. The following steps offer guidance though establishing a strong foundation while maintaining the flexibility you need as your business grows.
Rationalize controls against requirements and risk profile:
Choose appropriate security frameworks (for example, CIS and NIST).
Map compliance, regulatory, legal, and contractual requirements to your base framework.
Define clear security objectives and success criteria for your security and compliance program.
Design a comprehensive control strategy:
Document control requirements across all four types (preventive, proactive, detective, and responsive controls). You can use the framework to decide which controls are best for each type of requirement.
Plan implementation phases and priorities.
Define metrics for measuring effectiveness.
Implement controls in layers:
Start with AWS Control Tower, which gives you foundational controls to mature from. You can add customizations if required.
Think about additional preventative controls that can help establish a stronger security and compliance posture.
Deploy proactive controls to stop violations.
Add detective controls as safeguards.
Implement responsive controls for automated or manual remediation.
Monitor and assess effectiveness
Evaluate control performance against defined metrics.
Identify gaps and areas for improvement.
Adjust controls based on emerging threats and changing requirements.
Implement continuous improvement feedback loop.
Evolution from detective controls: The common path
Most organizations find themselves starting with detective controls and face challenges in maturing from there:
Initial state:
Baseline detective controls through Security Hub and AWS Config
Manual remediation processes
Limited visibility into security posture
Maturation steps:
Analyze findings to identify patterns
Implement automated remediation for common issues
Add preventative and proactive controls based on recurring events
Periodically refine and update policies
Optimization:
Review control effectiveness
Identify gaps in coverage
Implement additional preventative, proactive, detective, and responsive measures
Automate processes where possible
The goal: Comprehensive and layered security controls
The goal of implementing security controls across multiple layers isn’t just about compliance or following best practices—it’s about creating a robust, resilient security posture that can effectively help prevent, detect, and respond to security issues. Let’s explore why this approach is crucial:
Why multiple control layers matter
Security controls shouldn’t exist in isolation. When implementing a security requirement, you should consider:
How can we prevent this issue from occurring?
How will we detect if our preventative controls fail?
What should happen when we detect a violation?
What policies and standards guide these decisions?
Moving beyond detection
While detective controls are important, they signal that a security violation has already occurred. A mature security posture requires:
Strong preventative controls to stop violations before they happen
Detective controls as a safety net if there is drift or a violation
Automated remediation where possible, to reduce exposure time
Clear policies to guide implementation and decisions
Measuring success
You should measure the effectiveness of your control framework through several key performance indicators. Success can be seen in the steady reduction of security findings over time, coupled with decreasing time-to-remediation metrics. The maturity of the framework becomes evident through an increasing percentage of automated remediation activities and a declining number of recurring issues. These improvements manifest in better audit outcomes, providing tangible evidence that the control framework is delivering its intended results.
Practical implementation: From theory to practice
Let’s examine how to implement a comprehensive control framework using a common security requirement: preventing exposure of sensitive data through public S3 buckets. This example demonstrates how different control types work together to create defense in depth. While not every control might be necessary for every situation, each should be carefully considered and evaluated based on various factors including system criticality, data sensitivity, operational overhead, and organizational risk tolerance. The decision to implement or omit specific controls should be deliberate and documented, rather than occurring by default.
The architecture will have layers and components like the following.
Preventative layer:
Service control policies (SCPs) or resource control policies (RCPs)
An effective security and compliance strategy includes all four types of security controls. While preventative controls are a first line of defense to help prevent unauthorized access or unwanted changes to your network, it’s important to make sure that you establish detective and responsive controls so that you know when an event occurs and can take immediate and appropriate action to remediate it. Using proactive controls adds another layer of security because it complements preventative controls, which are generally stricter in nature.
Begin by defining your security objectives, then establish clear policies to meet those objectives:
Define organizational and business objectives:
Identify data protection goals
Determine acceptable risk levels
Align with compliance requirements
Establish clear policies:
For example, document business requirements for external data sharing and access controls in security policies. These requirements will drive technical decisions around AWS storage configurations such as S3 bucket policies and public access settings.
Define permitted use cases for public access.
Establish exception processes.
Set clear ownership and responsibilities.
Deploy preventative guardrails:
Organization level:
SCPs to block public bucket creation at the organization level
Account-level S3 Block Public Access settings to enforce account-level restrictions
Resource level:
IAM policies restricting bucket policy modifications
S3 bucket policy templates with controlled deployment
RCPs to enforce rules on specific resource types across your organization
Deploy proactive guardrails:
Infrastructure as code:
Implement policy-as-code checks in CI/CD pipelines using:
Enable relevant optional AWS Control Tower guardrails
Add detective controls by creating a monitoring framework:
AWS CloudTrail for comprehensive API activity logging and auditing to enable investigation of unauthorized access attempts and configuration changes.
AWS Config rules for bucket configuration. AWS Config rules or AWS Config conformance packs deployed for the entire organization can monitor S3 bucket configurations for compliance.
Security Hub findings for continuous assessment by aggregating findings and flagging non-compliant resources.
Amazon EventBridge rules for policy changes to detect and route S3 bucket policy modifications.
IAM Access Analyzer for external access review.
Regular compliance reporting, which can be automated through AWS Audit Manager.
Implement responsive controls by automating remediation where possible:
Security Hub and Systems Manager integration to automate incident response workflows.
Custom Lambda functions for specific use cases.
Integration with ITSM for human review when needed.
The following table describes control types, what a basic implementation includes, and the services and methods used for advanced implementation.
Control type
Basic implementation
Advanced implementation
Preventative
Documentation, peer reviews
SCPs, RCPs, DPs, IAM policies, and S3 Block Public Access
Detective
Security Hub, AWS Config rules
Security Hub, AWS Config, and CloudWatch alerts
Responsive
Manual remediation
Auto-remediation through AWS Config, Systems Manager, EventBridge, and Lambda
Compliance
One-time checks
CIS/NIST mapping with Security Hub and automation of evidence collection and reporting using AWS Audit Manager
Automation
Limited
Full CI/CD Integration (for example, using CloudFormation or Terraform)
Cost optimization effort
High (manual effort)
Low (automation reduces overhead)
Scaling and management considerations
As your security and governance program matures, scaling these controls across a growing organization requires thoughtful management and automation. This section explores key considerations for effectively managing your security posture at scale, optimizing costs, and maintaining consistency across your AWS environment. Whether you’re expanding across multiple accounts, business units, or AWS Regions, these practices help you balance security requirements with operational efficiency and cost management.
Use AWS services effectively:
Consider deploying AWS Control Tower for consistent account setup and centrally deploying and managing controls at scale across multiple use cases and organizational units.
AWS Organizations can aid hierarchical policy management and the implementation of:
IAM policies for identity-based guardrails and permissions
SCPs for access guardrails
RCPs define permissions based on resource attributes
DPs to help facilitate consistent resource configurations across your organization
Tag policies for consistent resource categorization
Backup policies for data protection standards
AI service opt-out policies for data privacy requirements
Cost allocation tag policies to standardize cost attribution
Data residency policies to enforce regional restrictions
Implement resource governance through policy integration
For example, use Organizations tag policies to enforce a Confidential tag on S3 buckets storing personally identifiable information (PII). Combine this with SCPs that mandate AES-256 encryption for tagged buckets, overriding developer attempts to disable it.
Using backup policies to enforce retention rules (for example, Retention=7 years).
Use DPs to help maintain consistent security configurations across resources, such as enforcing encryption settings on Amazon Elastic Block Store (Amazon EBS) volumes or requiring specific security group rules.
Centralize logging and monitoring
Manage compliance exceptions:
Implement clear exception processes
Document and track approved exceptions
Establish regular periodic reviews of exceptions
Use time-bound approvals with automated expiration
Optimize costs:
Use periodic instead of continuous checking where appropriate
Implement targeted monitoring based on resource criticality
Implementing a comprehensive control framework is a journey, not a destination. Start from your organization’s current position, whether that’s with basic detective controls or a fresh implementation, and focus on progressive improvement rather than attempting to implement everything at once. Success comes from carefully documenting decisions about control implementation, regularly reviewing them, and using automation to reduce operational overhead while improving consistency. Progress can be measured through concrete metrics: reduced findings, faster remediation times, and increased automation.
Remember that the goal extends beyond better security—it’s about transforming security and governance from a reactive operation to a strategic enabler that provides real business value. This transformation manifests through reduced risk from systematic controls, improved operational efficiency through automation, and enhanced visibility and governance. Perhaps most importantly, it frees security teams to focus on strategic initiatives rather than routine operational tasks.
By following this approach, you can build a robust security and governance posture that not only protects your organization’s AWS environment but also supports business innovation and growth. The result is a security program that evolves alongside the business, enabling rather than hindering progress, while maintaining a strong approach that can scale with your organization’s needs.
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Cloud adoption has fundamentally reshaped security operations, bringing flexibility and scalability, but also complexity. In this session from the Take Command 2025 Virtual Cybersecurity Summit, Rapid7’s product leaders discussed how today’s SOC and MDR capabilities must evolve to keep up. Hosted by Ellis Fincham, the panel featured Dan Martin and Tyler Terenzoni, who shared real-world insights on what cloud detection and response truly requires, what CNAPP can and can’t solve, and how to bridge the growing gap between alerts and actionable context.
The cloud has changed the rules
Traditional SOC tooling often struggles to keep up with cloud-native architectures. Dan Martin opened the discussion by highlighting a key shift:
“Detection doesn’t start at the endpoint anymore. It starts with understanding your architecture.”
The panel emphasized that while cloud offers flexibility and scale, it also introduces operational complexity. From short-lived containers to decentralized ownership, cloud environments require a different approach.
Visibility is the starting point
Tyler Terenzoni spoke to the importance of understanding what’s running and who owns it:
“There’s always a disconnect between what engineering thinks is in the environment and what security actually sees.”
He noted that cloud visibility isn’t just about logs, but also understanding user behavior, policy changes, and asset configuration in near real-time. Without this, SOC teams are often reacting to alerts without enough context.
This issue was reflected in the post-event survey, where 35% of respondents listed lack of visibility across the environment as a primary challenge in their threat detection efforts.
CNAPP isn’t the answer – but it helps
The panel clarified that Cloud-Native Application Protection Platforms (CNAPPs) are useful, but not a complete solution. According to Dan Martin:
“CNAPP is great for giving you coverage, but it doesn’t give you the operational context your SOC needs.”
Integrating CNAPP data into SIEM, XDR, and MDR platforms enables richer investigations and tighter correlation across sources.
The shift from alerts to contextual action
Rather than focusing on the volume of alerts, the speakers urged security leaders to ask: can we act on this alert quickly and with confidence?
Dan Martin shared:
“It’s not about reducing alerts, it’s about giving your analysts the context to know what matters and what to do about it.”
Tyler Terenzoni added that turning alerts into action requires better integrations and unified telemetry. Without that foundation, even advanced detections can lead to noise and inefficiency.
AI will play a role, but not alone
While the session didn’t center on AI, the panel acknowledged its growing role in detection workflows. Dan Martin noted:
“AI helps with triage and correlation, but your success still depends on how well your tools talk to each other.”
The emphasis was on automation that supports analysts, not replaces them, especially in cloud environments where missteps can be costly.
Watch the full session on demand
If your team is looking to strengthen cloud detection, improve response times, or better align MDR with cloud operations, this session offers real-world insights and practical guidance.
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.
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.
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.
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.
Wicked6 Cyber Games 2025 brought hundreds of women together worldwide from March 28–30. This dynamic virtual competition, sponsored by Amazon Web Services (AWS), helped attendees tackle real-world cybersecurity challenges through e-sports experiences. With 72 hours of women talking about cybersecurity, 11 cybersecurity games, and an attack and defense tournament streamed live, the weekend-long event highlighted the value of immersive learning while investing in the next generation of cybersecurity leaders.
Now in its sixth year, Wicked6 has established itself as more than just a competition—it’s become a cornerstone in building a collaborative security community. The Women’s Society of Cyberjutsu, a national 501(c)(3) nonprofit that promotes training, mentoring, and advancement of women and girls in cybersecurity careers, has co-hosted the event since its inception. This year’s theme was leveling up, and the virtual format enabled unprecedented global participation with 31 speakers and over 500 participants of all skill levels from 48 countries.
Keynotes and sessions
The event kicked off with an upbeat introduction from Wicked6 emcee Kristin Demoranville, founder and CEO of AnzenSage, Jessica Gulick, Executive Director of Wicked6 and founder of Cyber Esports Foundation, and Mari Galloway, CEO of Women’s Society of Cyberjutsu. The trio emphasized the importance of programs such as Wicked6 that provide women with space and opportunities to learn and grow, strengthen our confidence, and celebrate each other’s contributions to the cybersecurity community.
Keynotes featuring speakers from Africa, Australia, Japan, Saudi Arabia, and the US resonated with the multinational participants. Topics ranged from hacking and protecting AI in the age of large language models (LLMs) to drawing inspiration from science fiction novels, with an eye toward boosting skills.
In his introduction to keynote speaker Anna Collard, SVP of Content Strategy and Evangelist at KnowBe4 Africa, Hart Rossman, Global Security Services Vice President at AWS, noted the positive impact of time invested by Wicked6 participants and supporters. He pointed out that the opportunity the event provides to build relationships and practice both soft skills and technical skills is a great example of what it means to build strong security culture.
“At AWS, we recognize that security is a team sport. It’s about building community and raising the bar together, so we can overcome determined adversaries and make all of our customers, colleagues, and communities safer.” —Hart Rossman, Global Security Services Vice President at AWS
Technical sessions included a presentation focused on safeguarding Amazon Simple Storage Service (Amazon S3) buckets by two AWS women in security, Customer Incident Response Team (CIRT) Responder Jennifer Paz and Worldwide Specialist Security Solutions Architect Shahna Campbell. Paz and Campbell detailed an unusual increase of data encryption events in S3 buckets that used an encryption method known as server-side encryption using client-provided keys (SSE-C). This activity, which was recently detected by the AWS CIRT team and its automated security monitoring systems, has been attributed to malicious actors who obtained valid customer credentials and were using them to re-encrypt objects. Paz and Campbell demonstrated how collective security awareness and best practices can help prevent unauthorized access to S3 buckets and protect against ransomware events that abuse stolen credentials. Details of their investigation and prescriptive guidance for helping to prevent unintended encryption of Amazon S3 objects are available in a related AWS Security Blog post.
Gamified learning
A security-focused AWS Jam was integrated into Wicked6 for a unique, gamified learning experience. With AWS Jam, individuals and teams compete to solve a series of technical challenges in a lab-based cloud infrastructure that enhances practical understanding of AWS services and best practices. Additionally, Wicked6 participants had access to 11 different cybergame services, including Hack The Box, Haiku, InspireTech, and MetaCTF, fostering a collaborative learning environment where security practitioners at all levels could grow together.
An AWS GameDay during the event also focused on enhancing cloud security skills. Led by AWS ProServe Security and AWS Support experts Jonas Buecker, Hicham Terkiba, and Makendran Gunasekaran, the games focused on network security (including network log inspections), identity and access management (IAM) policies, and using application security techniques and AWS Web Application Firewall (AWS WAF) to help prevent SQL injections. One participant enthusiastically commented, “This was an amazing opportunity to practice hands-on AWS security learning,” underscoring the unique value of the experience.
Investing in tomorrow’s security leaders
AWS partnered to donate event tickets to South Africa’s MiDO Academy, which aims to create pathways out of poverty and meaningful employment opportunities for young people, while alleviating the pressures felt by business owners to upskill and integrate new cybersecurity talent. Dale Simons, CEO of MiDO Academy said, “The sponsored tickets from AWS didn’t just provide access to training—they gave our students entry into a global security community. Our young women now see themselves as part of a larger security mission, understanding that their contributions to cybersecurity can have worldwide impact.”
By combining technical challenges with mentorship and collaboration, Wicked6 helped participants work together to upskill and address tomorrow’s challenges. Gulick highlighted the event’s impact, stating “Wicked6 2025 was a success. Each year, women from all over the world join us for speakers, games, and networking. By learning to play cybersecurity games, these women can leverage games to learn new tech skills throughout their careers.”
No matter your role—whether you’re a seasoned professional or just starting your cybersecurity journey—continuous learning is key.
“It’s important as women and as cybersecurity professionals not to get comfortable with the status quo. Leveling up means stepping out of our comfort zones and doing things that scare us. Going to networking events, actively talking with people, connecting with people on LinkedIn, getting educated to improve skills, and putting ourselves out there. Wicked6 is the perfect place to do that this year and in the years to come!” —Mari Galloway, CEO of Women’s Society of Cyberjutsu
Pursuing the path to success
As cyber threats continue to evolve, AWS remains committed to strengthening global security culture through initiatives that promote active participation and partnership. This year’s Wicked6 Cyber Games exemplified how the security community can encourage and support future leaders with collaborative learning experiences and foster a more resilient and adaptable workforce.
If you have feedback about this blog post, submit comments in the Comments section below. You can also start a new thread on the AWS Security, Identity, and Compliance re:Post to get answers from the community.
As AI and machine learning (AI/ML) become increasingly accessible through cloud service providers (CSPs) such as Amazon Web Services (AWS), new security issues can arise that customers need to address. AWS provides a variety of services for AI/ML use cases, and developers often interact with these services through different programming languages. In this blog post, we focus on Python and its pickle module, which supports a process called pickling to serialize and deserialize object structures. This functionality simplifies data management and the sharing of complex data across distributed systems. However, because of potential security issues, it’s important to use pickling with care (see the warning note in pickle — Python object serialization). In this post, we’re going to show you ways to build secure AI/ML workloads that use this powerful Python module, ways to detect that it’s in use that you might not know about, and when it might be getting abused, and finally highlight alternative approaches that can help you avoid these issues.
Quick tips
Avoid unpickling data from untrusted sources
Use alternative serialization formats, when possible, such as Safetensors
Implement integrity checks for serialized data
Use static code analysis tools to detect unsafe pickling patterns, such as Semgrep
Understanding insecure pickle serialization and deserialization in Python
Effective data management is crucial in Python programming, and many developers turn to the pickle module for serialization. However, issues can arise when deserializing data from untrusted sources. The Python bytestream that pickling uses, is proprietary to Python. Until it’s unpickled, the data in the bytestream can’t be thoroughly evaluated. This is where security controls and validation become critical. Without proper validation, there’s a risk that an unauthorized user could inject unexpected code, potentially leading to arbitrary code execution, data tampering, or even unintended access to a system. In the context of AI model loading, secure deserialization is particularly important—it helps prevent outside parties from modifying model behavior, injecting backdoors, or causing inadvertent disclosure of sensitive data.
Throughout this post, we will refer to pickle serialization and deserialization collectively as pickling. Similar issues can be present in other languages (for example, Java and PHP) when untrusted data is used to recreate objects or data structures, resulting in potential security issues such as arbitrary code execution, data corruption, and unauthorized access.
Static code analysis compared to dynamic testing for detecting pickling
Security code reviews, including static code analysis, offer valuable early detection and thorough coverage of pickling-related issues. By examining source code (including third-party libraries and custom code) before deployment, teams can minimize security risks in a cost-effective way. Tools that provide static analysis can automatically flag unsafe pickling patterns, giving developers actionable insights to address issues promptly. Regular code reviews also help developers improve secure coding skills over time.
While static code analysis provides a comprehensive white-box approach, dynamic testing can uncover context-specific issues that only appear during runtime. Both methods are important. In this post, we focus primarily on the role of static code analysis in identifying unsafe pickling.
Tools like Amazon CodeGuru and Semgrep are effective at detecting security issues early. For open source projects, Semgrep is a great option to maintain consistent security checks.
The risks of insecure pickling in AI/ML
Pickling issues in AI/ML contexts can be especially concerning.
Invalidated object loading: AI/ML models are often serialized for future use. Loading these models from untrusted sources without validation can result in arbitrary code execution. Libraries such as pickle, joblib, and some yaml configurations allow serialization but must be handled securely.
For example: If a web application stores user input using pickle and unpickles it later with no validation, an unauthorized user could craft a harmful payload that executes arbitrary code on the server.
Data integrity: The integrity of pickled data is critical. Unexpectedly crafted data could corrupt models, resulting in incorrect predictions or behaviors, which is especially concerning in sensitive domains such as finance, healthcare, and autonomous systems.
For example: A team updates its AI model architecture or preprocessing steps but forgets to retrain and save the updated model. Loading the old pickled model under new code might trigger errors or unpredictable outcomes.
Exposure of sensitive information: Pickling often includes all attributes of an object, potentially exposing sensitive data such as credentials or secrets.
For example: An ML model might contain database credentials within its serialized state. If shared or stored without precautions, an unauthorized user who unpickles the file might gain unintended access to these credentials.
Insufficient data protection: When sent across networks or stored without encryption, pickled data can be intercepted, leading to inadvertent disclosure of sensitive information.
For example: In a healthcare environment, a pickled AI model containing patient data could be transmitted over an unsecured network, enabling an outside party to intercept and read sensitive information.
Performance overhead: Pickling can be slower than other serialization formats (such as, JSON or Protocol Buffers), which can affect ML and large language model (LLM) applications when inference speed is critical.
For example: In a real-time natural language processing (NLP) application using an LLM, heavy pickling or unpickling operations might reduce responsiveness and degrade the user experience.
Detecting unsafe unpickling with static code analysis tools
Static code analysis (SCA) is a valuable practice for applications dealing with pickled data, because it helps detect insecure pickling before deployment. By integrating SCA tools into the development workflow, teams can spot questionable deserialization patterns as soon as code is committed. This proactive approach reduces the risk of events involving unexpected code execution or unintended access due to unsafe object loading.
For instance, in a financial services application where objects are routinely pickled, a SCA tool can scan new commits to detect unvalidated unpickling. If identified, the development team can quickly address the issue, protecting both the integrity of the application and sensitive financial data.
Patterns in the source code
There are various ways to load a pickle object in Python. In this context, methods for detection can be tailored for secure coding habits and needed package dependencies. Many Python libraries include a function to load pickle objects. An effective approach can be to catalog all Python libraries used in the project, then create custom rules in your static code analysis tool to detect unsafe pickling or unpickling within those libraries.
CodeGuru and other static analysis tools continue to evolve their capability to detect unsafe pickling patterns. Organizations can use these tools and create custom rules to identify potential security issues in AI/ML pipelines.
Let’s define the steps for creating a safe process for addressing pickling issues:
Generate a list of all the Python libraries that are used in your repository or environment.
Check the static code analysis tool in your pipeline for current rules and the ability to add custom rules. If the tool is capable of discovering all the libraries used in your project, you can rely on it. However, if it’s not able to discover all the libraries used in your project, you should consider adding user-provided custom rules in your static code analysis tool.
Most of the issues can be identified with well-designed, context-driven patterns in the static code analysis tool. For addressing the pickling issues, you need to identify pickling and unpickling functions.
Implement and test the custom rules to verify full coverage of pickling and unpickling risks. Let’s identify patterns for a few libraries:
NumPy can efficiently pickle and unpickle arrays; useful for scientific computing workflows requiring serialized arrays. To catch potential unsafe pickle usage in NumPy, custom rules could target patterns like:
import numpy as np
data = np.load('data.npy', allow_pickle=True)
npyfile is a utility for loading NumPy arrays from pickled files. You can add the following patterns to your custom rules to discover potentially unsafe pickle object usage.
import npyfile
data = npyfile.load('example.pkl')
pandas can pickle and unpickle DataFrames using pickle, allowing for efficient storage and retrieval of tabular data. You can add the following patterns to your custom rules to discover potentially unsafe pickle object usage.
import pandas as pd
df = pd.read_pickle('dataframe.pkl')
joblib is often used for pickling and unpickling Python objects that involve large data, especially NumPy arrays, more efficiently than standard pickle. You can add the following patterns to your custom rules to discover potentially unsafe pickle object usage.
from joblib import load
data = load('large_data.pkl')
Scikit-learn provides joblib for pickling and unpickling objects and is particularly useful for models. You can add the following patterns to your custom rules to discover potentially unsafe pickle object usage.
from sklearn.externals import joblib
data = joblib.load('example.pkl')
PyTorch provides utilities for loading pickled objects that are especially useful for ML models and tensors. You can add the following patterns to your custom rule format to discover potentially unsafe pickle object usage.
import torch
data = torch.load('example.pkl')
By searching for these functions and parameters in code, you can set up targeted rules that highlight potential issues with pickling.
Effective mitigation
Addressing pickling issues requires not only detection, but also clear guidance on remediation. Consider recommending more secure formats or validations where possible as follows:
PyTorch
Use Safetensors to store tensors. If pickling remains necessary, add integrity checks (for example, hashing) for serialized data.
pandas
Verify data sources and integrity when using pd.read_pickle. Encourage safer alternatives (for example, CSV, HDF5, or Parquet) to help avoid pickling risks.
scikit-learn (via joblib)
Consider Skops for safer persistence. If switching formats isn’t feasible, implement strict validation checks before loading.
General advice
Identify safer libraries or methods whenever possible.
Switch to formats such as CSV or JSON for data, unless object-specific serialization is absolutely required.
Perform source and integrity checks before loading pickle files—even those considered trusted.
Example
The following is an example implementation that shows safe pickle implementation as a representation of the preceding information.
import io
import base64
import pickle
import boto3
import numpy as np
from cryptography.fernet import Fernet
###############################################################################
# 1) RESTRICTED UNPICKLER
###############################################################################
#
# By default, pickle can execute arbitrary code when loading. Here we implement
# a custom Unpickler that only allows certain safe modules/classes. Adjust this
# to your application's requirements.
#
class RestrictedUnpickler(pickle.Unpickler):
"""
Restricts unpickling to only the modules/classes we explicitly allow.
"""
allowed_modules = {
"numpy": set(["ndarray", "dtype"]),
"builtins": set(["tuple", "list", "dict", "set", "frozenset", "int", "float", "bool", "str"])
}
def find_class(self, module, name):
if module in self.allowed_modules:
if name in self.allowed_modules[module]:
return super().find_class(module, name)
# If not allowed, raise an error to prevent arbitrary code execution.
raise pickle.UnpicklingError(f"Global '{module}.{name}' is forbidden")
def restricted_loads(data: bytes):
"""Helper function to load pickle data using the RestrictedUnpickler."""
return RestrictedUnpickler(io.BytesIO(data)).load()
###############################################################################
# 2) AWS KMS & ENCRYPTION HELPERS
###############################################################################
def generate_data_key(kms_key_id: str, region: str = "us-east-1"):
"""
Generates a fresh data key using AWS KMS.
Returns (plaintext_key, encrypted_data_key).
"""
kms_client = boto3.client("kms", region_name=region)
response = kms_client.generate_data_key(KeyId=kms_key_id, KeySpec='AES_256')
# Plaintext data key (use to encrypt the pickle data locally)
plaintext_key = response["Plaintext"]
# Encrypted data key (store along with your ciphertext)
encrypted_data_key = response["CiphertextBlob"]
return plaintext_key, encrypted_data_key
def decrypt_data_key(encrypted_data_key: bytes, region: str = "us-east-1"):
"""
Decrypts the encrypted data key via AWS KMS, returning the plaintext key.
"""
kms_client = boto3.client("kms", region_name=region)
response = kms_client.decrypt(CiphertextBlob=encrypted_data_key)
return response["Plaintext"]
def build_fernet_key(plaintext_key: bytes) -> Fernet:
"""
Construct a Fernet instance from a 32-byte data key.
Fernet requires a 32-byte key *encoded* in URL-safe base64.
"""
if len(plaintext_key) < 32:
raise ValueError("Data key is smaller than 32 bytes; cannot build a Fernet key.")
fernet_key = base64.urlsafe_b64encode(plaintext_key[:32])
return Fernet(fernet_key)
###############################################################################
# 3) MAIN LOGIC
###############################################################################
def upload_pickled_data_s3(
numpy_obj: np.ndarray,
bucket_name: str,
s3_key: str,
kms_key_id: str,
region: str = "us-east-1"
):
"""
Pickle a numpy object, encrypt it locally, and upload the ciphertext +
encrypted data key to S3.
"""
# 1. Generate data key from KMS
plaintext_key, encrypted_data_key = generate_data_key(kms_key_id, region)
# 2. Build Fernet from plaintext data key
fernet = build_fernet_key(plaintext_key)
# 3. Serialize the numpy object with pickle
pickled_data = pickle.dumps(numpy_obj, protocol=pickle.HIGHEST_PROTOCOL)
# 4. Encrypt the pickled data
encrypted_data = fernet.encrypt(pickled_data)
# 5. Upload to S3 along with the encrypted data key (in metadata)
s3_client = boto3.client("s3", region_name=region)
s3_client.put_object(
Bucket=bucket_name,
Key=s3_key,
Body=encrypted_data,
Metadata={
"encrypted_data_key": base64.b64encode(encrypted_data_key).decode("utf-8")
}
)
print(f"Encrypted pickle uploaded to s3://{bucket_name}/{s3_key}")
def download_and_unpickle_data_s3(
bucket_name: str,
s3_key: str,
region: str = "us-east-1"
) -> np.ndarray:
"""
Download the ciphertext and the encrypted data key from S3. Decrypt the data
key with KMS, use it to decrypt the pickled data, then load with a restricted
unpickler for safety.
"""
s3_client = boto3.client("s3", region_name=region)
# 1. Get object from S3
response = s3_client.get_object(Bucket=bucket_name, Key=s3_key)
# 2. Extract the encrypted data key from metadata
metadata = response["Metadata"]
encrypted_data_key_b64 = metadata.get("encrypted_data_key")
if not encrypted_data_key_b64:
raise ValueError("Missing encrypted_data_key in S3 object metadata.")
encrypted_data_key = base64.b64decode(encrypted_data_key_b64)
# 3. Decrypt data key via KMS
plaintext_key = decrypt_data_key(encrypted_data_key, region)
fernet = build_fernet_key(plaintext_key)
# 4. Decrypt the pickled data
encrypted_data = response["Body"].read()
decrypted_pickled_data = fernet.decrypt(encrypted_data)
# 5. Use restricted unpickler to load the numpy object
numpy_obj = restricted_loads(decrypted_pickled_data)
return numpy_obj
###############################################################################
# DEMO USAGE
###############################################################################
if __name__ == "__main__":
# --- Replace with your actual values ---
KMS_KEY_ID = "arn:aws:kms:us-east-1:123456789012:key/your-kms-key-id"
BUCKET_NAME = "your-secure-bucket"
S3_OBJECT_KEY = "encrypted_npy_demo.bin"
AWS_REGION = "us-east-1" # or region of your choice
# Example numpy array
original_array = np.random.rand(2, 3)
print("Original Array:")
print(original_array)
# Upload (pickle + encrypt) to S3
upload_pickled_data_s3(
numpy_obj=original_array,
bucket_name=BUCKET_NAME,
s3_key=S3_OBJECT_KEY,
kms_key_id=KMS_KEY_ID,
region=AWS_REGION
)
# Download (decrypt + unpickle) from S3
retrieved_array = download_and_unpickle_data_s3(
bucket_name=BUCKET_NAME,
s3_key=S3_OBJECT_KEY,
region=AWS_REGION
)
print("\nRetrieved Array:")
print(retrieved_array)
# Verify integrity
assert np.allclose(original_array, retrieved_array), "Arrays do not match!"
print("\nSuccess! The retrieved array matches the original array.")
Conclusion
With the rapid expansion of cloud technologies, integrating static code analysis into your AI/ML development process is increasingly important. While pickling offers a powerful way to serialize objects for AI/ML and LLM applications, you can mitigate potential risks by applying manual secure code reviews, setting up automated SCA with custom rules, and following best practices such as using alternative serialization methods or verifying data integrity.
When working with ML models on AWS, see the AWS Well-Architected Framework’s Machine Learning Lens for guidance on secure architecture and recommended practices. By combining these approaches, you can maintain a strong security posture and streamline the AI/ML development lifecycle.
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.
On March 24, 2025, Kubernetes disclosed 5 new vulnerabilities affecting the Ingress NGINX Controller for Kubernetes. Successful exploitation could allow attackers access to all secrets stored across all namespaces in the Kubernetes cluster, which could result in cluster takeover.
CVE-2025-1974 (9.8 Critical): RCE escalation. An unauthenticated attacker with access to the pod network can achieve arbitrary code execution in the context of the ingress-nginx controller. This can lead to disclosure of Secrets accessible to the controller. (In the default installation, the controller can access all Secrets cluster-wide.)
CVE-2025-24514(8.8 High): Configuration injection via unsanitized auth-url annotation. In ingress-nginx, the `auth-url` Ingress annotation can be used to inject configuration into nginx. This can lead to arbitrary code execution in the context of the ingress-nginx controller, and disclosure of Secrets accessible to the controller.
CVE-2025-1097 (8.8 High): Configuration injection via unsanitized auth-tls-match-cn annotation. The `auth-tls-match-cn` Ingress annotation can be used to inject configuration into nginx. This can lead to arbitrary code execution in the context of the ingress-nginx controller, and disclosure of Secrets accessible to the controller.
CVE-2025-1098 (8.8 High): Configuration injection via unsanitized mirror annotations. The `mirror-target` and `mirror-host` Ingress annotations can be used to inject arbitrary configuration into nginx. This can lead to arbitrary code execution in the context of the ingress-nginx controller, and disclosure of Secrets accessible to the controller.
CVE-2025-24513 (4.8 Medium): Auth secret file path traversal vulnerability. Attacker-provided data is included in a filename by the ingress-nginx Admission Controller feature, resulting in directory traversal within the container. This could result in denial of service, or when combined with other vulnerabilities, limited disclosure of Secret objects from the cluster.
Of the 5 vulnerabilities disclosed, any one of the injection vulnerabilities (CVE-2025-24514, CVE-2025-1097, CVE-2025-1098) may be chained with CVE-2025-1974 to achieve unauthenticated RCE on the Kubernetes pod that is running a vulnerable Ingress NGINX Controller. Achieving RCE could allow an attacker to take over a Kubernetes cluster. As of March 25, 2025, none of the above CVEs is known to be exploited in the wild.
Ingress is a Kubernetes feature to route HTTP(S) traffic into a Kubernetes cluster. An Ingress Controller is an application responsible for performing the routing. While there are many Ingress Controllers available, the vulnerabilities disclosed on March 24 are specific to the Ingress NGINX Controller, which is an Ingress Controller based upon NGINX.
The original finders of all five vulnerabilities, Wiz, noted that 43% of cloud environments are vulnerable to the issues disclosed, and that they have identified 6,500 clusters with publicly exposed Ingress NGINX Controllers.
As of March 25, 2025 (14:00 pm GMT), there is now one known publicly available RCE exploit for CVE-2025-1974 (here). This exploit is unverified, but based on our understanding of the vulnerability, it appears viable.
Mitigation guidance
All 5 vulnerabilities are reported to affect the following versions of Ingress NGINX Controller:
Versions <= 1.11.4
Version 1.12.0
Notably, the Wiz advisory says that CVE-2025-24514 does not affect version 1.12.0, but the vendor indicates that the issue does affect 1.12.0.
Customers who use the Ingress NGINX Controller for Kubernetes are advised to update to the following versions immediately:
Version 1.11.5
Version 1.12.1
Rapid7 customers
With the latest Kubernetes Cluster Scanner (expected to be available Wednesday, March 26), InsightCloudSec customers will have the ability to discover Kubernetes workloads that have this vulnerability in their cluster. The discovery will be shown via the insights pack with a new insight called Publicly exposed vulnerable Ingress NGINX Admission. The insight will also include the remediation steps needed in order to resolve this vulnerability.
By implementing the PBHVA control overlay over a CCCS Medium baseline, you can better protect your organization’s most critical assets from potential threats and vulnerabilities, providing continuity of essential government operations and safeguarding sensitive information.
Understanding CCCS PBHVA overlay requirements
The CCCS PBHVA overlay consists of 137 controls designed to protect high-value assets, including 69 new controls and 68 controls from CCCS Medium. These controls provide enhanced data protection, particularly for integrity and availability, and are based on NIST SP 800-53 Revision 5.
Key findings from the Coalfire assessment
Coalfire’s assessment found that the LZA on AWS solution significantly supports CCCS PBHVA overlay compliance requirements:
71 percent of in-scope controls (97 of 137) are supported by the AWS contribution to compliance in the shared responsibility model
The solution uses over 35 AWS services to provide comprehensive security capabilities
Strong network segmentation is achieved through network account and network-boundary VPC design
Infrastructure-as-code (IaC) enables reliable build and deployment results
The 29 percent of controls not addressed by the LZA are on the customer side of the shared responsibility model. They are addressed in the customer’s application stack or as non-technical controls such as policies and procedures.
Key security capabilities
The LZA solution implements several critical security features:
While the LZA solution provides significant compliance support, organizations should note:
The solution alone does not guarantee compliance
Organizations must implement their own policies, standards, and procedures
A thorough understanding of the shared responsibility model is essential
The AWS Landing Zone Accelerator Verified Reference Architecture documentation is available for customer download in AWS Artifact. This resource can help organizations reduce the time and effort required to deploy an environment that aligns with CCCS PBHVA overlay requirements.
Conclusion
The Coalfire assessment confirms that the LZA on AWS solution provides effective support for CCCS PBHVA overlay compliance objectives. However, organizations should remember that compliance is an ongoing process that requires active management and cannot be achieved through technology alone.
For more information about implementing the Landing Zone Accelerator for CCCS PBHVA overlay requirements, contact your AWS account team or the AWS Public Sector team directly.
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.
If you’d like to skip directly to the detailed mapping between the CISA guidance and AWS security controls and best practices, visit our Github page.
Implementing CISA’s enhanced visibility and hardening guidance for communications infrastructure
In response to recent cybersecurity incidents attributed to actors from the People’s Republic of China, a number of cybersecurity agencies led by the U.S. Cybersecurity and Infrastructure Security Agency (CISA) have jointly released comprehensive guidance for securing communications infrastructure. As communications service providers (CSPs) migrate their workloads to the cloud, they must take steps to implement these security measures effectively in cloud environments.
This blog post describes how CSPs can use Amazon Web Services (AWS) capabilities to implement this guidance while benefiting from the advantages of the cloud.
The guidance focuses on two key domains:
Strengthening visibility: Enabling security teams to monitor, detect, and respond to potential threats through comprehensive visibility into digital assets
Hardening systems and devices: Implementing robust security controls and configurations to minimize vulnerabilities and help prevent unauthorized access
Overview of fundamental cloud concepts
Before exploring the specific guidance in this post, it’s important to understand how security recommendations apply differently to public cloud environments than to private infrastructure. A common tendency in the telecommunications industry is to treat public clouds as merely scaled-up versions of private clouds. This can result in a misunderstanding of security capabilities and underutilization of cloud-native security features of the public cloud.
The fundamental difference lies in how public clouds are architected—specifically for multi-tenancy, with strong tenant isolation as a cornerstone of their design. In AWS, virtual resources are isolated by default and require explicit configuration to interconnect. For example, when you create a virtual private cloud (VPC) with Amazon VPC, this logically isolated network does not permit inbound or outbound traffic until specific routes and ports are deliberately configured. Similarly, Amazon Simple Storage Service (Amazon S3) buckets are private by default, requiring explicit configuration to grant access. This isolation extends to the core of our virtualization infrastructure through the AWS Nitro System, which provides unprecedented workload isolation—even AWS operators with the highest privileges have no technical access to customer workloads. Furthermore, data moving between Nitro System based virtual machines or across our global backbone network is automatically encrypted, providing additional layers of protection beyond customer-implemented encryption.
This secure-by-design and secure-by-default philosophy permeates throughout AWS service design and operations. It isn’t merely a design choice—it’s a business imperative driven by the critical need for operational resilience and customer trust in the public cloud model. Our commitment to principles of this sort is reflected in our participation as a signatory to CISA’s Secure by Design Pledge.
When AWS customers operate in the public cloud, understanding the shared responsibility model is paramount. This model clearly delineates security responsibilities: AWS is responsible for security of the cloud, while customers are responsible for security in the cloud. This division of responsibilities significantly reduces your operational burden, because AWS assumes responsibility for securing everything about and inside the cloud services it provides, all the way down to the physical protection of data centers. As a result, you can concentrate your security resources where they matter most—protecting your applications and workloads—while AWS handles the undifferentiated heavy lifting of infrastructure security.
This shared responsibility model becomes even more advantageous when considering the economies of scale inherent to public cloud operations. The massive scale of AWS allows us to invest more in securing the foundations than a single enterprise could achieve independently, creating a security multiplier effect that benefits all customers. A compelling example of this scale advantage is our comprehensive threat intelligence program, which deploys honeypot sensors throughout our global network. These sensors observe more than 100 million potential threat interactions and probes daily. Using artificial intelligence and machine learning (AI/ML), we analyze this information and take swift, often automated actions to mitigate threats. In the first half of 2023 alone, this program enabled us to dismantle the sources of approximately 230,000 Layer 7 distributed denial of service (DDoS) events. We also provide this intelligence to customers through services like Amazon GuardDuty, extending the benefits of our scale to our customers.
The scale of AWS operations not only enables exemplary threat intelligence, but also necessitates extensive automation of our security operations. Several routine tasks, such as feature and patch deployments and configuration updates, are fully automated through deployment pipelines. Automation has the added benefit of taking humans out of the loop, thereby decreasing opportunities for mistakes.
Our scale also facilitates our comprehensive compliance with security standards across multiple industries and jurisdictions. Our global presence and diverse customer base necessitate adherence to the most stringent security requirements worldwide. Through the AWS Compliance Program, we’ve achieved 143 security standards and compliance certifications, ranging from ISO standards for cloud security and privacy to industry-specific regulations in finance and healthcare, as well as government security programs. This includes independent verification of our claims on the isolation properties of our Nitro System virtualization infrastructure. Consequently, you benefit from this scale-driven compliance, gaining access to a secure, certified infrastructure that implements state-of-the-art security systems.
These are a few reasons why, in a blog post titled Why cloud first is not a security problem, the UK’s National Cyber Security Centre concluded that “using the cloud securely should be your primary concern – not the underlying security of the public cloud.”
Private clouds, on the other hand, are typically within the control of a single organization and are single-tenanted, offering relatively weak workload isolation. Virtual resources in private clouds usually default to being interconnected upon creation, and so require explicit steps to increase isolation. Manual operations, with their opportunities for mistakes as well as potential involvement of threat actors, are often still a large part of private cloud workflows. Rarely do they undergo the level of security scrutiny that public clouds are routinely subjected to. These, and other differences, mean that security risks in each offering are inherently different, so correspondingly distinct security controls and solution architectures are needed to mitigate these risks.
Implementing hardening guidance with AWS
Your cloud resources and data are contained in an AWS account. An account acts as an identity and access management isolation boundary. When you need to share resources and data between two accounts, you must explicitly allow this access. This reduces the risk of lateral movement between accounts.
Designing your AWS environment correctly lays a strong foundation that can help you meet the CISA cybersecurity guidance. AWS Control Tower, working with AWS Organizations, enables you to establish a well-architected, multi-account environment based on security best practices.
For detailed guidance on creating a secure landing zone for telecom workloads, refer to our comprehensive blog post on this topic.
We’ve analyzed the recommendations in CISA’s guidance and grouped them into six categories across the two key domains. Refer to the GitHub page linked at the bottom of this post, which has further detailed guidance on the relevant AWS services that can assist your implementation of each individual security measure in the guidance.
Logging and monitoring
The guidance in this category emphasizes the importance of increasing visibility to understand network activity, which is essential for detecting anomalies and responding to incidents. Key security controls include the following: have a robust asset management capability, enable logging at various levels, centralize logging, protect the logging and monitoring infrastructure, and use security tools to detect anomalies and incidents.
Enhanced visibility is an inherent advantage of the public cloud model, particularly in AWS. This transparency is not just a bolted-on feature, but a fundamental necessity driven by the API-centric, pay-as-you-go business model. To accurately bill customers, AWS has built comprehensive tracking and logging capabilities into its core architecture. As a result, AWS provides robust tools that allow you to capture, centralize, and monitor logs across every layer of your network workload. This level of visibility extends far beyond what’s typically achievable in traditional infrastructure, offering you unprecedented insight into your IT assets and user activities.
Another key guidance is this area is to centralize security-related logging while isolating the logging infrastructure from other production environments. You can implement this guidance in AWS by using Amazon Security Lake together with OpenSearch implemented in separate accounts, with access restricted to just the security organization. Alternatively, this solution provides a best-practice implementation of creating collection and ingestion pipelines to allow for centralization and inspection of log sources across your AWS workloads without the use of Security Lake.
Configuration and change management
The guidance in this category emphasizes the centralization, security, and protection of configurations. It highlights the importance of detecting and providing alerts for unauthorized modifications, auditing configurations for compliance, and a change management process that automates routine changes to minimize unintended drift.
In AWS, you can implement infrastructure and configuration as code, which allows for central storage of configuration in repositories, tracking changes through version control, and implementing changes through approved change management processes. You can use code repositories and continuous integration and continuous delivery (CI/CD) pipelines to automate the implementation of these configurations, helping you increase deployment speed, maintain consistency, simplify management, and implement a rigorous and auditable change control process.
Regardless of how infrastructure is deployed and managed, you can use the AWS Config service to automatically track the current state and history of a wide set of configuration information across more than 100 AWS services and hundreds of their resource types. You can also write custom AWS Config rules to take automatic actions whenever sensitive resources are modified, or take advantage of more than 400 AWS managed rules in AWS Config that send alerts or create automatic remediations when critical resources change state.
Identity and access management
The guidance in this category emphasizes the importance of active account and permissions management, use of phishing-resistant authentication methods, implementing least privilege through role-based access controls, managing emergency access, and limiting sessions.
Authentication and authorization, which are critical components of access control, are managed through AWS Identity and Access Management (IAM), AWS IAM Identity Center, and AWS Organizations. AWS provides you with capabilities to manage permissions at scale across identities, resources, and services, including mandating the use of multi-factor authentication (MFA) for logins. Furthermore, these capabilities support customers adhering to the principle of least privilege by encouraging time-bound, session-based credential management by using AWS Security Token Service (AWS STS).
The guidance in this category emphasizes segmenting workloads and networks to limit the potential for lateral movement and exposure to the internet, monitoring and regulating traffic flows by using policies, and securing unused ports.
You can achieve network micro-segmentation, a critical aspect of modern security architecture, through VPCs and subnets. You can, for example, segregate internet-facing components of your application from those that don’t require such access by placing them in separate VPCs and enabling internet access only on the VPC that requires it. You can control traffic flows within and between segments by using a variety of network services—routing tables, internet gateways, transit gateways, and firewall services, to name a few. This segmentation minimizes your risk from unauthorized activity that originates from the internet and limits the potential for lateral movement in the event of a breach.
To implement the guidance regarding out-of-band management, you can architect your network connections to separate management traffic from network signaling traffic by using subnets—for example, a single EC2 instance can have multiple elastic network interfaces (ENIs) attached to different subnets or even different VPCs: one that permits only management traffic and another that permits only signaling traffic.
Strong cryptography to encrypt data at rest and in traffic
The guidance in this category emphasizes using strong cipher suites, secure versions of encryption protocols, and PKI-based certificates to protect data at rest and in transit.
Encryption, a cornerstone of data protection, is comprehensively addressed in AWS offerings. API endpoints of AWS services support TLS 1.3 (and a minimum of TLS 1.2) with secure standards-based cipher suites, encryption keys, and advanced security features like HKDF (HMAC-based extract-and-expand key derivation function) for added security. AWS services that manage customer secrets sent over the wire also support post-quantum cryptography. For example, AWS Key Management Service (AWS KMS), AWS Certificate Manager, and AWS Secrets Manager support a hybrid post-quantum key exchange option for the TLS network encryption protocol. In its use of the Border Gateway Protocol (BGP), AWS uses Resource Public Key Infrastructure (RPKI) and Route Origin Authorization (ROA) to protect the Amazon IP address space and routes from misconfigurations and hijacking.
You can also use AWS cryptographic services such as AWS KMS, AWS CloudHSM, and AWS Certificate Manager to help secure your data in transit and at rest. Keys that you create in AWS KMS are protected by FIPS 140-2 Level 3 validated hardware security modules (HSMs), and there is no mechanism for anyone, including AWS service operators, to view, access, or export plaintext key material.
AWS Secrets Manager helps you securely manage, retrieve, and rotate database credentials, application credentials, OAuth tokens, API keys, and other secrets throughout their lifecycles. For more details on AWS cryptography solutions and best practices, refer to Encryption best practices for AWS services.
Vulnerability management
This guidance emphasizes minimizing exploitation risks through proper lifecycle management, regular patching, and elimination of insecure protocols. AWS helps address these requirements through both shared responsibility and innovative architectural approaches.
Under the shared responsibility model, AWS manages the security of underlying infrastructure. This includes maintaining up-to-date systems and services, disabling insecure protocols and unused ports, and providing Security Bulletins for timely vulnerability notifications. AWS services are supported through contractually defined terms, so that you don’t need to worry about end-of-life infrastructure components.
For your applications, AWS enables a transformative approach to vulnerability management through ephemeral resources and immutable infrastructure. Instead of maintaining long-lived instances that require continuous patching, you can maintain a single, hardened, and frequently updated Amazon Machine Image (AMI) as your golden image. When updates are needed, rather than patching running instances, you simply deploy new instances with your application code installed from an updated AMI. Similar approaches also apply to container-based workloads. Workloads based on AWS Lambda reduce your patching responsibility even further, because only the code that contains your business logic (and any supporting layers you have chosen) needs to be updated—AWS patches the underlying hypervisors, operating systems, and containers automatically. This approach enables you to keep your systems in a known, secure state while reducing both the threat surface and operational overhead. You can further enhance security by using AWS networking features like security groups to disable insecure protocols, such as enforcing HTTPS rather than HTTP.
Conclusion
The comprehensive guidance from cybersecurity agencies provides a crucial framework for securing telecommunications infrastructure. As demonstrated throughout this post, AWS offers a robust set of native services and capabilities that align with the recommendations from CISA and allied governments. From enhanced visibility through logging and monitoring, to strong identity management, network segmentation, encryption, and vulnerability management, AWS provides the tools you need to implement these security controls effectively while maintaining operational efficiency. The shared responsibility model, combined with AWS continuous innovation in security, enables telecommunications companies to build and maintain resilient, secure cloud environments.
We continue to expand the scope of our assurance programs at Amazon Web Services (AWS) and are pleased to announce the successful completion of our first ever Protected B High Value Assets (PBHVA) assessment with 149 assessed services and features. Completion of this assessment effective October 4, 2024, makes AWS the first cloud service provider (CSP) in Canada to meet this high security bar and provide assurance to our valued customers. This assessment also re-affirms our commitment to helping public and commercial customers achieve and maintain the highest-grade security standard for workloads with increased sensitivity.
What is the PBHVA assessment and why is it important?
The Protected B High Value Asset (PBHVA) overlay seeks to enhance the integrity and availability of customer organizational workloads that are considered to have an increased level of sensitivity. These are systems that the Government of Canada (GC) and its service providers use to support delivery of services at a national scale or that are determined to be significant for handling sensitive information. The overlay is a set of 117 controls from the ITSG-33 security control catalogue (baselined against NIST 800-53), which augments the security safeguards to enhance integrity and availability.
As of October 4, 2024, there are a total of 149 AWS services and features that were assessed by the Canadian Centre for Cyber Security (CCCS) under PBHVA assessment criteria. The assessment covers services and features that are available in both the Canada (Central) and Canada West (Calgary) AWS Regions.
How can you access the assessment?
The summary assessment is available through AWS Artifact. You can also learn more about the PBHVA assessment on our AWS PBHVA webpage.
AWS strives to continuously bring services into scope of its compliance programs to help you meet your architectural and regulatory needs. Please reach out to your AWS account team if you have questions or feedback about the PBHVA assessment.
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.
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The cloud has become the backbone of modern innovation, powering everything from AI to remote work. But as organizations embrace the cloud, they also face an ever-expanding and increasingly complex attack surface. With purpose-built harvesting technology providing real-time visibility into everything running across multi-cloud environments, Exposure Command from Rapid7 ensures teams have an up-to-date inventory, mapping their cloud attack surface and enriching asset data with risk and business context.
To ensure teams can keep up with the torrid pace of innovation and overcome increased complexity, Rapid7 remains dedicated to investing in advancing the cloud security capabilities available within Exposure Command. To that end, we’ve made a few significant updates across AI resource coverage, third-party CNAPP enrichment and more. Let’s dive right in.
Extending coverage for securing AI/ML development in the cloud
AI and machine learning (ML) are transforming industries, but the speed of adoption can often leave organizations vulnerable. AI/ML workloads often process sensitive or proprietary data, requiring robust protections to ensure compliance with ever-evolving regulations. Safeguarding these environments isn’t just about securing the infrastructure; it’s about understanding the unique workflows and ensuring compliance at every step.
These workloads also introduce unique risks, such as model poisoning attacks or vulnerabilities in APIs, creating new vectors for data exfiltration and service disruption. Additionally, the dynamic nature of cloud-hosted AI services presents challenges in maintaining secure configurations as resources scale elastically, potentially exposing sensitive endpoints or misconfigured setups.
To that end, Exposure Command has expanded support for critical AI services like Amazon Comprehend and Polly, AWS’s natural language processing and text-to-speech services.This provides comprehensive visibility across an organization’s attack surface, aligning AI-specific risks with broader enterprise priorities.
Shifting left and securing the software supply chain
Developers are at the forefront of modern cloud environments, making “shift-left” strategies essential for effective security. By addressing risks during development rather than after deployment, teams can eliminate vulnerabilities before they become costly issues.
Exposure Command now offers more robust Infrastructure-as-Code (IaC) scanning and deeper CI/CD integration, with Terraform and CloudFormation support across hundreds of resource types. For development teams, integrations like GitLab, GitHub Actions, AWS CloudFormation, and Azure DevOps bring security checks directly into their workflows. Whether it’s identifying misconfigurations in AWS Glue Catalogs or assessing risks in SES configurations, these tools help teams secure their code without breaking their stride.
Bridging the hybrid cloud gap with native and third-party CNAPP connectors
For many organizations, the challenge isn’t just securing the cloud – it’s securing everything holistically. Hybrid environments that span on-prem systems and multiple cloud providers can create silos, leading to gaps in visibility and risk management. To tackle this, we’ve integrated InsightCloudSec data directly into Surface Command, empowering security teams with a unified view of their entire attack surface in one place.
But we didn’t stop at consolidating our own native CNAPP capabilities. Teams now get out-of-the-box integrations with popular cloud security tools like Wiz and Orca as well as CSP-native services like AWS Inspector, all making it easier than ever to identify risks across cloud-native and hybrid environments. Everything can now be seen in one place – from endpoint vulnerabilities to cloud misconfigurations and overly permissive roles – allowing for faster action with clarity and precision.
Tackling virtual desktop risks with custom registry keys
With the rise of remote work, virtual desktop infrastructures (VDIs) like AWS Workspaces have become essential. Yet, their dynamic nature makes tracking vulnerabilities a challenge. Exposure Command addresses this with features like custom registry keys for golden images, ensuring you can trace a risk back to its source and effectively prioritize remediation.
Commanding the cloud attack surface
The challenges of securing modern environments aren’t going away. Attack surfaces will continue to expand, threats will grow more sophisticated, and organizations will face increasing pressure to innovate securely.
Keep an eye out for more updates coming soon as we continue to invest in helping organizations effectively manage exposures from endpoint to cloud.
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