Post Syndicated from The History Guy: History Deserves to Be Remembered original https://www.youtube.com/watch?v=BSIrhOxYQq0
Yearly Archives: 2024
Comic for 2024.10.22 – Harry Potter Scar
Post Syndicated from Explosm.net original https://explosm.net/comics/harry-potter-scar
New Cyanide and Happiness Comic
170 AWS services achieve HITRUST certification
Post Syndicated from Mark Weech original https://aws.amazon.com/blogs/security/170-aws-services-achieve-hitrust-certification/
Amazon Web Services (AWS) is excited to announce that 170 AWS services have achieved HITRUST certification for the 2024 assessment cycle, including the following 12 services that were certified for the first time:
- AWS AppFabric
- AWS Application Migration Service
- Amazon Bedrock (Including the Titan Model)
- AWS Clean Rooms
- Amazon DataZone
- AWS Entity Resolution
- AWS HealthImaging
- AWS IoT Device Defender
- AWS IoT TwinMaker
- Amazon Managed Grafana
- AWS Resilience Hub
- AWS User Notifications
The full list of AWS services, which a third-party assessor audited and certified under the HITRUST CSF, is now available on our Services in Scope by Compliance Program page. Customers can view and download our 2024 HITRUST certification on demand through AWS Artifact.
AWS HITRUST certification is available for customer inheritance
As an added benefit to our customers, organizations no longer have to assess inherited controls for their HITRUST validated assessment because AWS already has. You can deploy business solutions to the AWS Cloud and inherit our HITRUST certification, provided that you use only in-scope services and properly apply the controls detailed on the HITRUST website according to the AWS Shared Responsibility Model.
Our HITRUST certification is based on the version 11.2 control framework, so you can inherit the latest controls and related scoring, knowing that AWS has attested to the latest framework standards available. Leading organizations in a variety of industries have adopted HITRUST CSF as part of their approach to security and privacy. For more information, see the HITRUST website.
As always, we value your feedback and questions and are committed to helping you achieve and maintain the highest standard of security and compliance. Feel free to contact the team through AWS Compliance Support. If you have feedback about this post, submit comments in the Comments section below.
Meta Brings AMD EPYC Turin to Yosemite v4
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/meta-brings-amd-epyc-turin-to-yosemite-v4/
It looks like the Meta Yosemite v4 platform will have an AMD EPYC 9005 Turin module that is CXL enabled for 2025 deployment
The post Meta Brings AMD EPYC Turin to Yosemite v4 appeared first on ServeTheHome.
Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job
Post Syndicated from Ramakant Joshi original https://aws.amazon.com/blogs/big-data/demystify-data-sharing-and-collaboration-patterns-on-aws-choosing-the-right-tool-for-the-job/
Data is the most significant asset of any organization. However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture.
In this context, the adoption of data lakes and the data mesh framework emerges as a powerful approach. By decentralizing data ownership and distribution, enterprises can break down silos and enable seamless data sharing. Cataloging data, making the data searchable, implementing robust security and governance, and establishing effective data sharing processes are essential to this transformation. AWS offers services like AWS Data Exchange, AWS Glue, AWS Clean Rooms and Amazon DataZone to help organizations unlock the full potential of their data.
Personas
Let’s identify the various roles involved in the data sharing process.
First of all, there are data producers, which might include internal teams/systems, third-party producers, and partners. The data consumers include internal stakeholders/systems, external partners, and end-customers. At the core of this ecosystem lies the enterprise data platform. When considering enterprises, numerous personas come into play:
- Line of business users – These personas need to classify data, add business context, collaborate effectively with other lines of business, gain enhanced visibility into business key performance indicators (KPIs) for improved outcomes, and explore opportunities for monetizing data
- Partners – Partners should be able to share data, collaborate with other partners and customers.
- Data scientists and business analysts – These personas should be able to access the data, analyze it and generate actionable business insights
- Data engineers – Data engineers are tasked with building the proper data pipeline and cataloging the data that meets the diverse needs of stakeholders, including business analysts, data scientists, partners, and line of business users
- Data security and governance officers – Data security involves making sure producers and consumers have appropriate access to the data, implementing right access permissions, and maintaining compliance with industry regulations, particularly in highly regulated sectors like healthcare, life sciences, and financial services. This persona is also responsible for enhancing data governance by tracking lineage, and establishing data mesh policies
Choosing the right tool for the job
Now that you have identified the various personas, it’s important to select the appropriate tools for each role:
- Starting with the producers, if your data source includes a software as a service (SaaS) platform, AWS Glue offers options to automate data flows between software service providers and AWS services.
- For producers seeking collaboration with partners, AWS Clean Rooms facilitates secure collaboration and analysis of collective datasets without the need to share or duplicate underlying data.
- When dealing with third-party data sources, AWS Data Exchange simplifies the discovery, subscription, and utilization of third-party data from a diverse range of producers or providers. As a producer, you can also monetize your data through the subscription model using AWS Data Exchange.
- Within your organization, you can democratize data with governance, using Amazon DataZone, which offers built-in governance features.
- For SaaS consumers, AWS Glue supports bidirectional transfer and serves both as a producer and consumer tool for various SaaS providers.
Let’s briefly describe the capabilities of the AWS services we referred above:
AWS Glue is a fully managed, serverless, and scalable extract, transform, and load (ETL) service that simplifies the process of discovering, preparing, and loading data for analytics. It provides data catalog, automated crawlers, and visual job creation to streamline data integration across various data sources and targets.
AWS Data Exchange enables you to find, subscribe to, and use third-party datasets in the AWS Cloud. It also provides a platform through which a data producer can make their data available for consumption for subscribers. It is a data marketplace featuring over 300 providers offering thousands of datasets accessible through files, Amazon Redshift tables, and APIs. This service supports consolidated billing and subscription management, offering you the flexibility to explore 1,000 free datasets and samples. You don’t need to set up a separate billing mechanism or payment method specifically for AWS Data Exchange subscriptions.
AWS Clean Rooms is designed to assist companies and their partners in securely analyzing and collaborating on collective datasets without revealing or sharing underlying data. You can swiftly create a secure data clean room, fostering collaboration with other entities on the AWS Cloud to derive unique insights for initiatives such as advertising campaigns or research and development. This service protects underlying data through a comprehensive set of privacy-enhancing controls and flexible analysis rules tailored to specific business needs.
Amazon DataZone is a data management service that makes it fast and straightforward to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. With Amazon DataZone, administrators and data stewards who oversee an organization’s data assets can manage and govern access to data using fine-grained controls. These controls are designed to grant access with the right level of privileges and context. Amazon DataZone makes it straightforward for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization so they can discover, use, and collaborate to derive data-driven insights.
Use cases
Let’s review some example use cases to understand how these diverse services can be effectively applied within a business context to achieve the desired outcomes. In this particular scenario, we focus on a company named AnyHealth, which operates in the healthcare and life sciences sector. This company encompasses multiple lines of businesses, specializing in the sale of various scientific equipment. Three key requirements have been identified:
- Sales and customer visibility by line of business – AnyHealth wants to gain insights into the sales performance and customer demands specific to each line of business. This necessitates a comprehensive view of sales activities and customer requirements tailored to individual lines of business.
- Cross-organization supply chain and inventory visibility – The company faces challenges related to supply chain and inventory management, especially in global crisis situations like a pandemic. They want to address instances where inventory items are idle in one line of business while there is demand for the same items in another. To overcome this, they want to establish cross-organizational visibility of supply chain and inventory data, breaking down silos and achieving prompt responses to business demands.
- Cross-sell and up-sell opportunities – AnyHealth intends to boost sales by implementing cross-selling and up-selling strategies. To achieve this, they plan to use machine learning (ML) models to extract insights from data. These insights will then be provided to sales representatives and resellers, enabling them to identify and capitalize on opportunities effectively.
In the following sections, we discuss how to address each requirement in more detail and the AWS services that best fit each solution.
Sales and customer visibility by line of business
The first requirement involves obtaining visibility into sales and customer demand by line of business. The key consumers of this data include line of business leaders, business analysts, and various other business stakeholders.
The initial step is to ingest sales and order data into the platform. Currently, this data is centralized in the ERP system, specifically SAP. The objective is to regularly retrieve this data and capture any changes that occur. The data engineers are instrumental in building this pipeline. Given that we are dealing with a SaaS integration, AWS Glue is the logical choice for seamless data ingestion.
Next, we focus on building the enterprise data platform where the accumulated data will be hosted. This platform will incorporate robust cataloging, making sure the data is easily searchable, and will enforce the necessary security and governance measures for selective sharing among business stakeholders, data engineers, analysts, security and governance officers. In this context, Amazon DataZone is the optimal choice for managing the enterprise data platform.
As stated earlier, the first step involves data ingestion. Data is ingested from a third-party vendor SaaS solution (SAP), and the data engineer uses AWS Glue. Utilizing the SAP data connector, the data engineer establishes a connection with the SAP environment, running scheduled jobs.
The data lands in Amazon Simple Storage Service (Amazon S3). Additional AWS Glue jobs are created to transform and curate the data. The curated data is placed in a designated bucket and AWS Glue crawlers are run to catalog the data. This cataloged data is then managed through Amazon DataZone.
In Amazon DataZone, the data security officer creates the corporate domain. She/he creates producer projects and enables access to data engineers, and business analysts. Data engineers ensure sales and customer data is available from the source into the Amazon DataZone project. Business analysts enhance the data with business metadata/glossaries and publish the same as data assets or data products. The data security officer sets permissions in Amazon DataZone to allow users to access the data portal. Users can search for assets in the Amazon DataZone catalog, view the metadata assigned to them, and access the assets.
Amazon Athena is used to query, and explore the data. Amazon QuickSight is used to read from Amazon Athena and generate reports that is consumed by the line of business users and other stakeholders.
The following diagram illustrates the solution architecture using AWS services.

Cross-organization supply chain and inventory visibility
For the second requirement, the objective is to achieve visibility of supply chain and inventory across the organization. The key stakeholders remain line of business users. They would like to get a cross-organization visibility of supply chain and inventory data. The aim is to ingest supply chain and inventory information in a scheduled manner from the ERP system (SAP), and also capture any changes in the supply chain and inventory data. The persona involved in setting up the data ingestion pipeline is a data engineer. Given that we are extracting data from SAP, AWS Glue is the suitable choice for this requirement.
The next step involves obtaining economic indicators and weather information from third-party sources. AnyHealth, with its diverse lines of business, including one that manufactures medical equipment such as inhalers for asthma treatment, recognizes the significance of collecting weather information, particularly data about pollen, because it directly impacts the patient population. Additionally, socioeconomic conditions play a crucial role in government-assisted programs related to out-of-hospital care. To incorporate this third-party data, AWS Data Exchange is the logical choice.
Finally, all the accumulated data needs to be hosted on the enterprise data platform, with cataloging, and robust security and governance measures. In this context, Amazon DataZone is the preferred solution.
The pipeline begins with the ingestion of data from SAP, facilitated by AWS Glue. The data lands in Amazon S3, where AWS Glue jobs are used to curate the data, generate curated tables, and then AWS Glue crawlers are used to catalog the data.
AWS Data Exchange serves as the platform for collecting economic trends and weather information. The business analyst leverages AWS Data Exchange to retrieve data from various sources. In the AWS Data Exchange marketplace, they identify the data set, subscribe to the data, and subsequently consume it. Any changes in the source data invokes events, which updates the data object in the Amazon S3 bucket.
Amazon DataZone is used to manage and govern the datalake. Similar to the first use case, the data security officer creates a producer project. The data owner from LoB creates supply chain and inventory data assets in the producer project and publishes the same. From the consumer perspective, the data security officer also creates a consumer project, which allows the sales and marketing teams from different LoBs to search for the supply chain and inventory data published by the producer. Consumers request access to the published supply chain and inventory data, and the producer grants the necessary access. Amazon Athena is used to query, and explore the data. Amazon QuickSight is used to read from Amazon Athena and generate reports.
The following diagram illustrates this architecture.

Cross-sell and up-sell opportunities
The third requirement involves identifying cross-sell and up-sell opportunities. The key business consumers in this context are the sales representatives and resellers. AnyHealth operates globally, selling products in Europe, America, and Asia. Direct business transactions with consumers occur in America and Europe, and resellers facilitate sales in Asia, where AnyHealth lacks a direct relationship with the consumers.
The enterprise data platform is used to host and analyze the sales data and identify the customer demand. This data platform is managed by Amazon Data Zone. Cross-sell and up-sell opportunities, derived through ML models, are integrated into the customer relationship management (CRM) system, which in this case is Salesforce. Sales representatives access this data from Salesforce to engage with the market and collaborate with customers. AWS Glue is used for this integration.
Typically, resellers don’t provide their partners direct access to their customer data. Although AnyHealth doesn’t have direct access, understanding customer personas and profile information is essential to equip resellers with right offers to cross-sell and up-sell products. AWS Clean Rooms enables collaboration on collective datasets with stringent security controls, enabling insights without sharing the underlying data.
By addressing these requirements, AnyHealth can effectively identify and capitalize on cross-sell and up-sell opportunities, tailoring their approach based on the distinct dynamics of direct and reseller-based business models across various regions.
The initial step in the architecture involves a pipeline where SAP data is ingested into Amazon S3 and curated using AWS Glue job. The curated data is cataloged, governed and managed using Amazon DataZone.
In this scenario, where sales and customer information are acquired, data scientists build ML models to identify cross-sell and upsell opportunities. Using Amazon DataZone, these opportunities are shared with line of business users, providing transparency regarding the opportunities presented to sales reps and resellers. The cross-sell and upsell insights are pushed to Salesforce through AWS Glue, with an event-driven workflow for timely communication to sales reps. However, for resellers, a different pipeline is needed as AnyHealth doesn’t have direct access to the customer sales data. AnyHealth uses AWS Clean Rooms for this purpose.
With AWS Clean Rooms, the collaboration is started by AnyHealth (the collaboration initiator) who invites resellers to join. Resellers participate in the collaboration, and share the customer profile and segment information, while maintaining privacy by excluding customer names and contact details. AnyHealth uses the customer profile information and order trends to identify cross-sell and upsell opportunities. These opportunities are shared with the reseller to pursue further and position products in the market.
The following diagram illustrates this architecture.

Final architecture
Let’s now examine the complete architecture which covers all three use cases. In this architecture, purpose-built services like AWS Data Exchange, AWS Glue, AWS Clean Rooms and Amazon DataZone, have been used. The seamless integration of these services works cohesively to achieve end-to-end business objectives.
The following diagram illustrates this architecture.

To strengthen the security posture of your cloud infrastructure, we recommend using AWS Identity and Access Management (IAM), which allows you to manage access to AWS resources by creating users, groups, and roles with specific permissions. Additionally, you can use AWS Key Management Service (AWS KMS), which enables you to create, manage, and control encryption keys used to protect your data, so only authorized entities can access sensitive information. To provide an audit trail for compliance, you can use AWS CloudTrail, which records API calls made within your AWS account.
Conclusion
In this post, we discussed how to choose right tool for building an enterprise data platform and enabling data sharing, collaboration and access within your organization and with third-party providers. We addressed three business use cases using AWS Glue, AWS Data Exchange, AWS Clean Rooms, and Amazon DataZone through three different use cases.
To learn more about these services, check out the AWS Blogs for Amazon DataZone, AWS Glue, AWS Clean Rooms, and AWS Data Exchange.
About the authors
Ramakant Joshi is an AWS Solutions Architect, specializing in the analytics and serverless domain. He has a background in software development and hybrid architectures, and is passionate about helping customers modernize their cloud architecture.
Debaprasun Chakraborty is an AWS Solutions Architect, specializing in the analytics domain. He has around 20 years of software development and architecture experience. He is passionate about helping customers in cloud adoption, migration and strategy.
AWS Weekly Roundup: Agentic workflows, Amazon Transcribe, AWS Lambda insights, and more (October 21, 2024)
Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-agentic-workflows-amazon-transcribe-aws-lambda-insights-and-more-october-21-2024/
Agentic workflows are quickly becoming a cornerstone of AI innovation, enabling intelligent systems to autonomously handle and refine complex tasks in a way that mirrors human problem-solving. Last week, we launched Serverless Agentic Workflows with Amazon Bedrock, a new short course developed in collaboration with Dr. Andrew Ng and DeepLearning.AI.
This hands-on course, taught by my colleague Mike Chambers, teaches how to build serverless agents that can handle complex tasks without the hassle of managing infrastructure. You will learn everything you need to know about integrating tools, automating workflows, and deploying responsible agents with built-in guardrails on Amazon Web Services (AWS) with Amazon Bedrock. The hands-on labs provided with the course let you apply your knowledge directly in an AWS environment, hosted by AWS Partner Vocareum. Find more information and enroll for free on the DeepLearning.AI course page.
Now, let’s turn our attention to other exciting news in the AWS universe from last week.
Last week’s launches
Here are some launches that got my attention:
Amazon Transcribe now supports streaming transcription in 30 additional languages – Amazon Transcribe has expanded its support to include 30 additional languages, bringing the total number of supported languages to 54. This enhancement helps you reach a broader global audience and improves accessibility across various industries, including contact centers, broadcasting, and e-learning. The expanded language support allows for more efficient content moderation, improved agent productivity, and automatic subtitling for live events and meetings.
AWS Lambda console now surfaces key function insights and supports real-time log analytics – The AWS Lambda console now features a built-in Amazon CloudWatch Metrics Insights dashboard and supports CloudWatch Logs Live Tail, providing instant visibility into critical function metrics and real-time log streaming. You can now identify and troubleshoot errors or performance issues for your Lambda functions without leaving the console, as well as view and analyze logs in real time as they become available. You can reduce context switching and accelerate the development and troubleshooting processes for serverless applications. Check out the launch post for more details.
Amazon Bedrock Model Evaluation now supports evaluating custom model import models – You can now evaluate custom models you’ve imported to Amazon Bedrock using the model evaluation feature. This helps you to complete the full cycle of selecting, customizing, and evaluating models before deploying them. To evaluate an imported model, select the custom model from the list of models to evaluate in the model selector tool when creating an evaluation job.
Amazon Q in AWS Supply Chain – You can now use Amazon Q, an interactive AI assistant, to analyze your supply chain data in AWS Supply Chain and get insights to operate your supply chain more efficiently. Amazon Q can answer your supply chain questions by diving into your data. This reduces the time spent searching for information and streamlines finding answers to improve your supply chain operations.
For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.
Other AWS news
Here are some additional news items and posts that you might find interesting:
New Amazon OpenSearch Service YouTube channel – The channel offers bite-sized tutorials, curated content, and organized playlists on topics such as log analytics, semantic search, vector databases, and operational best practices. You can also provide feedback to influence future channel content and the OpenSearch Service roadmap. Check out the launch post for more details and subscribe to the Amazon OpenSearch Service YouTube channel.
Deploying Generative AI Applications with NVIDIA NIM Microservices on Amazon Elastic Kubernetes Service (Amazon EKS) – This post shows you how to use Amazon EKS to orchestrate the deployment of pods containing NVIDIA NIM microservices, to enable quick-to-setup and optimized large-scale large language model (LLM) inference on Amazon EC2 G5 instances. It also demonstrates how to scale (both pod and cluster) by monitoring for custom metrics through Prometheus, and how you can load balance using an Application Load Balancer.
Instant Well-Architected CDK Resources with Solutions Constructs Factories – You can now create well-architected AWS resources such as Amazon Simple Storage Service (Amazon S3) buckets and AWS Step Functions state machines with a single function call using the new AWS Solutions Constructs Factories. These factories handle all the best practices configuration for you while still allowing customization. Try using a Constructs factory the next time you need to deploy one of the supported resources.
Upcoming AWS events
Check your calendars and sign up for these AWS events:
AWS GenAI Lofts – AWS GenAI Lofts are about more than just the tech, they bring together startups, developers, investors, and industry experts. Whether you’re looking to gain deep insights, or get your questions answered by generative AI pros, our GenAI Lofts have you covered and provide everything you need to start building your next innovation. Join events in London (through October 25), Seoul (October 30–November 6), São Paulo (through November 20), and Paris (through November 25).
AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Malta (November 8), Chile (November 9), and Kochi, India (December 14).
AWS re:Invent – Registration is now open for the annual tech extravaganza, taking place December 2–6 in Las Vegas. At re:Invent 2024, you’ll get a front row seat to hear real stories from customers and AWS leaders about navigating pressing topics, such as generative AI. Learn about new product launches, watch demos, and get behind-the-scenes insights during five headline-making keynotes.
You can browse all upcoming in-person and virtual events.
That’s all for this week. Check back next Monday for another Weekly Roundup!
— Antje
This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!
A new kernel testing tree
Post Syndicated from corbet original https://lwn.net/Articles/994983/
Sasha Levin has announced a
new tree that is intended to perform continuous-integration tests of pull
requests aimed at the mainline. The plan is for this tree to hold more
finished work than sometimes ends up in linux-next; in a name that seems
destined to create typographical confusion, it is called “linus-next”.
The linus-next tree aims to provide a more stable and testable
integration point compared to linux-next, addressing the runtime
issues that make testing linux-next challenging and focusing on
code that’s about to be pulled by Linus.
Bootc 1.1.0 released
Post Syndicated from jzb original https://lwn.net/Articles/994962/
Version 1.1.0 of the bootc utility for
performing transactional, in-place operating system updates using Open Container Initative (OCI)
images, has been released. This release “officially stabilizes all
” for bootc and includes a number of bug fixes. LWN covered bootc in June.
APIs
Micael Dahlen | Science of Wellbeing & Happiness | Talks at Google
Post Syndicated from Talks at Google original https://www.youtube.com/watch?v=FAiQOlf7aTY
[$] Python PGP proposal poses packaging puzzles
Post Syndicated from jzb original https://lwn.net/Articles/993787/
Sigstore is a
project that is meant to simplify and improve the process of signing,
verifying, and protecting software. It is a relatively new project, declared
“generally available” in 2022. Python is an early adopter of sigstore; it started providing
signatures for CPython artifacts with Python 3.11
in 2022. This is in addition to the OpenPGP signatures it has been
providing since at
least 2001. Now, Seth Michael Larson—the Python Software
Foundation (PSF) security
developer-in-residence—would like to deprecate the PGP
signature and move to sigstore exclusively by next year. If that
happens, it will involve some changes in the way that Linux
distributions verify Python releases, since none of the major
distributions have processes for working with sigstore.
Security updates for Monday
Post Syndicated from jake original https://lwn.net/Articles/994941/
Security updates have been issued by Debian (asterisk, chromium, php-horde-mime-viewer, and php-horde-turba), Fedora (apache-commons-io, buildah, chromium, containers-common, libarchive, libdigidocpp, oath-toolkit, podman, rust-hyper-rustls, rust-reqwest, rust-rustls-native-certs, rust-rustls-native-certs0.7, rust-tonic, rust-tonic-build, rust-tonic-types, rust-tower, rust-tower-http, rust-tower-http0.5, rust-tower0.4, thunderbird, and unbound), SUSE (buildah, chromedriver, chromium, element-desktop, element-web, jetty-annotations, nodejs-electron, php7, php74, php8, podman, python3-virtualbox, qemu, thunderbird, and valkey), and Ubuntu (amd64-microcode).
A vulnerability in the Guix build system
Post Syndicated from daroc original https://lwn.net/Articles/994865/
The
Guix project has
disclosed a security vulnerability in the build daemon that the distribution uses to build and install software locally. The vulnerability allows an existing unprivileged user to get access to a
setuid binary, and from there potentially interfere with any other software built or installed on the computer. The project recommends upgrading the guix daemon now, to avoid the issue.
This exploit requires the ability to start a derivation build and the
ability to run arbitrary code with access to the store in the root PID
namespace on the machine the build occurs on. As such, this represents
an increased risk primarily to multi-user systems and systems using
dedicated privilege-separation users for various daemons: without
special sandboxing measures, any process of theirs can take advantage
of this vulnerability.
How to build a Security Guardians program to distribute security ownership
Post Syndicated from Mitch Beaumont original https://aws.amazon.com/blogs/security/how-to-build-your-own-security-guardians-program/
Welcome to the second post in our series on Security Guardians, a mechanism to distribute security ownership at Amazon Web Services (AWS) that trains, develops, and empowers builder teams to make security decisions about the software that they create. In the previous post, you learned the importance of building a culture of security ownership to scale security within your organization, and how AWS achieves this using the Security Guardians program. Since then, many customers have asked how they can build their own, similar program.
In this post, you will learn the steps to build your own Security Guardians program for your organization, including how to:
- Set the vision, mission, and goals of your program
- Identify developer teams that can pilot your new program
- Define the expected behaviors for those teams
- Develop training and create opportunities for career development to keep your teams engaged in the program
The guidance in this post is based on what we learned at AWS. Because every organization is different, the final version of the program you build is likely to look different from the one at AWS. Your program needs to reflect the current state of your organization’s culture of security and be designed to cultivate the security-related behaviors that are most important to your organization.
Security Guardians program mechanism
As discussed in the previous post, mechanisms form a key part of our business at AWS. Figure 1 demonstrates how a mechanism is a complete process, or virtuous cycle, that reinforces and improves itself as it operates. It takes controllable inputs and transforms them into ongoing outputs to address a recurring business challenge. In this case, the business challenge AWS faced was that security findings were being identified late in the development lifecycle, making it more expensive—in terms of time, money and effort—to remediate them. This led to bottlenecks in our security review process. The culture of security at AWS, specifically our culture of ownership, provides support to solve this challenge, but we needed the Security Guardians mechanism to actually do it.
Figure 1: AWS mechanism cycle
With most mechanisms, driving adoption is difficult, especially when the mechanism requires human participation to succeed. This is also true in the case of Security Guardians, and you can use our experience to help you avoid some of the challenges and growing pains of driving adoption.
Getting everyone aligned
“If I had an hour to solve a problem and my life depended on the solution, I would spend the first 55 minutes determining the proper question to ask, for once I know the proper question, I could solve the problem in less than five minutes.” – Albert Einstein
Getting alignment for the need to distribute security expertise starts with deeply understanding what problems need to be addressed. For example:
- Is product delivery velocity being negatively impacted by delays in the security review process?
- What business goal or metric are these delays negatively impacting?
- Where in the security review process are those delays occurring?
- What factors are contributing to those delays?
- Is it a lack of time, people, or skills?
Thoroughly understanding the specific problems and their root causes, as identified by answering those questions, allows you to evaluate whether distributing security ownership is the appropriate solution. This in turn makes it easier to gain alignment and buy-in across the organization for the chosen approach.
A component of a culture of security
Building a strong culture of security requires support from executive leadership to set the direction for the rest of the organization. Executive support makes it easier for product leaders to secure the resources and finances needed for a Security Guardians program to be successful. To align with your organization’s leaders, you can reflect on the goals of your leaders and how the Security Guardians program can be built to meet those goals.
For example, if your business goal is to ship products 25 percent faster, understand how a particular resourcing effort from Security Guardians is going to help your organization meet that goal. AWS benefited from the program with a 26.9 percent reduction in the time to review a new service or feature when a Security Guardian was involved.
Our experience is that it’s challenging to establish a Security Guardians program without executive support. If you’re struggling to identify a business leader to sponsor the program and provide insight on the business problem, your AWS account team—including your account manager or solutions architect—can help. If you’re a business leader or executive reading this post, consider becoming that sponsor yourself.
One step at a time
A step-by-step approach to implementing the Security Guardians program helps overcome organizational challenges and avoid common pitfalls that could lead to failure. These steps, shown in Figure 2, are:
- Set the vision
- Choose innovators
- Define behaviors
- Maintain interest
- Measure success
These steps support the activities that make a mechanism successful: adoption, inspection, and tools.
Figure 2: Steps for implementing a Security Guardians program
Set the vision
Now that you’ve identified the business problem or business goal, set the vision for the Security Guardians program by working backwards from this problem or goal to define the purpose of your program. For example, the vision of the AWS Security Guardians is “To nourish security ownership that consistently delights our customers with security-by-design throughout the development lifecycle.”
Craft an ambitious vision for your Security Guardians program. Think beyond easy wins and focus on bold, forward-thinking security outcomes for your organization. Make sure that each element of your vision aligns with a business problem or goal. The following table is an example of how the vision of the program is aligned with business goals:
| Business goals | Security outcome | Long-term goals |
| Develop products faster and more efficiently. | To improve developer agility while reducing security risk. | Increase the number of threat models performed by Security Guardians (instead of by application security engineers). Over time, this goal could change to “increase the quality of threat models.”.
Decrease the average monthly security issue rate. Train three new Security Guardians each quarter. |
| Reduce long-term security spend. | To identify and mitigate security risk as early as possible. | |
| Increase customer trust. | To exceed customer security expectations by raising the security bar. |
The next step is to define a clear mission that is supported with measurable goals. The mission and goals must be achievable and help to move the needle towards the long-term vision.
The final part is to name your program. We chose Security Guardians, like Marvel’s Guardians of the Galaxy. We’ve also heard customers using Security Champions, Security Advocates, Security Innovators, and Security Drivers. Have fun with it and make sure the name resonates with as many participants as possible.
After you’ve defined the vision, future state, mission, measurable goals, and name of the program, review them with your security and business leaders. It’s beneficial to include your innovators or Security Guardians who will be early adopters of the program in this review. In the next section, you’ll learn how to identify these innovators.
Choosing innovators
Just as you develop for and iterate with early adopters of the products you’re building, you should identify individuals and teams who will pilot the program with you. Before the AWS Security Guardians program, our application security engineering teams built relationships with product teams through security reviews.
This meant that they already knew which individuals within those product teams had an interest in security. This is where AWS started, but the success of your program isn’t dependent on whether you already know who these individuals are. Development teams will self-identify and nominate Security Guardians from their own teams. Figure 4 shows examples to help you get started understanding which development teams will be good early adopters for your program.
Figure 3: Example product teams for early program adopters
The examples in Figure 3 include:
Candidate A: Quick wins team
Early adopters typically share key traits, including existing security measures and a designated security role or team members with security expertise. Essentially, they already prioritize security at the team level.
Candidate B: High impact team
This is the team most impacted by the disparity between product development teams and security teams; the agility and time-related benefits of the Security Guardians program will be the highest for this team. For example, this team might be facing long delays in launching products because of the current security review process at your organization.
Candidate C: High risk team
This team owns a product that has a high security risk because of the nature of the product. This team will benefit the most from additional security scrutiny and from raising the security bar at your organization. For example, this team might be building a product that’s considered a critical asset, hosts sensitive data, or performs critical processes.
After you’ve identified one or more teams that could be good early adopters of the program, you need to identify at least one individual from each team to serve as the Security Guardian. Keep the vision and goals of your program in mind when selecting your Security Guardian. Your early Security Guardians should have at least the following characteristics:
- Ability to exercise well-informed and decisive judgement
- Maintain and showcase their knowledge
- Not afraid to have their work be independently validated
- Advocate for their security needs in internal discussions
- Hold a high security bar
- Thoughtful and assertive to make customer security a top priority on their team
In terms of time commitment, our experience is that each Security Guardian spends an average of 3.5 hours each month on activities such as answering general security questions, identifying security stories needed for sprints, diving deep into security related tasks and supporting security related tasks. Each application security review takes approximately 4 hours of effort.
The first post of this series contains even more details on the characteristics that make a good Security Guardian.
Defining behaviors
It’s important to set expectations on what behaviors you want Security Guardians, developers, and security teams to exhibit within the context of the program. These behaviors typically relate directly to the goals of the program. For example, if one of the goals is to increase the number of threat models created, then create threat modeling will be one of the defined behaviors. The behaviors need to be measurable with some flexibility for change as you improve the program.
At AWS, our Security Guardians have access to a runbook that lists the activities each Guardian should take when engaged as part of a review. With each of these activities understood, the program team will then make sure appropriate training is provided so that the Security Guardians are able to complete each of the activities. For example, AWS Security Guardians are asked to help develop threat models. To support this, the program team has developed and released training material to teach Security Guardians how to create a threat model.
With the defined behaviors, understand how the Security Guardian and product development team will engage with the security team. Although we’re clearly defining behaviors, the behaviors aren’t typically done in a silo for the successful launch of a secure product. At AWS, the Security Guardians and product developers engage with the security teams in key partnership areas. If you’re unsure of where to start in defining the behaviors of your program, Figure 4 shows an example of how teams interact at AWS, beginning with the creation of an initial threat model and going through review, remediation, and testing. Consider creating your own version of the model to help define the behaviors and key partnership areas at your organization.
Figure 4: Example behaviors and partnership areas at AWS
In the example of a threat model review, the Guardian and the central security team will jointly create and review the threat model. Specific activity examples include reviewing threats that have no documented mitigations and discussing additional threats that haven’t yet been considered.
As part of encouraging a culture of ownership, AWS recommends allowing Security Guardians to influence the role within a set of boundaries. An example of this is allowing the Security Guardians to be a part of recurring reviews of the program growth metrics, actively collecting their feedback, and encouraging them to host their own training sessions. Active Security Guardians are key to the success of the program and allowing them to influence the program will give them a sense of ownership and inclusion.
Maintaining interest
It’s important to not lose sight that a program like the AWS Security Guardians program is supported by volunteers. Most of your Security Guardians will be product developers who already have a full-time job developing products for your organization. The time and effort to find and onboard new Security Guardians will have a low return on investment if they stop engaging because the program owners didn’t keep them engaged. Keeping Security Guardians is just as important as finding them.
At AWS, we invest time to understand how to build trust with Security Guardians and provide value by working backwards from their wants and needs. Some Security Guardians joined the program to learn new skills and for career growth opportunities. AWS built training programs that were designed for Security Guardians and provide metrics that are used to document their impact to their managers and leaders.
AWS Security Guardians constantly tell us that they value recognition of their contributions by leadership. We work to build mechanisms to continuously surface the great work of our Security Guardians. We also recognize the contributions Security Guardians make through awards, gifts, and other incentives. For example, each quarter, the AWS Security Guardians team sends out a newsletter to senior leaders of the organization. This communication identifies the Guardians within their organization and highlights their contributions, including the number and impact of reviews they’ve completed.
Another way that AWS recognizes the contributions of our Security Guardians is through the Guardians Belt Program. The Guardians Belt Program is designed to recognize Security Guardians for their contributions and support them as they work to advance their security skills and expand their scope of impact. Security Guardians earn Black, Green, Yellow, and White belts with each belt corresponding to significant accomplishments that require consistent commitment to raising the security bar.
To make sure that Security Guardians value the program, your organization should provide and actively facilitate benefits. The benefits must be accessible without requiring additional time or effort from the Security Guardians, promoting immediate and direct gains. Consider the following examples of benefits to maintain Security Guardian interest and support:
- Specialized training: Workshops, game days, challenges and contests.
- Impact opportunities: Ability to impact multiple products by working with other teams in the organization, ability to help define patterns, best practices, and automation for the program.
- Community: Collaborate, connect, share and learn from experts and individuals with similar interests.
- Ownership opportunities: Ability to accelerate certain steps in the process.
- Leadership opportunities: Active involvement in recurring program or business reviews.
The best ways to maintain interest are determined by the culture of your organization. What does your organization value the most, and how will the program provide that to your Security Guardians? Sometimes, the best way to answer these questions is to ask your early or potential Security Guardians.
Measuring success
The final step of building a successful Security Guardians program is to measure program success. Measuring success is equivalent to the inspection step from Figure 1. This verifies that your desired outcomes are being achieved and provides a jumping off point for iteration. Measuring success also gives you the opportunity to audit the output or results of the Security Guardians program and perform corrections and improvements.
Earlier in this post, we covered identifying the business problem and creating the vision and measurable goals for your Security Guardians program. Example metrics include:
- Average time to release features
- Average number of security issues per team
- Average time spent by Security Guardians and builders doing security work
- Percentage of Security Guardians who have taken required and non-required training
Measuring success includes steps to collect feedback and tune the program over time, shown in Figure 5.
Figure 5: Feedback and tuning steps for Security Guardians program.
The cycle to gather feedback and tune the program includes:
- Report on metrics
- Communicate wins
- Measure outcome and cycle time
- Identify trends
- Review goals
Gathering feedback from Security Guardians is as important as providing feedback to them. One of the ways AWS collects feedback from Security Guardians is through an annual survey that collects feedback on their experiences of program and tooling. To help both builders and Security Guardians improve over time, our security review tooling captures feedback from security engineers on the inputs from Security Guardians. Combined, the data gathered through these surveys helps our security ownership mechanism reinforce and improve itself over time.
Figure 6 summarizes the steps that you can take to develop your program.
Figure 6: Security Guardians program steps
The broad steps to develop a program include:
- Set the vision: Set your vision for the program and metrics for success. Get sponsorship from leadership. Choose a name for your program.
- Choose innovators: Identify innovators who have a passion for security and foster a community with continuous knowledge sharing.
- Define behaviors: Redefine your RACI (responsible, accountable, consulted, informed) and be clear on expectations from your security advocates.
- Maintain interest: Provide clear training and learning paths and opportunities for career advancement.
- Measure success: Gather feedback and measure the program’s effectiveness.
Conclusion
This post and the previous post covered numerous concepts, considerations, and ideas, including:
- The initial intention of the Security Guardians program is to focus on training developers in product teams. This improves early security-focused design thinking.
- An alternative approach is to embed or align security engineers directly with product development teams. This can be more effective in organizations where reporting structures and accountability are key considerations.
- Some organizations draw Security Guardians from all job types. The program can also be used to focus on uplifting developers and broad security culture.
- You must regularly inspect the outcomes delivered by the Security Guardians program and use the information to make incremental improvements as the program matures.
For additional support building a Security Guardians program, contact your AWS account representative and they will get you in touch with a specialist who can help you develop your program.
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.
The Forgotten Foundational Document: The Articles of Association.
Post Syndicated from The History Guy: History Deserves to Be Remembered original https://www.youtube.com/watch?v=mi_j6ppZ7kQ
AI and the SEC Whistleblower Program
Post Syndicated from B. Schneier original https://www.schneier.com/blog/archives/2024/10/ai-and-the-sec-whistleblower-program.html
Tax farming is the practice of licensing tax collection to private contractors. Used heavily in ancient Rome, it’s largely fallen out of practice because of the obvious conflict of interest between the state and the contractor. Because tax farmers are primarily interested in short-term revenue, they have no problem abusing taxpayers and making things worse for them in the long term. Today, the U.S. Securities and Exchange Commission (SEC) is engaged in a modern-day version of tax farming. And the potential for abuse will grow when the farmers start using artificial intelligence.
In 2009, after Bernie Madoff’s $65 billion Ponzi scheme was exposed, Congress authorized the SEC to award bounties from civil penalties recovered from securities law violators. It worked in a big way. In 2012, when the program started, the agency received more than 3,000 tips. By 2020, it had more than doubled, and it more than doubled again by 2023. The SEC now receives more than 50 tips per day, and the program has paid out a staggering $2 billion in bounty awards. According to the agency’s 2023 financial report, the SEC paid out nearly $600 million to whistleblowers last year.
The appeal of the whistleblower program is that it alerts the SEC to violations it may not otherwise uncover, without any additional staff. And since payouts are a percentage of fines collected, it costs the government little to implement.
Unfortunately, the program has resulted in a new industry of private de facto regulatory enforcers. Legal scholar Alexander Platt has shown how the SEC’s whistleblower program has effectively privatized a huge portion of financial regulatory enforcement. There is a role for publicly sourced information in securities regulatory enforcement, just as there has been in litigation for antitrust and other areas of the law. But the SEC program, and a similar one at the U.S. Commodity Futures Trading Commission, has created a market distortion replete with perverse incentives. Like the tax farmers of history, the interests of the whistleblowers don’t match those of the government.
First, while the blockbuster awards paid out to whistleblowers draw attention to the SEC’s successes, they obscure the fact that its staffing level has slightly declined during a period of tremendous market growth. In one case, the SEC’s largest ever, it paid $279 million to an individual whistleblower. That single award was nearly one-third of the funding of the SEC’s entire enforcement division last year. Congress gets to pat itself on the back for spinning up a program that pays for itself (by law, the SEC awards 10 to 30 percent of their penalty collections over $1 million to qualifying whistleblowers), when it should be talking about whether or not it’s given the agency enough resources to fulfill its mission to “maintain fair, orderly, and efficient markets.”
Second, while the stated purpose of the whistleblower program is to incentivize individuals to come forward with information about potential violations of securities law, this hasn’t actually led to increases in enforcement actions. Instead of legitimate whistleblowers bringing the most credible information to the SEC, the agency now seems to be deluged by tips that are not highly actionable.
But the biggest problem is that uncovering corporate malfeasance is now a legitimate business model, resulting in powerful firms and misaligned incentives. A single law practice led by former SEC assistant director Jordan Thomas captured about 20 percent of all the SEC’s whistleblower awards through 2022, at which point Thomas left to open up a new firm focused exclusively on whistleblowers. We can admire Thomas and his team’s impact on making those guilty of white-collar crimes pay, and also question whether hundreds of millions of dollars of penalties should be funneled through the hands of an SEC insider turned for-profit business mogul.
Whistleblower tips can be used as weapons of corporate warfare. SEC whistleblower complaints are not required to come from inside a company, or even to rely on insider information. They can be filed on the basis of public data, as long as the whistleblower brings original analysis. Companies might dig up dirt on their competitors and submit tips to the SEC. Ransomware groups have used the threat of SEC whistleblower tips as a tactic to pressure the companies they’ve infiltrated into paying ransoms.
The rise of whistleblower firms could lead to them taking particular “assignments” for a fee. Can a company hire one of these firms to investigate its competitors? Can an industry lobbying group under scrutiny (perhaps in cryptocurrencies) pay firms to look at other industries instead and tie up SEC resources? When a firm finds a potential regulatory violation, do they approach the company at fault and offer to cease their research for a “kill fee”? The lack of transparency and accountability of the program means that the whistleblowing firms can get away with practices like these, which would be wholly unacceptable if perpetrated by the SEC itself.
Whistleblowing firms can also use the information they uncover to guide market investments by activist short sellers. Since 2006, the investigative reporting site Sharesleuth claims to have tanked dozens of stocks and instigated at least eight SEC cases against companies in pharma, energy, logistics, and other industries, all after its investors shorted the stocks in question. More recently, a new investigative reporting site called Hunterbrook Media and partner hedge fund Hunterbrook Capital, have churned out 18 investigative reports in their first five months of operation and disclosed short sales and other actions alongside each. In at least one report, Hunterbrook says they filed an SEC whistleblower tip.
Short sellers carry an important disciplining function in markets. But combined with whistleblower awards, the same profit-hungry incentives can emerge. Properly staffed regulatory agencies don’t have the same potential pitfalls.
AI will affect every aspect of this dynamic. AI’s ability to extract information from large document troves will help whistleblowers provide more information to the SEC faster, lowering the bar for reporting potential violations and opening a floodgate of new tips. Right now, there is no cost to the whistleblower to report minor or frivolous claims; there is only cost to the SEC. While AI automation will also help SEC staff process tips more efficiently, it could exponentially increase the number of tips the agency has to deal with, further decreasing the efficiency of the program.
AI could be a triple windfall for those law firms engaged in this business: lowering their costs, increasing their scale, and increasing the SEC’s reliance on a few seasoned, trusted firms. The SEC already, as Platt documented, relies on a few firms to prioritize their investigative agenda. Experienced firms like Thomas’s might wield AI automation to the greatest advantage. SEC staff struggling to keep pace with tips might have less capacity to look beyond the ones seemingly pre-vetted by familiar sources.
But the real effects will be on the conflicts of interest between whistleblowing firms and the SEC. The ability to automate whistleblower reporting will open new competitive strategies that could disrupt business practices and market dynamics.
An AI-assisted data analyst could dig up potential violations faster, for a greater scale of competitor firms, and consider a greater scope of potential violations than any unassisted human could. The AI doesn’t have to be that smart to be effective here. Complaints are not required to be accurate; claims based on insufficient evidence could be filed against competitors, at scale.
Even more cynically, firms might use AI to help cover up their own violations. If a company can deluge the SEC with legitimate, if minor, tips about potential wrongdoing throughout the industry, it might lower the chances that the agency will get around to investigating the company’s own liabilities. Some companies might even use the strategy of submitting minor claims about their own conduct to obscure more significant claims the SEC might otherwise focus on.
Many of these ideas are not so new. There are decades of precedent for using algorithms to detect fraudulent financial activity, with lots of current-day application of the latest large language models and other AI tools. In 2019, legal scholar Dimitrios Kafteranis, research coordinator for the European Whistleblowing Institute, proposed using AI to automate corporate whistleblowing.
And not all the impacts specific to AI are bad. The most optimistic possible outcome is that AI will allow a broader base of potential tipsters to file, providing assistive support that levels the playing field for the little guy.
But more realistically, AI will supercharge the for-profit whistleblowing industry. The risks remain as long as submitting whistleblower complaints to the SEC is a viable business model. Like tax farming, the interests of the institutional whistleblower diverge from the interests of the state, and no amount of tweaking around the edges will make it otherwise.
Ultimately, AI is not the cause of or solution to the problems created by the runaway growth of the SEC whistleblower program. But it should give policymakers pause to consider the incentive structure that such programs create, and to reconsider the balance of public and private ownership of regulatory enforcement.
This essay was written with Nathan Sanders, and originally appeared in The American Prospect.
Amazing SmartWave Shades: Stylish Light Control! 😎🌞
Post Syndicated from BeardedTinker original https://www.youtube.com/watch?v=tu6arB7URJg
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Post Syndicated from LastWeekTonight original https://www.youtube.com/watch?v=qXiEGPWVjGU
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Post Syndicated from LastWeekTonight original https://www.youtube.com/watch?v=wDSlvMp5knk
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Post Syndicated from LastWeekTonight original https://www.youtube.com/watch?v=t-ZXW-KLt7A
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Post Syndicated from LastWeekTonight original https://www.youtube.com/watch?v=yI15h4s6ppM
