All posts by Esra Kayabali

AWS Weekly Roundup: Student Rewards on AWS Builder Center, Local Zone in Las Vegas, and more (August 24, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-student-rewards-on-aws-builder-center-local-zone-in-las-vegas-and-more-august-24-2026/

During my time at AWS, I have always looked for opportunities to work with students. I have delivered over 50 talks at universities across the region, and watching the potential in the room is always a strong motivator. It reminds me of why I do this work, and that the students I meet today may well become our customers and collaborators tomorrow. That is why I am happy to open this week with Student Rewards on AWS Builder Center.

Rick Suttles published Introducing Student Rewards on AWS Builder Center, a new benefit for verified higher education students. When you verify your enrollment through SheerID and complete your Builder Center profile, you unlock 12 months of premium AWS Skill Builder access (900+ courses, hands-on labs, certification exam prep, and game-based learning). From there, you earn badges through actions on Builder Center: publishing articles, commenting, and maintaining engagement. At 7 badges, you unlock $10 in AWS Credits. At 14 badges, another $20 in credits. At 21 badges, you earn an AWS Foundational Certification exam voucher ($100 value).

This represents a commitment of over $500 million in resources during this back-to-school season, providing students with the training, tools, and certification needed to start building their careers in cloud and AI. Student Rewards is available to students 18 years or older and enrolled at accredited higher education institutions worldwide, subject to verification and applicable terms.

Verify your student status and start learning, earning badges, and unlocking rewards!

Last week’s launches
Here’s what else happened this week.

  • A new AWS Local Zone in Las Vegas, Nevada – This new Local Zone supports Amazon EC2 C7i, M7i, R7i, and C8gn instances, Amazon EBS, Amazon ECS, Amazon EKS, Application Load Balancer, and AWS Direct Connect. AWS Local Zones are now available in more than 30 metropolitan areas worldwide. In addition, AWS added a fourth Availability Zone to the Europe (London) Region, delivering next-generation AI and ML capacity with Trn3 and P6 accelerated instances alongside general-purpose compute.
  • Amazon EC2 Auto Scaling now supports batch instance termination – You can now pass up to 100 instance IDs to the TerminateInstanceInAutoScalingGroup API to terminate them as a batch, reducing the number of API calls needed to scale down your Auto Scaling groups. Batch termination is designed for workloads that need to rapidly scale down, such as AI/ML training jobs, container orchestrators, or event-driven architectures that spin up large fleets temporarily.
  • AWS CloudShell now includes a built-in visual file editor – CloudShell now includes a visual file editor that you can launch directly from your shell session using a single edit command. The editor supports syntax highlighting, find-and-replace, multi-line selection, copy-paste, and undo-redo in a single browser session. Whether you are updating a deployment script, modifying an agent steering file, editing a CloudFormation template, or fixing a Lambda function, the editor provides a seamless edit-and-run experience without leaving CloudShell.
  • Amazon Bedrock now supports SpaceXAI Grok 4.6 with cross-Region inference – Grok 4.6, a frontier model built for coding, agentic tasks, and knowledge work, is now available on Amazon Bedrock. The model runs on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and works with existing account-level controls including model invocation logging, Amazon CloudWatch metrics, and cost itemization in AWS Cost Explorer.
  • Amazon Bedrock expands API support and introduces cross-Region inference for OpenAI models – Amazon Bedrock now supports OpenAI GPT-5.6 models (Sol, Terra, and Luna) with the Responses, Converse, and Chat Completions APIs, and adds cross-Region inference. Geo cross-Region inference routes requests within a predefined geography (including new US Geo support with this launch), while Global cross-Region inference serves requests from any commercial AWS Region at a lower per-token cost.
  • AgentCore payments is now generally available in Amazon Bedrock AgentCore – At general availability, AgentCore payments includes Quick Create for Coinbase credential provisioning directly within the AgentCore console, a curated Coinbase Bazar MCP server of pay-per-use x402 endpoints via AgentCore gateway, support for the Machine Payment Protocol (MPP), and the “upto” scheme in the x402 protocol for pay-per-inference and dynamic pricing use cases. To learn more, visit the AI Blog post.
  • AWS Glue 6.0 delivers 30% price reduction and Iceberg v3 support – AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions. With Iceberg v3, Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts you may find useful:

  • Updates to your AWS Sign-In experience – AWS is gradually introducing updates to the sign-in and sign-up experience. The redesigned sign-in page introduces a unified email entry point for root users and customers using the new email-based sign-in method, while IAM users continue signing in with their account ID, username, and password. The page also includes sign-in options for customers whose AWS account was created using a supported identity provider (Google, GitHub, Apple, or Amazon.com). A redesigned session selection page simplifies viewing and managing multiple active account and role sessions. If your organization relies on browser automation or scripted workflows that interact with the sign-in page, review the post to understand how these changes might affect your configuration.
  • In the works: AWS Builder Lofts in Berlin, Hyderabad, and São Paulo – My colleague Channy announced plans to open new Builder Lofts in three cities. Since the first Builder Loft opened in San Francisco in July 2025, it has welcomed more than 22,500 developers through its doors. Each new location will be a permanent community space offering free workshops, networking events, pitch nights, content creation spaces, and co-working areas. Berlin will focus on digital sovereignty and security-readiness, Hyderabad on AI and cloud-native architecture, and São Paulo on supporting Latin America’s developer ecosystem.
  • AWS and Amazon WorkSpaces recognized as a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service – AWS has been named a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service (DaaS) for the third consecutive year, evaluated on Completeness of Vision and Ability to Execute. Gartner noted strengths in operations, geographic strategy, and overall viability. This is also the first year the evaluation includes Amazon WorkSpaces for AI agents, a capability that runs AI agents within the same desktop environment, security perimeter, and audit trail as human users.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building.

Summer is slowly coming to an end, and I am already planning a few days off in the coming months to keep me motivated through the rainy autumn ahead. I hope you are doing the same. Come back next week for more!

— Esra

Amazon DynamoDB now supports real-time vector search at any scale

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/amazon-dynamodb-now-supports-real-time-vector-search-at-any-scale/

Today, we’re announcing the general availability of vector search in Amazon DynamoDB. You can now store vector embeddings alongside your operational data in DynamoDB and run similarity searches directly against that data, without replicating it to a separate vector store.

DynamoDB supports native vector search with single-digit millisecond latency at 99%+ recall, and is designed for any scale, even trillions of vectors. There are no servers to provision, patch, or manage, and no software to install, maintain, or operate. The service has no versions, no maintenance windows, and zero downtime maintenance.

Vector indexes have no storage limits and scale horizontally as your data grows. You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search.

If your application already uses DynamoDB, adding vector search previously required copying data into a dedicated vector database while maintaining a synchronization pipeline between the two services. This added operational overhead, data movement costs, licensing costs, and the challenge of maintaining predictable low latency at scale. With vector search built into DynamoDB, your vectors and operational data share the same serverless infrastructure and the same pay-per-request pricing model.

Vector search in DynamoDB introduces a new index type that you create on an attribute storing vector embeddings. You generate embeddings using a model of your choice, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them as a list of floats in your table using a standard PutItem call. You then create a vector index on that attribute and specify the number of dimensions, the distance function, and any non-vector attributes you want to use as filters to narrow search results at query time. The SearchVectors API accepts a query vector, the number of results to return (up to 100), and optional filter conditions. It returns results ranked by similarity.

Use vector search in DynamoDB when your operational data already lives in DynamoDB and you want to add similarity search without provisioning a separate database or managing a synchronization pipeline. DynamoDB is fully serverless, so vector search scales automatically with no infrastructure to manage. It supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering.

Getting started with vector search in DynamoDB
This walkthrough shows how to add vector search to an existing DynamoDB table using the DynamoDB console. The scenario contains an online sporting goods store with a product catalog table. Each item has standard operational attributes such as productId, category, description, marketplace, name, and price. The goal is to add semantic search so shoppers can find products using natural language queries rather than exact keyword matches.

1. Prepare DynamoDB table
To enable semantic search, I first generate vector embeddings for the product descriptions already in my table. Embeddings are numerical representations of text generated by a machine learning model that capture the meaning of the content. Two items with similar descriptions will have embeddings that are close to each other in vector space, which is what makes similarity search possible.

I can generate embeddings using Amazon Bedrock Titan Text Embeddings or another embedding model, then add them to my table using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, AWS CloudFormation, or other infrastructure-as-code (IaC) tools.

For an existing table like ProductCatalog, I add the embeddings to each item as a new attribute named descriptionEmbedding using an UpdateItem call. DynamoDB stores vector embeddings using its existing List data type. Each element in the list is a Number that represents a single float value of the embedding vector. This means I do not need a new data type or schema change to start storing vectors alongside my existing operational attributes.

2. Create vector index
In the DynamoDB console, open the ProductCatalog table and choose the Indexes tab. I choose Create vector index. On the Create vector index page, I fill in the index details as follows. I enter ProductDescriptionIndex as the Index name and descriptionEmbedding as the Vector attribute.

I enter the number of Dimensions that matches my embedding model’s output and select Cosine as the Distance function. Cosine measures the angle between vectors rather than their magnitude, which makes it effective for comparing semantic similarity of text embeddings. Vector search in DynamoDB also supports Euclidean and Dot product distance functions.

  • Euclidean: Use when the magnitude of the vectors is meaningful, such as clustering items by a numeric value like purchase count.
  • Dot product: Use when both direction and magnitude matter, such as in recommendation systems that weight interest alignment and frequency together. As a general rule, match the distance function to the one used to train your embedding model for the best accuracy.

I enter marketplace as the Partition key. The vector index partition key controls how DynamoDB distributes vectors across partitions, allowing the index to scale out while maintaining predictable latencies. Each search is scoped to a single partition key value, so a product catalog serving multiple marketplaces can search within one marketplace’s inventory without scanning the entire index. The partition key is optional, but recommended for large datasets with high query throughput.

I expand Inline filter attributes and add category as a filter attribute. This helps me narrow search results to a specific product category at query time. Filter conditions support exact-match values only; range conditions such as BETWEEN or BEGINS_WITH are not supported. I leave Attribute projections set to All so that all table attributes are returned with my search results. Choose Create vector index and wait for the index status to change to Active.

3. Run vector search
I generate a query vector from a natural language search term such as “lightweight running shoes for summer” using the same embedding model I used for the product descriptions. In the DynamoDB console, I choose Explore items in the left navigation pane and select the ProductCatalog table.

Choose Search to switch to vector search mode. I select ProductDescriptionIndex from the Select a vector index dropdown, paste the query vector into the Search vector field, and set Number of results (Top K) to 5. I enter US as the Partition key value to scope the search to the US marketplace. I expand Inline filter attributes and set category equal to footwear to narrow the search to footwear products only. Now, choose Run.

DynamoDB returns the five most semantically similar products in the footwear category, ranked by similarity score, alongside the standard operational attributes such as name and price in the same response. The similarity score’s meaning depends on the distance function selected for the index. For Cosine and Euclidean distance functions, lower similarity score values indicate higher similarity, with a score of 0 indicating identical vectors. For the dot product distance function, higher similarity score values indicate higher similarity.

To interact with vector search programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon DynamoDB Developer Guide.

Get started today
Vector search in Amazon DynamoDB is generally available in all commercial AWS Regions, including the AWS GovCloud (US) Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For pricing details, visit the Amazon DynamoDB pricing page.

Start exploring vector search in DynamoDB today and send feedback to AWS re:Post for Amazon DynamoDB or through your usual AWS Support contacts.

— Esra

Amazon SQS turns 20: Two decades of reliable messaging at scale

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/amazon-sqs-turns-20-two-decades-of-reliable-messaging-at-scale/

On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3. We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies. If one service called another directly and that service was slow or unavailable, failures cascaded through the entire system. Message queuing solved this by letting services communicate asynchronously: a producer could drop a message into a queue and move on, while a consumer picked it up when ready. This approach kept individual service failures from affecting the rest of the system.

When Amazon SQS launched publicly in July 2006, it made this pattern available to every AWS customer. Twenty years later, that core function, decoupling producers from consumers, remains the reason customers use SQS. The scale, performance, and operational controls around it look very different now though.

Jeff Barr covered the first 15 years of SQS milestones in his 15th anniversary post, from the original 8 KB message limit in 2006 through FIFO queues, server-side encryption, and Lambda integration. Over the last five years, we have continued to scale SQS, added stronger security defaults, and introduced new capabilities that address increasingly complex workload patterns.

Key milestones between 2021 and 2026
High throughput mode for FIFO queues (2021): In May 2021, we launched general availability of high throughput mode for FIFO queues, supporting up to 3,000 transactions per second (TPS) per API action, a tenfold increase over the previous limit. We continued raising this ceiling over the following two years: to 6,000 TPS in October 2022, to 9,000 TPS in August 2023, and to 18,000 TPS in October 2023, before reaching 70,000 TPS per API action in select Regions by November 2023.

Server-side encryption with SSE-SQS (2021): In November 2021, we introduced server-side encryption with Amazon SQS-managed encryption keys (SSE-SQS), giving customers an encryption option that required no key management. In October 2022, we made SSE-SQS the default for all newly created queues, so customers no longer needed to explicitly enable it.

Dead-letter queue redrive enhancements (2021): We progressively expanded how customers recover unconsumed messages from dead-letter queues. In December 2021, we added DLQ redrive to source queue directly in the SQS console. In June 2023, we extended this capability to the AWS SDK and CLI through new APIs, including StartMessageMoveTask, CancelMessageMoveTask, and ListMessageMoveTasks. In November 2023, we added redrive support for FIFO queues.

Attribute-based access control, ABAC (2022): In November 2022, we introduced ABAC, giving customers the ability to configure access permissions based on queue tags rather than maintaining static policies as resources scaled.

JSON protocol support (2023): In November 2023, we added support for the JSON protocol in the AWS SDK, reducing end-to-end message processing latency by up to 23% for a 5 KB payload and lowering client-side CPU and memory usage.

Amazon EventBridge Pipes console integration (2023): We added the ability to connect a queue directly to EventBridge Pipes from the SQS console, routing messages to a broad range of AWS service targets without writing custom integration code.

Extended Client Library for Python (2024): We brought the Extended Client Library, previously available for Java, to Python developers, allowing messages up to 2 GB to be sent through SQS by storing the payload in Amazon S3 and passing a reference through the queue.

FIFO in-flight message limit increase (2024): We increased the in-flight message limit for FIFO queues from 20,000 to 120,000 messages, so consumers can process significantly more messages concurrently without being constrained by the previous ceiling.

Fair queues for multi-tenant workloads (2025): We introduced fair queues to mitigate the noisy neighbor problem in multi-tenant standard queues. By including a message group ID when sending messages, customers can prevent a single tenant from delaying message delivery for others, without any changes required on the consumer side.

1 MiB maximum message payload size (2025): We increased the maximum message payload from 256 KiB to 1 MiB for both standard and FIFO queues, helping customers send larger messages without offloading data to external storage. AWS Lambda event source mapping for SQS was updated in parallel to support the new payload size.

The constant underneath the change
Despite two decades of feature additions, the fundamental use case for SQS has not shifted. Customers use it to decouple services, buffer bursts of traffic, and build systems that stay resilient when individual components fail. That same pattern now extends to AI workloads. Customers use SQS queues to buffer requests to large language models, manage inference throughput, and coordinate communication between autonomous AI agents operating as independent services. For an example of this architecture in practice, read Creating asynchronous AI agents with Amazon Bedrock.

To learn more about Amazon SQS, visit the Amazon SQS product page, review the developer guide, or explore recent updates on the AWS Blogs.

— Esra

AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-builder-center-at-one-year-network-scanning-in-security-hub-loom-for-aws-and-more-july-13-2026/

AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library. To mark the anniversary, Rick Suttles published a full feature timeline covering everything shipped over the past year: AWS Capabilities by Region (1,500+ services across 37 Regions), Spaces for community-created groups, workshops with category and complexity filters, badges and streaks, article series, view counts, saved items, student status, availability notifications, sign-in with GitHub and Amazon, and sandbox environments.

Jeff Barr published a retrospective summarizing Builder Center’s first year. Since launch, 5,548 authors have published 6,448 articles with more than 10.4 million page views combined. Builders have earned 99,226 badges since the badge system launched in March 2026. Community members have submitted 565 wishes, 10 of which have shipped with another 20 on the near-term roadmap.

The top community article Building an AWS Study Buddy with MCP + Strands Agents SDK by Dineshraj Dhanapathy reached 50,000+ views. Chris Miller’s Migrating an EOL Linux Server to AWS in 8 Hours with Kiro followed at 45,000+, and Yash Aggarwal’s AIdeas: NeuroVoice – Multimodal AI for Early Screening of Neurological Diseases article reached 38,000+.

The week’s headline addition is Sandbox Environments by Rick Suttles. Sandboxes give you a free, pre-provisioned AWS account to complete a workshop exercise. Each environment is active for 8 hours, after which the account and all its resources are automatically de-provisioned. You can have one active sandbox at a time and request one per week. No personal AWS account, credit card, or manual cleanup required.

Last week’s launches
Here’s what else happened this week.

  • AWS Security Hub introduces Network Scanning – Security Hub introduced Network Scanning, a capability that identifies resources in your environment that are reachable from the public internet. Network Scanning probes your resources from the internet to detect actual reachability, complementing the existing network reachability findings in Security Hub that identify configurations that could make a resource reachable. It discovers public IP addresses, virtual machines, and load balancers across your AWS and Azure environments, identifies reachable ports, and determines what services are running behind them. Each reachable port generates a Security Hub finding with evidence of the port and service discovered. Security Hub Exposures then automatically correlates these findings with other findings and resource configurations to determine broader risk. Existing customers can enable Network Scanning in individual accounts and Regions, or across an organization through a configuration policy. For new customers, Network Scanning is on by default. It is included with Security Hub Essentials at no additional cost.
  • Security Hub also extends unified security management to Microsoft Azure – Security Hub now monitors Microsoft Azure resources, providing unified posture management, vulnerability management, and security response across both clouds. It automatically discovers Azure VMs, container images, Function Apps, and identities, and evaluates them for misconfigurations, internet exposure, and software vulnerabilities. AWS and Azure findings appear in the same prioritized view with the same formats and automation workflows.
  • Amazon SageMaker Studio integrates with Hugging Face for one-click model deployment and customization – You can now go from discovering a model on Hugging Face to working with it in SageMaker Studio in a single click. Select any supported model on Hugging Face and choose “Customize on SageMaker AI” or “Deploy on SageMaker AI” to land directly on the corresponding workflow page with the model pre-loaded. New customers receive a Studio environment created in seconds with pre-configured permissions for serverless model customization (including fine-tuning with custom reward functions for reinforcement learning), model evaluation, and deployment to SageMaker or Bedrock endpoints. Verified customers receive default GPU access to G5, G6, and G4dn instances without requesting quota increases, and quota utilization is visible directly inside the Studio environment.
  • Amazon EKS Auto Mode and Amazon ECS Managed Instances reduce GPU management fees by up to 60% – Beginning July 1, 2026, EKS Auto Mode and ECS Managed Instances reduce management fees for accelerated instance types: G-series fees are down 35%, and P-series and AWS Trainium fees are down 60%. The reductions apply automatically to existing clusters and require no action from customers. Both services include capabilities built for accelerated workloads. EKS Auto Mode provides automatic parallel image pulling on GPU instances with local NVMe storage and accelerator-aware node repair. ECS Managed Instances provides GPU metrics through Amazon CloudWatch Container Insights and automatic health monitoring for GPU hardware failures.
  • Amazon Aurora DSQL change data capture (CDC) is now generally available – Aurora DSQL CDC streams the results of insert, update, and delete operations as change events to Amazon Kinesis Data Streams. You can use it to synchronize data across microservices, trigger Lambda functions, or deliver changes to S3, Redshift, and OpenSearch Service through Amazon Data Firehose. CDC streaming is designed to have zero impact on database workload performance and requires no infrastructure to manage.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts you may find useful:

  • Building secure AI agents at scale: Introducing Loom for AWS – Loom is an open-source enterprise platform for building agents with AWS Strands Agents and deploying them on Amazon Bedrock AgentCore Runtime. It provides a unified management UI and backend API with identity provider integration, scope-based authorization, multi-persona navigation, and full lifecycle management for agents, memory, MCP servers, and agent-to-agent integrations. Loom enforces automated resource tagging for cost attribution, implements RBAC and ABAC for multi-tenant security, uses paved-path blueprints for agent deployments, manages identity propagation through delegated actor chains, integrates with AWS Agent Registry for discovery and governance, and supports human-in-the-loop review before sensitive actions. The project is available in AWS Labs on GitHub.
  • Introducing Claude apps gateway for AWS – The Claude apps gateway is a self-hosted control plane that gives organizations centralized control over access, cost, and policy for Claude Code and Claude Desktop. It connects to any OIDC-compliant identity provider, enforces managed settings on every request, routes inference to Amazon Bedrock or Claude Platform on AWS, and supports per-user and per-group spend caps. The gateway runs as a stateless container in your private network, backed by a PostgreSQL database for short-lived sign-in state. No long-lived secrets are stored on developer machines. Deploy it through Amazon Bedrock to keep data within the AWS security boundary, or through Claude Platform on AWS for the native Claude platform experience.
  • Introducing OAuth support for AWS MCP Server – You can now connect agents to the AWS MCP Server using browser-based OAuth with the same credentials you use for the AWS Console or CLI. The new sign-in path supports IAM federation, AWS IAM Identity Center, and root or IAM users. AWS Sign-In issues short-lived access tokens and refresh tokens, with automatic token management so developers stay authenticated across restarts. For headless use cases, a non-interactive flow lets applications with existing AWS credentials obtain OAuth access tokens through the create-oauth2-token-with-iam API. New governance controls include OAuth-specific IAM condition keys, token introspection and revocation, dynamic client registration, and CloudTrail audit elements.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building.

Wishing everyone a restful and enjoyable summer. Whether you’re building, learning, or recharging, I hope you find time for all three. I’ll be heading to Scandinavia for a few weeks to trade the heat for some cooler weather and longer evenings. Come back next week for more news!

— Esra

AWS DevOps Agent adds release management capabilities to assess code changes before production (preview)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-devops-agent-adds-release-management-capabilities-to-assess-code-changes-before-production-preview/

Today, we’re announcing a new release management capability in AWS DevOps Agent that is now available in preview. AWS DevOps Agent is your always-available teammate that spans software changes and operations across AWS, multicloud, and on-premises environments. The practice of DevOps aims to make software change and operations smooth and increasingly autonomous, and AWS DevOps Agent delivers on both by leveraging its deep understanding of your environment, your services, their dependencies, and how they behave in production. Already generally available for post-deployment operations, it autonomously investigates incidents, provides root cause analysis and mitigation steps, and delivers targeted recommendations to prevent recurring issues. With today’s preview, AWS DevOps Agent adds release readiness review of code changes and autonomous release testing. These new features verify every change against the natural language standards you give to the DevOps Agent and run change-specific tests in production-like environments. AWS DevOps Agent now supports teams from code creation to production, helping reviewers and testers keep pace with the volume of AI-generated code.

As development teams adopt AI coding tools, the volume of pull requests moving through delivery pipelines has increased faster than review and testing processes can handle. When teams are under pressure to keep up, reviews are approved without thorough examination, and test environments drift from production. The value that coding agents generate sits waiting in review queues instead of reaching end users. At the same time, AI models are increasingly capable of catching functional and security issues that human reviewers might miss under time pressure, making speedy and safe delivery a requirement rather than a tradeoff.

The release readiness review feature evaluates every code change against production requirements, dependency safety, and the standards and best practices you provide to the DevOps Agent. The agent checks cross-repository dependency risks that could affect other services, access control changes against AWS Well-Architected Framework best practices, and compliance with any standards you have defined. When no standards are provided, the agent applies general best practices. As part of the review, the agent also runs your software in an AWS-managed isolated environment, executing lightweight user journey tests to verify the software builds, runs, and passes basic functional checks before the change enters the pipeline. Findings appear in the AWS DevOps Agent console and as comments on pull requests in GitHub or GitLab. You can also invoke reviews directly from your IDE through the Kiro power or Claude Code plugin, so developers can identify and fix dependency risks, standards violations, and access control issues before the change is committed to version control.

The autonomous release testing feature goes further, generating and running change-specific test plans for web and API-based applications in customer-provisioned, production-like environments before the change merges. Rather than running a static test suite, the agent reasons about what the change does and constructs tests tailored to it, covering functional correctness, behavioral regressions, and integration scenarios that a manually maintained test plan might not anticipate. Every test run produces structured artifacts including metrics, logs, traces, and an execution summary, giving reviewers a consistent record of what was tested and what the results were.

Getting started with AWS DevOps Agent release management
This walkthrough shows how to run an on-demand release readiness review using the AWS DevOps Agent web app. Before you begin, confirm that you have at least one GitHub or GitLab repository connected to your Agent Space. Once your repositories are connected, AWS DevOps Agent will index your code and build a knowledge graph of cross-repository and cloud dependencies.

To open the web app, navigate to the AWS DevOps Agent console, select your Agent Space, and choose the Web app tab. Choose Operator access to open the web app.

Without standards configured, the agent applies general best practices. To tailor reviews to your internal standards, navigate to Knowledge, then choose the Instructions tab. You will see a list of instruction sets, each scoped to a specific agent or task. Choose View next to Release readiness review to edit the instructions for production-readiness change review. Write your internal standards in plain English. For example, you can define infrastructure and data standards on encryption or network access rules, best practices that warn without blocking such as logging and observability requirements, and sensitive data classification best practices that identify applications or resources requiring higher security measures. To apply instructions across all agents in your space, choose View next to All agents.

You can trigger a release readiness review in two ways: by submitting a pull request to a connected repository, or by entering an on-demand query in the chat interface. To run an on-demand review from chat, choose New chat and enter a request such as:

Perform a production risk analysis on my repository branch

The agent will ask for the repository and branch you want to analyze. You can provide a branch name, a pull request number, or a commit SHA. Once you confirm your selection, the agent queues the review and analyzes the change for production risks, including infrastructure impacts, configuration changes, and potential issues.

After the review completes, you can ask follow-up questions directly in the chat to explore the findings in more detail. For example, you can ask which downstream consumers a change affects, and the agent will return a structured breakdown of in-repository and cross-repository consumers that will break, the specific files and line numbers affected, and the recommended steps to resolve the issue before deployment.

After submitting a review request, navigate to Changes in the left navigation pane. The Proposed changes table shows each review that has run, including the proposed change description, its source, category, status, and when it was created. You can filter by category or status to find specific reviews, or search by name using the search bar. Choose any entry to open the full execution detail.

The Timeline tab shows the agent’s step-by-step reasoning process, including the tools it called, the dependencies it consulted, and the observations it made at each step. Each entry is timestamped, giving you a complete record of how the agent built its understanding of the change and reached its conclusion.

Choose the Report tab to see the final recommendation. The report opens with a summary header showing the recommended action, the number of critical issues found, the commit revision, and the number of files changed. The recommended action is either BLOCK, Proceed with Caution, or Safe to Release.

Below the summary header, the Analysis section explains why the recommendation was made, citing specific risks and the evidence the agent found to support its conclusion. The Issues section lists each finding by severity, giving you a prioritized view of what needs to be addressed before the change can proceed. The Recommendations section provides specific, actionable steps the developer can take to resolve each issue. Finally, the Changes section lists each file that was modified, with the type of change, the category it falls under, and a description of what was changed, so reviewers have a complete picture of what the change does before it merges.

You can also invoke the autonomous release testing feature directly from the chat interface. To run an autonomous release test on a web or API-based application, choose New chat and enter a query such as:

Run a release test on my application deployed at [application URL]

The agent generates a change-specific test plan and executes it in your provisioned environment. Results appear in Changes, where you can review the execution steps and a structured summary of what was tested.

Get started today
The release readiness review and autonomous release testing features for AWS DevOps Agent are available in preview. These features are available at no additional cost during preview in the US East (N. Virginia) Region. For pricing information on other AWS DevOps Agent features, visit the AWS DevOps Agent pricing page.

For configuration details, visit the AWS DevOps Agent user guide.

— Esra

AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-waf-adds-ai-traffic-monetization-capability-to-help-content-owners-charge-ai-bots-for-content-access/

AWS WAF now includes AI traffic monetization capability that gives digital content owners and publishers a way to charge AI bots and agents for access to protected web content directly at the network edge. The capability helps content owners and publishers set per-request pricing by content path, bot category, or verification tier without modifying their origin infrastructure or writing application code. Content owners can define granular access policies per agent type, collect payments in stablecoins to their preferred wallet, and monitor revenue and bot activity from a single dashboard.

AI bot traffic now accounts for more than 50% of web traffic for many content providers, with AI-specific crawlers growing more than 300% year-over-year. Unlike traditional search engine crawlers, which index content and return measurable referral traffic back to publisher websites, AI bots consume the same content to generate summaries and responses in AI interfaces, with little to no traffic sent back to the original source. Publishers bear the infrastructure costs of serving that traffic without the page views, ad impressions, or subscription conversions that typically offset those costs. AWS WAF Bot Control already gives customers visibility into bot activity and the ability to block or rate-limit traffic, but setting pricing and collecting payment from AI agents has not been possible until now. AI traffic monetization is a new Bot Control capability that closes that gap, giving content owners and publishers a way to configure pricing rules directly through the AWS WAF console and collect payments from AI agents through third-party payment integrations, without building custom payment infrastructure or negotiating individual licensing agreements. Payment settlement and verification flows are provided by Coinbase’s x402 Facilitator. Integration with Stripe for direct account payments and Machine Payments Protocol (MPP) support is coming soon.

Getting Started with AI Traffic Monetization
Before configuring monetization, confirm that AWS WAF Bot Control is enabled at Common or Targeted level on the web ACL associated with your CloudFront distribution. Bot Control provides the agent classification that monetization rules depend on. If you have not set this up yet, visit Adding the AWS WAF Bot Control managed rule group to your web ACL documentation. In the AWS Management Console, go to WAF & Shield and choose Protection packs (web ACLs) in the left navigation pane to get started.

A protection pack is the core configuration unit for AI traffic monetization. It defines which content paths are monetized, what each agent verification tier is charged, which payment methods you accept, and what license terms apply. To create one, choose Create protection pack (web ACL).

In Tell us about your app, select one or more app categories that describe your content (for example, Content & publishing systems, E-commerce & transaction platforms, or Enterprise & business applications), and choose an App focus. AWS WAF uses these selections to recommend suitable security protections for your configuration.

In Select resources to protect, choose Add resources to associate regional or global resources such as CloudFront distributions with this protection pack. You can skip this step and add resources later.

In Choose initial protections, select from AWS WAF managed rule packages based on your app category and resource selections. You can also choose individual rules instead of packages.

In Name and describe, provide a name and optional description for the protection pack.

Optionally, expand Customize protection pack (web ACL) to configure additional settings including pricing tiers, payment methods, content scope, and license terms.

When finished, choose Create protection pack (web ACL).

Once your protection pack is in place, review the AI traffic analysis dashboard to understand the impact of AI bot traffic on your content before setting your pricing strategy. In the WAF & Shield console, go to AI traffic analysis in the left navigation pane. Select your protection pack (web ACL) from the dropdown to populate the dashboard.

The AI traffic analysis dashboard breaks down traffic into four categories visible in the bot traffic overview panel: All bot requests, AI bot requests, Verified AI bot traffic, and Unverified AI bot traffic. The dashboard surfaces infrastructure impact metrics including bandwidth consumed, estimated monthly cost, and peak request rates. A per-path heatmap shows which content paths receive the most AI bot activity by hour, giving you the data you need to make informed pricing decisions.

AWS WAF Bot Control classifies over 650 distinct AI bot and agent types including GPTBot, Claude-Web, and Perplexity-Bot, and assigns each a verification tier:

  • Verified — Agent identity confirmed through Web Bot Auth (WBA) Ed25519 cryptographic signature, or sourced from a documented IP range with a known set of user-agents and domain names.
  • Unverified — Agent recognized through user-agent matching, behavioral fingerprinting, and IP reputation, but identity not cryptographically confirmed.

Once you have reviewed your traffic patterns, return to Protection packs (web ACLs), select your protection pack from the list, and choose Configure AI monetization from the right panel to set pricing and access policies. Each protection pack defines the pricing, agent policies, accepted payment methods, and license terms that apply to a defined set of content paths. You can create multiple protection packs and apply different pricing to different content zones within the same distribution. Once created, associate the protection pack with your web ACL by opening the web ACL and choosing Add protection pack.

For each agent verification tier within the pack, you can assign one of six actions: Monetize (return a 402 with pricing), Allow (grant free access), Block (deny access entirely), Count (log without charging), CAPTCHA (present a puzzle to verify a human sender), or Challenge (run a silent check to verify the client is a browser, not a bot).

In the Edit monetization configuration page, configure the following:

Under Payment settlement, select one or more blockchain networks for stablecoin payments. Any wallet address on the supported networks is accepted, whether self-managed or hosted by a wallet provider such as Coinbase. For each network, provide your wallet address and set a Base price per page in USDC. You can add multiple networks using Add network. AWS does not process payments or take a fee on content revenue; disbursement is self-managed or managed by your wallet provider.

When a Monetize rule matches an incoming request, AWS WAF returns an HTTP 402 Payment Required response. The response body contains a machine-readable price manifest in JSON format using the x402 open protocol for machine-to-machine payments. The manifest includes the content price in USDC, accepted blockchain networks such as Base and Solana, the destination wallet address, the maximum payment timeout, and the payment scheme.

Any x402-compatible agent runtime can complete this flow autonomously. The client submits a signed payment authorization on their payment network of choice. AWS WAF verifies it, fetches the content, integrates with third-party facilitator services for settling the payment on-chain, and serves the response.

Note that the Monetize action is supported exclusively for web ACLs associated with Amazon CloudFront distributions. Adding a Monetize rule to a regional web ACL is not supported.

Since the Currency mode toggle is available directly in the monetization configuration page, you can switch between Real and Test mode at any time. Before going live, use test mode on non-production traffic to validate pricing, wallet configuration, and x402 payment flows. Note that test mode still enforces x402 payments, but those payments can be made on testnets such as Base Sepolia or Solana Devnet using test funds obtained from faucets such as faucet.circle.com. To activate test mode, toggle Currency mode to Test in your protection pack configuration. AWS WAF returns real price manifests and runs the full payment flow identically to production on the configured test chain. All events are logged with CurrencyMode: TEST. When satisfied with the configuration, toggle Currency mode back to Real to begin processing real payments.

Once you have switched Currency mode to Real, navigate to AI access monetization in the left navigation pane to track monetization outcomes in real time. Note that the AI access monetization dashboard only reflects activity from real currency mode and does not display test transactions.

The Revenue dashboard shows Total revenue, revenue broken down by Verified bots and Unverified bots, and Avg. per request. The Top revenue sources panel groups earnings by bot category, and the AI access patterns panel ranks content paths by revenue generated. Use the Settlements tab to reconcile payments by provider and review payment method distribution and failed payment attempts.

Now Available
AI traffic monetization is available now for Amazon CloudFront customers at no additional charge beyond standard AWS WAF pricing. The capability is available in all edge locations where AWS WAF web ACLs are associated with Amazon CloudFront distributions.

To learn more about AI traffic monetization, see the AWS WAF Developer Guide.

— Esra

AWS Weekly Roundup: AWS FinOps Agent in preview, Gemma 4 on Bedrock, Kiro Pro Max, and more (June 15, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-finops-agent-in-preview-gemma-4-on-bedrock-kiro-pro-max-and-more-june-15-2026/

This week, New York City is hosting AWS Summit, bringing together builders, customers, and AWS teams for a full day of announcements, demos, and technical sessions at the Javits Center. I wrote blog posts for some of the Summit launches, so I am excited to see them go live this week. I just won’t be watching from the Javits Center. I’ll be at a four-day music festival, following the launches on my phone while trying to figure out how to put up a tent. If you weren’t able to attend in person like me, the keynote livestream is available on June 17, with Dr. Swami Sivasubramanian, VP of Agentic AI, and Chet Kapoor, VP of Security Services and Observability, covering new capabilities across developer tools, AI infrastructure, and security.

Here’s what happened this week.

Headlines
How frontier teams are reinventing AI-native development — Swami published a detailed post this week drawing on data from experiments across hundreds of Amazon engineering teams. The findings are worth reading carefully if you are thinking about how to structure AI adoption on your own team.

A six-engineer team rebuilt the Amazon Bedrock inference engine in 76 days, a project originally scoped for 30 developers over 12 to 18 months. The median productivity gain across structured pilots with Amazon Stores teams was 4.5x in normalized deployment velocity, with some teams exceeding 10x. Perfect Order Experience went from a two-week feature cycle to shipping in an afternoon. WW Grocery cut design document creation from five days to a few hours.

The post distills these results into five practices for becoming a frontier team. First, invest in agent context: build steering files, coding standards, and structured repositories before writing production code. Second, expect an initial slowdown while workflows are restructured, and push through it. Third, maintain a steady backlog of well-scoped tasks so agents can run in parallel without constant supervision. Fourth, make intent explicit through structured specifications before code generation begins. Fifth, shift testing left so agents can self-correct before code reaches the pipeline.

The post closes with a note that commit velocity is only part of the picture, and that a follow-up will cover release management, operations, security operations, and EOL upgrades.

AWS FinOps Agent is now available in preview — AWS FinOps Agent is a new agent for FinOps practitioners and engineering teams that answers cost questions, surfaces optimization opportunities, investigates cost anomalies, and runs recurring FinOps workflows on a defined schedule. You can use it to query your AWS costs, generate cost reports for finance and engineering teams, and surface rightsizing, idle resource, and Savings Plans recommendations from AWS Cost Optimization Hub and AWS Compute Optimizer. The agent can open Jira tickets on your behalf based on those recommendations. When a cost anomaly is detected, FinOps Agent can automatically investigate the root cause and post findings to a Slack channel.

Last week’s launches
I’ll start with one I wrote this week, then cover the other launches that caught my attention:

  • Amazon EC2 M9g and M9gd instances are now generally available — Powered by AWS Graviton5 processors and built on the sixth-generation AWS Nitro System, M9g instances deliver up to 25% better compute performance compared to Graviton4-based instances, with up to 35% faster performance for web applications, up to 35% for machine learning inference, and up to 30% for databases. Graviton5 is the first processor in the AWS fleet to support PCIe Gen6 and DDR5-8800 memory, and includes a 5x larger L3 cache compared to the previous generation. M9g and M9gd instances offer up to 15% higher network bandwidth and 20% higher Amazon EBS bandwidth on average across sizes compared to M8g. This release also introduces the Nitro Isolation Engine, an enhancement to the Nitro System that uses formal verification to provide mathematically proven isolation between virtual machines — establishing Nitro as the first formally verified cloud hypervisor. M9gd instances add up to 11.4 TB of NVMe SSD local storage with 30% higher IOPS compared to M8gd. Both instance types support Instance Bandwidth Configuration (IBC) for adjusting bandwidth allocation between EBS and VPC networking by up to 25%.
  • Anthropic Claude Fable 5 on Amazon Bedrock — Claude Fable 5 launched on Amazon Bedrock on June 9, bringing extended asynchronous task execution, advanced vision capabilities across diagrams, charts, and PDFs, and proactive self-verification. Access requires opting into data sharing via the Data Retention API before invoking the model; Anthropic requires 30-day retention of inputs and outputs for Mythos-class models. Important note on availability: On June 12, Anthropic asked AWS to revoke access to Claude Fable 5 and Claude Mythos 5 for all users to support compliance with a US Government export control directive. All other models, including Opus 4.8, are unaffected. Read the Anthropic statement for details. AWS will share further updates as they become available.
  • Gemma 4 models are now available on Amazon Bedrock — The Gemma 4 family from Google DeepMind is now available on Amazon Bedrock across three variants: Gemma 4 31B (dense, 256K-token context window, suited for reasoning and coding workloads), Gemma 4 26B-A4B (mixture-of-experts architecture, targeting cost- and latency-sensitive workloads), and Gemma 4 E2B (smallest variant, designed for low-latency interactive use cases). All three support native function calling, structured output, reasoning, response streaming, multimodal input across text, image, video, and audio, and more than 35 languages.
  • Amazon OpenSearch Service launches MCP Apps for agentic observability — Amazon OpenSearch Service now supports MCP Apps, enabling observability workflows inside compatible agentic IDEs including Claude Desktop and VS Code. An AI agent in your local environment can investigate incidents using logs, traces, metrics, and alerts stored in OpenSearch domains, collections, and Amazon Managed Service for Prometheus. Each MCP App tool call returns a dual response: a text summary for the agent to reason over and an interactive visualization rendered in the same conversation thread. Available MCP App tools cover log, metrics, and trace investigation; service performance; topology; dynamic visualizations; agent health; cluster health; and instrumentation scoring.

Other AWS news
Here are some additional posts and updates you may find useful:

  • AWS CLI v1 enters maintenance mode — When CLI v1 enters maintenance mode, the botocore and s3transfer dependencies will be vendored directly into the CLI v1 codebase rather than installed as separate packages. This means upgrading CLI v1 will no longer update the standalone botocore or s3transfer packages, and installing those packages independently will have no effect on the versions used by CLI v1. Environments with both CLI v1 and boto3 installed will contain separate copies of these libraries. New CLI v1 releases will be limited to critical bug fixes and security issues. The recommended path is to migrate to AWS CLI v2.
  • AWS Workload Credentials Provider is now available — AWS has launched a new Workload Credentials Provider that enables workloads to obtain short-term AWS credentials without requiring long-term access keys. This supports credential management for applications running outside of AWS, giving teams a way to follow least-privilege access patterns for workloads in third-party or on-premises environments.
  • Kiro Pro Max is now available — Kiro has introduced a new Pro Max tier, adding higher usage limits, access to the latest frontier models, and additional agentic capabilities for development teams. Kiro Pro Max is designed for professional developers who need sustained, high-volume use across coding, specification generation, and agent-driven tasks.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building. You can also browse upcoming AWS-led in-person and virtual events, plus developer-focused sessions.

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Now available: Amazon EC2 M9g and M9gd instances powered by new AWS Graviton5 processors

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/now-available-amazon-ec2-m9g-and-m9gd-instances-powered-by-new-aws-graviton5-processors/

AWS Graviton processors have improved steadily across generations, with each iteration delivering advances in compute performance, price-performance, and energy efficiency. At re:Invent 2025, we announced Amazon EC2 M9g, the first Graviton5-powered instances, in preview. Since then, customers have tested M9g across a wide range of workloads and shared their results. ClickHouse saw a 36% performance boost compared to M8g, with zero code changes. Honeycomb achieved 36% better throughput per core compared to Graviton4, across a 6-month A/B test of production observability workloads. HubSpot deployed M9g for MySQL databases and saw query duration drop by up to 60%. Today, M9g instances are generally available, alongside the new M9gd instances for customers who need high-speed, low-latency local NVMe SSD storage. Both are powered by Graviton5, the most powerful and most energy efficient processor AWS has ever built.

While many Arm-based instances have been introduced across the industry, no one comes close to the breadth and depth of the AWS Graviton footprint. After five generations of custom silicon and eight years of continuous investment, Graviton powers over 350 instance types serving more than 120,000 customers, from startups to large enterprises, a robust ISV partner ecosystem, and a broad set of managed services. You can use Graviton for a broad variety of workloads, including web applications, microservices, analytics, databases, machine learning (ML) inference, electronic design automation (EDA), gaming, and video encoding. As workloads grow more compute-intensive and data-driven, many have asked for more processing power, along with greater network and storage bandwidth to move more data and complete workloads faster. We’ve also designed these instances to efficiently package compute, memory, and I/O to maximize energy utilization.

As AI shifts from answering questions to taking actions, running code, using tools, evaluating results, and orchestrating multi-step tasks, the demand for CPU compute is growing rapidly. Graviton5 is built for this shift. With 192 cores, a 5x larger L3 cache, up to 33% lower inter-core latency, and DDR5 memory delivering high bandwidth, Graviton5 helps agents spend less time waiting on CPU-bound steps, processing more instructions, handling large numbers of concurrent environments, and keeping accelerators moving.

Meta is deploying Graviton at scale starting with tens of millions of cores to support its agentic AI efforts, making Meta one of the largest Graviton customers in the world. Agentic AI workloads, including real-time reasoning, code generation, and the orchestration of multi-step tasks, are CPU-intensive and benefit from the higher compute performance, larger caches, higher memory bandwidth, and core density in Graviton5.

What’s new in M9g and M9gd
Built on the sixth-generation AWS Nitro System, M9g instances are powered by AWS Graviton5 processors that deliver higher compute performance, larger caches, and improved memory and I/O scalability compared to Graviton4 processors. Graviton5 offers up to 25% better compute performance compared to Graviton4-based instances, with up to 35% faster performance for web applications, up to 35% for machine learning inference, and up to 30% for databases. As the first CPU in the AWS fleet to support the latest generation of PCIe Gen6 and DDR5-8800 memory, AWS Graviton5 instances deliver the fastest memory of any processor instances in the cloud, and 5 times more L3 cache compared to the previous generation. These improvements also come with better energy efficiency, helping you meet sustainability targets without compromising capability.

Networking and storage bandwidth have been expanded to keep pace with compute growth. M9g and M9gd instances offer up to 15% higher network bandwidth and 20% higher Amazon Elastic Block Store (Amazon EBS) bandwidth on average across sizes, with up to twice the network bandwidth for the largest instance size. M9g and M9gd instances also support Instance Bandwidth Configuration (IBC), a feature that helps you adjust the allocation of bandwidth between Amazon EBS and Amazon Virtual Private Cloud (Amazon VPC) networking for an Amazon EC2 instance by up to 25%. IBC can help optimize performance for workloads with specific bandwidth requirements, such as database read and write performance, query processing, and logging. These enhancements support faster data movement and improved throughput for workloads that rely on high I/O performance.

Security and isolation are foundational requirements for running workloads in the cloud. Within the Nitro System, the AWS Nitro Hypervisor is designed to isolate instances from each other as well as AWS operators. With M9g and M9gd instances we are raising the bar on security even further with the introduction of Nitro Isolation Engine. Nitro Isolation Engine is an enhancement to the Nitro System, which enforces isolation of instances and harnesses formal verification to provide assurances of isolation with mathematical precision. Nitro Isolation Engine is a purpose-built component that is responsible for enforcing isolation between virtual machines, including mediation of all access to virtual machine memory, CPU register state, and I/O devices through a minimal set of APIs. Nitro Isolation Engine leverages formal verification, a technique to mathematically demonstrate that the hardware or software behaves as intended, and not just in specific test cases. This intensive verification technique establishes Nitro as the first formally verified cloud hypervisor, pioneering a new standard for mathematically proven cloud security.

M9g instances provide one vCPU for every four GiB of memory and are well suited for a broad range of general-purpose workloads, including application servers, microservices, midsize data stores, gaming servers, caching fleets, containerized applications, large-scale Java applications, code repositories, web applications, and agentic AI.

For workloads that need high-speed, low-latency local storage, M9gd instances provide up to 11.4 TB of NVMe SSD storage and 30% higher IOPS and storage performance compared to Graviton4-based M8gd instances. M9gd instances are well suited for general-purpose workloads that require a balance of compute and memory with high-speed, low-latency local storage, including application servers, microservices, gaming servers, midsize key-value data stores, caching fleets, data logging, media processing, batch and log processing, and applications that need temporary storage such as caches and scratch files.

Here are the key specifications across the family:

M9g vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
medium 1 4 Up to 15 Up to 12
large 2 8 Up to 15 Up to 12
xlarge 4 16 Up to 15 Up to 12
2xlarge 8 32 Up to 17 Up to 12
4xlarge 16 64 Up to 17 Up to 12
8xlarge 32 128 17 12
12xlarge 48 192 25 18
16xlarge 64 256 34 24
24xlarge 96 384 50 36
48xlarge 192 768 100 72
metal-48xl 192 768 100 72

M9gd instances include local NVMe SSD storage. The table below shows the instance storage for each size. Compute, memory, network, and EBS bandwidth specifications are the same as M9g.

M9gd vCPUs Memory (GiB) Instance storage (GB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
medium 1 4 1 x 59 NVMe SSD Up to 15 Up to 12
large 2 8 1 x 118 NVMe SSD Up to 15 Up to 12
xlarge 4 16 1 x 237 NVMe SSD Up to 15 Up to 12
2xlarge 8 32 1 x 475 NVMe SSD Up to 17 Up to 12
4xlarge 16 64 1 x 950 NVMe SSD Up to 17 Up to 12
8xlarge 32 128 1 x 1900 NVMe SSD 17 12
12xlarge 48 192 3 x 950 NVMe SSD 25 18
16xlarge 64 256 1 x 3800 NVMe SSD 34 24
24xlarge 96 384 3 x 1900 NVMe SSD 50 36
48xlarge 192 768 3 x 3800 NVMe SSD 100 72
metal-48xl 192 768 3 x 3800 NVMe SSD 100 72

Now available
M9g and M9gd instances are available in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Frankfurt) Regions. M9g and M9gd instances are available for purchase through Savings Plans, On-Demand, Spot Instances, Dedicated Instances, or Dedicated Hosts. For more information, visit Amazon EC2 pricing.

To get started with M9g and M9gd instances, several resources are available. The AWS Graviton Getting Started Guide is a technical guide covering how to build, run, and optimize workloads on Graviton-based instances. The Graviton Savings Dashboard helps you track and measure the cost savings from running workloads on Graviton-based instances. And AWS Transform is an AI-powered service that automates code transformations for migrating Java applications from x86 to Graviton-based Amazon EC2 instances, handling compatibility analysis, automated recompilation, dependency updates, and validation.

To learn more about Graviton-based instances, visit AWS Graviton Processors or Level up your compute with AWS Graviton.

— Esra

AWS Weekly Roundup: What’s Next with AWS 2026, Amazon Quick, OpenAI partnership, and more (May 4, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-whats-next-with-aws-2026-amazon-quick-openai-partnership-and-more-may-4-2026/

Last week, I took some time off in York, England, often described as the most haunted city in the country. I wandered through the ruins of abbeys that have stood for nearly a thousand years, walked along medieval walls, and spent an evening on a ghost tour hearing stories passed down through centuries. There’s something grounding about standing in a place that has witnessed so much history. Now I’m back at my desk, and the contrast is hard to miss: those abbey stones have stood for a thousand years largely unchanged, while in the span of a single week away, the pace of technological change has moved forward yet again.

The ruins of Whitby Abbey in North Yorkshire. Stones that have seen a thousand years, while this week alone brought another wave of change.

Now, let’s get into this week’s AWS news.

Headlines
On April 28, Matt Garman, CEO of AWS, Colleen Aubrey, SVP Amazon Applied AI Solutions, Julia White, CMO of AWS, and OpenAI leaders took the stage to share how customers are changing the way businesses operate with agents. The event brought a packed slate of announcements across Amazon Quick, Amazon Connect, and a deeper partnership with OpenAI. Here’s a roundup of the biggest announcements from the event.

Amazon Quick expands with a desktop app, new pricing plans, and visual asset generation – Amazon Quick is an AI assistant for work that connects to your apps, learns what matters to you, and takes action on your behalf. This week, Quick introduced a new desktop app (Preview) that keeps you connected to your local files, calendar, and communications without opening a browser. You can sign up within minutes using your personal email address or existing Google, Apple, Github, or Amazon credentials—no AWS account required. Quick can now generate polished documents, presentations, infographics, and images directly from the chat interface, and native integrations expand to include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams. A new Build custom apps with Quick capability (Preview) lets you create intelligent apps, dashboards, and web pages connected to the rest of your business using natural language.

Amazon Connect expands into four agentic AI solutions – Amazon Connect is expanding from a single product into a set of four agentic AI solutions designed to work within your existing workflows. Amazon Connect Decisions is a supply chain planning and intelligence solution that shifts teams from crisis management to proactive planning, combining 30 years of Amazon operational science with more than 25 specialized supply chain tools. Amazon Connect Talent (Preview) is an agentic AI hiring solution that delivers AI-led interviews, science-backed assessments, and consistent evaluation for talent acquisition leaders managing scaled hiring. Amazon Connect Customer, previously known as Amazon Connect, delivers personalized customer experiences across voice, chat, and digital channels, with new configuration capabilities that enable organizations to set up conversational AI in weeks rather than months. Amazon Connect Health delivers agentic patient verification, appointment management, patient insights, ambient documentation, and medical coding, giving patients faster access to care and clinicians more time to deliver it.

AWS and OpenAI expand their partnership across Amazon Bedrock – AWS and OpenAI are bringing the latest OpenAI models to Amazon Bedrock, launching Codex on Amazon Bedrock, and introducing Amazon Bedrock Managed Agents powered by OpenAI — all in limited preview. OpenAI models on Amazon Bedrock (Limited preview) brings the latest OpenAI models, including GPT-5.5 and GPT-5.4, to the Bedrock APIs you already use, with unified security, governance, and cost controls. No additional infrastructure to configure, no new security model to learn. Codex on Amazon Bedrock (Limited preview) lets you access the OpenAI coding agent within your existing AWS environments, authenticating with your AWS credentials, processing inference through Bedrock, and applying Codex usage toward your AWS cloud commitments. Codex on Bedrock is available through the Bedrock API, starting with the Codex CLI, the Codex desktop app, and a Visual Studio Code extension. Amazon Bedrock Managed Agents, powered by OpenAI (Limited preview) combines OpenAI frontier models with AWS infrastructure to build production-ready OpenAI-powered agents in the cloud, built with the OpenAI harness for faster execution, sharper reasoning, and reliable steering of long-running tasks.

To learn more, visit Top announcements of the What’s Next with AWS, 2026.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

  • Amazon EC2 M8in and M8ib instances are now generally available – Powered by custom 6th-gen Intel Xeon Scalable processors and 6th-gen AWS Nitro cards, these instances deliver up to 43% higher performance over M6in and M6ib. M8in offers 600 Gbps network bandwidth, while M8ib delivers up to 300 Gbps EBS bandwidth. Available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain).
  • Amazon EC2 R8in and R8ib instances are now generally available – Memory-optimized instances built on the same 6th-gen Intel Xeon Scalable processors and Nitro cards, with the same 600 Gbps network and 300 Gbps EBS bandwidth profiles. Well-suited for large commercial databases, data lakes, and in-memory databases such as SAP HANA. Available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Spain).
  • Amazon EC2 C8ine and M8ine instances are now generally available – Network-optimized instances offering up to 2.5x higher packet performance per vCPU and up to 2x higher network throughput for traffic through internet gateways compared to C6in and M6in. Designed for security and network virtual appliances including virtual firewalls, load balancers, and 5G UPF workloads. Available in US East (N. Virginia), US West (Oregon), and Asia Pacific (Tokyo) for C8ine; US East (N. Virginia) and US West (Oregon) for M8ine.
  • Amazon Bedrock AgentCore adds optimization capabilities (Preview) – AgentCore now offers recommendations, batch evaluations, and A/B tests to complete the observe-evaluate-improve loop for agents in production. Recommendations analyze production traces and evaluation outputs to propose optimized system prompts and tool descriptions, which you can validate with batch evaluations against pre-defined test cases or A/B tests against live traffic. Every recommendation requires your approval before it ships.
  • AWS Lambda adds support for Ruby 4.0 – Ruby 4.0, the latest LTS release, is available as a Lambda managed runtime and container base image. It includes support for Lambda advanced logging controls, including JSON structured logs, configurable logging levels, and target CloudWatch log group configuration. Available in all AWS Regions, including China Regions and AWS GovCloud (US).

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

  • Amazon Q Developer end-of-support announcement – Amazon Q Developer IDE plugins and paid subscriptions will reach end of support on April 30, 2027, giving customers 12 months to transition to Kiro. New signups will be blocked starting May 15, 2026, although existing subscriptions can continue to add users. Starting May 29, 2026, Opus 4.6 will no longer be available on Q Developer Pro; Opus 4.5 and other existing models remain available, and the latest coding models including Opus 4.7 are available exclusively on Kiro. Amazon Q Developer in the AWS Management Console and first-party AWS experiences (documentation, mobile app, Slack, and Microsoft Teams) are not affected.
  • AWS 10,000 AIdeas Competition: Meet the Winners – AWS announced the 20 winners of the 10,000 AIdeas Competition, a global challenge where builders submitted AI applications built entirely with Kiro and the AWS Free Tier, with submissions from 115 countries narrowed down through four rounds of evaluation and two rounds of community voting. Winners span Global Champions, Regional Champions, Innovation Awards, and Creative Track categories, with cash prizes and AWS credits awarded across each tier.
  • AWS Student Builder Groups – AWS Cloud Clubs is evolving to AWS Student Builder Groups. The community now spans 600+ colleges and universities across 63 countries. Existing Cloud Club memberships, badges, and progress carry forward, and Cloud Club Captains become Group Leaders. Membership is open to any learner 18 or older. You can find a group near you on AWS Builder Center or apply to launch a new group on your campus.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building. You can also browse upcoming AWS-led in-person and virtual events, plus developer-focused sessions.

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Weekly Roundup: Amazon S3 turns 20, Amazon Route 53 Global Resolver general availability, and more (March 16, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-s3-turns-20-amazon-route-53-global-resolver-general-availability-and-more-march-16-2026/

Twenty years ago this past week, Amazon S3 launched publicly on March 14, 2006. While Amazon Simple Storage Service is often considered the foundational storage service that defined cloud infrastructure, what began as a simple object storage service has grown into something far larger in scope and scale.

As of March 2026, S3 stores more than 500 trillion objects, serves more than 200 million requests per second globally across hundreds of exabytes of data, and the price has dropped to just over 2 cents per gigabyte — an approximately 85% reduction since launch. My colleague Sébastien Stormacq wrote a detailed look at the engineering and the road ahead in Twenty years of Amazon S3 and building what’s next, and if you want to read about those earliest customers and how they shaped what AWS became, I recommend How three startups helped Amazon invent cloud computing and paved the way for AI. Twenty years is worth pausing to celebrate.

Alongside the 20th anniversary of S3, Channy Yun also wrote about a new S3 feature this week: Account regional namespaces for Amazon S3 general purpose buckets. With this feature, you can create general purpose buckets in your own account regional namespace by appending your account’s unique suffix to your requested bucket name, ensuring your desired names are always reserved exclusively for your account. You can enforce adoption across your organization using AWS IAM policies and AWS Organizations service control policies with the new s3:x-amz-bucket-namespace condition key. Read Channy’s post to learn more about account regional namespaces for Amazon S3 general purpose buckets.

This week’s featured launch is one I have a personal connection to: the general availability of Amazon Route 53 Global Resolver. I wrote about the preview of this capability back in December at re:Invent 2025, and I had a great time putting that post together, so I am happy to hear that it’s generally available now.

Amazon Route 53 Global Resolver is an internet-reachable anycast DNS resolver that provides DNS resolution for authorized clients from any location. It is now generally available across 30 AWS Regions, with support for both IPv4 and IPv6 DNS query traffic. Route 53 Global Resolver gives authorized clients in your organization anycast DNS resolution of public internet domains and private domains associated with Route 53 private hosted zones — from any location, not just from within a specific VPC or Region. It also provides DNS query filtering to block potentially malicious domains, domains that are not safe for work, and domains associated with advanced DNS threats such as DNS tunneling and Domain Generation Algorithms (DGA). Centralized query logging is included as well. With general availability, Global Resolver adds protection against Dictionary DGA threats.

Last week’s launches
Here are some of the other announcements from last week:

  • Amazon Bedrock AgentCore Runtime now supports stateful MCP server features — Amazon Bedrock AgentCore Runtime now supports stateful Model Context Protocol (MCP) server features, enabling developers to build MCP servers that use elicitation, sampling, and progress notifications alongside existing support for resources, prompts, and tools. With stateful MCP sessions, each user session runs in a dedicated microVM with isolated resources, and the server maintains session context across multiple interactions using an Mcp-Session-Id header. Elicitation enables server-initiated, multi-turn conversations to gather structured input from users during tool execution. Sampling allows servers to request LLM-generated content from the client for tasks such as personalized recommendations. Progress notifications keep clients informed during long-running operations. To learn more, see the Amazon Bedrock AgentCore documentation.
  • Amazon WorkSpaces now supports Microsoft Windows Server 2025 — New bundles powered by Microsoft Windows Server 2025 are now available for Amazon WorkSpaces Personal and Amazon WorkSpaces Core. These bundles include security capabilities such as Trusted Platform Module 2.0 (TPM 2.0), Unified Extensible Firmware Interface (UEFI) Secure Boot, Secured-core server, Credential Guard, Hypervisor-protected Code Integrity (HVCI), and DNS-over-HTTPS. Existing Windows Server 2016, 2019, and 2022 bundles remain available. You can use the managed Windows Server 2025 bundles or create a custom bundle and image. This support is available in all AWS Regions where Amazon WorkSpaces is available. For more information, visit the Amazon WorkSpaces FAQs.
  • AWS Builder ID now supports Sign in with GitHub and Amazon — AWS Builder ID now supports two additional social login options: GitHub and Amazon. These options join the existing Google and Apple sign-in capabilities. With this update, developers can access their AWS Builder ID profile — and services including AWS Builder Center, AWS Training and Certification, and Kiro — using their existing GitHub or Amazon account credentials, without managing a separate set of credentials. To learn more and get started, visit the AWS Builder ID documentation.
  • Amazon Redshift introduces reusable templates for COPY operations — Amazon Redshift now supports templates for the COPY command, allowing you to store and reuse frequently used COPY parameters. Templates help maintain consistency across data ingestion operations, reduce the effort required to execute COPY commands, and simplify maintenance by applying template updates automatically to all future uses. Support for COPY templates is available in all AWS Regions where Amazon Redshift is available, including the AWS GovCloud (US) Regions. To get started, see the documentation or read the Standardize Amazon Redshift operations using Templates blog.

For a full list of AWS announcements, be sure to keep an eye on our News Blog channel the What’s New with AWS page.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

AWS Summits – Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), and Bengaluru (April 23–24).

AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Pune (March 21), San Francisco (April 10), and Romania (April 23-24).

AWS at NVIDIA GTC 2026 — Join us at our AWS sessions, booths, demos, and ancillary events in NVIDIA GTC 2026 on March 16 – 19, 2026 in San Jose. You can receive 20% off event passes through AWS and request a 1:1 meeting at GTC.

AWS Community GameDay Europe — Taking place on March 17, 2026, AWS Community GameDay Europe is a team-based, hands-on AWS challenge event running simultaneously across 50+ cities in Europe. Your team is dropped into a broken AWS environment — misconfigured services, failing architectures, and security gaps — and has two hours to fix as much as possible. Find your nearest city and sign up at awsgameday.eu.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

AWS Weekly Roundup: Amazon EC2 M8azn instances, new open weights models in Amazon Bedrock, and more (February 16, 2026)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-ec2-m8azn-instances-new-open-weights-models-in-amazon-bedrock-and-more-february-16-2026/

I joined AWS in 2021, and since then I’ve watched the Amazon Elastic Compute Cloud (Amazon EC2) instance family grow at a pace that still surprises me. From AWS Graviton-powered instances to specialized accelerated computing options, it feels like every few months there’s a new instance type landing that pushes performance boundaries further. As of February 2026, AWS offers over 1,160 Amazon EC2 instance types, and that number keeps climbing.

This week’s opening news is a good example: The general availability of Amazon EC2 M8azn instances. These are general purpose, high-frequency, high-network instances powered by fifth generation AMD EPYC processors, offering the highest maximum CPU frequency in the cloud at 5 GHz. Compared to the previous generation M5zn instances, M8azn instances deliver up to 2x compute performance, 4.3x higher memory bandwidth, and a 10x larger L3 cache. They also provide up to 2x networking throughput and up to 3x Amazon Elastic Block Store (Amazon EBS) throughput compared with M5zn.

Built on the AWS Nitro System using sixth generation Nitro Cards, M8azn instances target workloads such as real-time financial analytics, high-performance computing, high-frequency trading, CI/CD pipelines, gaming, and simulation modeling across automotive, aerospace, energy, and telecommunications. The instances feature a 4:1 ratio of memory to vCPU and are available in 9 sizes ranging from 2 to 96 vCPUs with up to 384 GiB of memory, including two bare metal variants. For more information visit the Amazon EC2 M8azn instance page.

Last week’s launches
Here are some of the other announcements from last week:

  • Amazon Bedrock adds support for six fully managed open weights models – Amazon Bedrock now supports DeepSeek V3.2, MiniMax M2.1, GLM 4.7, GLM 4.7 Flash, Kimi K2.5, and Qwen3 Coder Next. These models span frontier reasoning and agentic coding workloads. DeepSeek V3.2 and Kimi K2.5 target reasoning and agentic intelligence, GLM 4.7 and MiniMax M2.1 support autonomous coding with large output windows, and Qwen3 Coder Next and GLM 4.7 Flash provide cost-efficient alternatives for production deployment. These models are powered by Project Mantle and provide out-of-the-box compatibility with OpenAI API specifications. With the launch, you can also use new open weight models–DeepSeek v3.2 , MiniMax 2.1, and Qwen3 Coder Next in Kiro, a spec-driven AI development tool.
  • Amazon Bedrock expands support for AWS PrivateLink – Amazon Bedrock now supports AWS PrivateLink for the bedrock-mantle endpoint, in addition to existing support for the bedrock-runtime endpoint. The bedrock-mantle endpoint is powered by Project Mantle, a distributed inference engine for large-scale machine learning model serving on Amazon Bedrock. Project Mantle provides serverless inference with quality of service controls, higher default customer quotas with automated capacity management, and out-of-the-box compatibility with OpenAI API specifications. AWS PrivateLink support for OpenAI API-compatible endpoints is available in 14 AWS Regions. To get started, visit the Amazon Bedrock console or the OpenAI API compatibility documentation.
  • Amazon EKS Auto Mode announces enhanced logging for managed Kubernetes capabilities – You can now configure log delivery sources using Amazon CloudWatch Vended Logs in Amazon EKS Auto Mode. This helps you collect logs from Auto Mode’s managed Kubernetes capabilities for compute autoscaling, block storage, load balancing, and pod networking. Each Auto Mode capability can be configured as a CloudWatch Vended Logs delivery source with built-in AWS authentication and authorization at a reduced price compared to standard CloudWatch Logs. You can deliver logs to CloudWatch Logs, Amazon S3, or Amazon Data Firehose destinations. This feature is available in all Regions where EKS Auto Mode is available.
  • Amazon OpenSearch Serverless now supports Collection Groups – You can use new Collection Groups to share OpenSearch Compute Units (OCUs) across collections with different AWS Key Management Service (AWS KMS) keys. Collection Groups reduce overall OCU costs through a shared compute model while maintaining collection-level security and access controls. They also introduce the ability to specify minimum OCU allocations alongside maximum OCU limits, providing guaranteed baseline capacity at startup for latency-sensitive applications. Collection Groups are available in all Regions where Amazon OpenSearch Serverless is currently available.
  • Amazon RDS now supports backup configuration when restoring snapshots – You can view and modify the backup retention period and preferred backup window before and during snapshot restore operations. Previously, restored database instances and clusters inherited backup parameter values from snapshot metadata and could only be modified after restore was complete. You can now view backup settings as part of automated backups and snapshots, and specify or modify these values when restoring, eliminating the need for post-restoration modifications. This is available for all Amazon RDS database engines (MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Db2) and Amazon Aurora (MySQL-Compatible and PostgreSQL-Compatible editions) in all AWS commercial Regions and AWS GovCloud (US) Regions at no additional cost.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

AWS Summits – Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), and Bengaluru (April 23–24).

AWS AI and Data Conference 2026 – A free, single-day in-person event on March 12 at the Lyrath Convention Centre in Ireland. The conference covers designing, training, and deploying agents with Amazon Bedrock, Amazon SageMaker, and QuickSight, integrating them with AWS data services, and applying governance practices to operate them at scale. The agenda includes strategic guidance and hands-on labs for architects, developers, and business leaders.

AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Ahmedabad (February 28), Slovakia (March 11), and Pune (March 21).

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

 

New AWS Security Agent secures applications proactively from design to deployment (preview)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/new-aws-security-agent-secures-applications-proactively-from-design-to-deployment-preview/

Today, we’re announcing AWS Security Agent in preview, a frontier agent that proactively secures your applications throughout the development lifecycle. It conducts automated application security reviews tailored to your organizational requirements and delivers context-aware penetration testing on demand. By continuously validating application security from design to deployment, it helps prevent vulnerabilities early in development.

Static application security testing (SAST) tools examine code without runtime context, whereas dynamic application security testing (DAST) tools assess running applications without application-level context. Both types of tools are one-dimensional because they don’t understand your application context. They don’t understand how your application is designed, what security threats it faces, and where and how it runs. This forces security teams to manually review everything, creating delays. Penetration testing is even slower—you either wait weeks for an external vendor or your internal security team to find time. When every application requires a manual security review and penetration test, the backlog grows quickly. Applications wait weeks or months for security validation before they can launch. This creates a gap between the frequency of software releases and the frequency of security evaluations. Security is not applied to the entire portfolio of applications, leaving customers exposed and knowingly shipping vulnerable code to meet deadlines. Over 60 percent of organizations update web applications weekly or more often, while nearly 75 percent test web applications monthly or less often. A 2025 report from Checkmarx found that 81 percent of organizations knowingly deploy vulnerable code to meet delivery deadlines.

AWS Security Agent is context-aware—it understands your entire application. It understands your application design, your code, and your specific security requirements. It continuously scans for security violations automatically and runs penetration tests on-demand instantly without scheduling. The penetration testing agent creates a customized attack plan informed by the context it has learned from your security requirements, design documents, and source code, and dynamically adapts as it runs based on what it discovers, such as endpoints, status and error codes, and credentials. This helps surface deeper, more sophisticated vulnerabilities before production, ensuring your application is secure before it launches without delays or surprises.

“SmugMug is excited to add AWS Security Agent to our automated security portfolio. AWS Security Agent transforms our security ROI by enabling pen test assessments that complete in hours rather than days, at a fraction of manual testing costs. We can now assess our services more frequently, dramatically decreasing the time to identify and address issues earlier in the software development lifecycle.” says Erik Giberti, Sr. Director of Product Engineering at SmugMug.

Get started with AWS Security Agent
AWS Security Agent provides design security review, code security review, and on-demand penetration testing capabilities. Design and code review check organizational security requirements that you define, and penetration testing learns application context from source code and specifications to identify vulnerabilities. To get started, navigate to the AWS Security Agent console. The console landing page provides an overview of how AWS Security Agent delivers continuous security assessment across your development lifecycle.

The Get started with AWS Security Agent panel on the right side of the landing page guides you through initial configuration. Choose Set up AWS Security Agent to create your first agent space and begin performing security reviews on your applications.

Provide an Agent space name to identify which agent you’re interacting with across different security assessments. An agent space is an organizational container that represents a distinct application or project you want to secure. Each agent space has its own testing scope, security configuration, and dedicated web application domain. We recommend creating one agent space per application or project to maintain clear boundaries and organized security assessments. You can optionally add a Description to provide context about the agent space’s purpose for other administrators.

When you create the first agent space in the AWS Management Console, AWS creates the Security Agent Web Application. The Security Agent Web Application is where users conduct design reviews and execute penetration tests within the boundaries established by administrators in the console. Users select which agent space to work in when conducting design reviews or executing penetration tests.

During the setup process, AWS Security Agent offers two options for managing user access to the Security Agent Web Application: Single Sign-On (SSO) with IAM Identity Center, which enables team-wide SSO access by integrating with AWS IAM Identity Center, or IAM users, which allows only AWS Identity and Access Management (IAM) users of this AWS account to access the Security Agent Web Application directly through the console and is best for quick setup or access without SSO configuration. When you choose the SSO option, AWS Security Agent creates an IAM Identity Center instance to provide centralized authentication and user management for AppSec team members who will access design reviews, code reviews, and penetration testing capabilities through the Security Agent Web Application.

The permissions configuration section helps you control how AWS Security Agent accesses other AWS services, APIs, and accounts. You can create a default IAM role that AWS Security Agent will use to access resources, or choose an existing role with appropriate permissions.

After completing the initial configuration, choose Set up AWS Security Agent to create the agent.

After creating an agent space, the agent configuration page displays three capability cards: Design review, Code review, and Penetration testing. While not required to operate the penetration testing, if you plan to use design review or code review capabilities, you can configure which security requirements will guide those assessments. AWS Security Agent includes AWS managed requirements, and you can optionally define custom requirements tailored to your organization. You can also manage which team members have access to the agent.

Security requirements
AWS Security Agent enforces organizational security requirements that you define so that applications comply with your team’s policies and standards. Security requirements specify the controls and policies that your applications must follow during both design and code review phases.

To manage security requirements, navigate to Security requirements in the navigation pane. These requirements are shared across all agent spaces and apply to both design and code reviews.

Managed security requirements are based on industry standards and best practices. These requirements are ready to use, maintained by AWS, and you can enable them instantly without configuration.

When creating a custom security requirement, you specify the control name and description that defines the policy. For example, you might create a requirement called Network Segmentation Strategy Defined that requires designs to define clear network segmentation separating workload components into logical layers based on data sensitivity. Or you might define Short Session Timeouts for Privileged and PII Access to mandate specific timeout durations for administrative and personally identifiable information (PII) access. Another example is Customer-Managed Encryption Keys Required, which requires designs to specify customer managed AWS Key Management Service (AWS KMS) keys rather than AWS managed keys for encrypting sensitive data at rest. AWS Security Agent evaluates designs and code against these enabled requirements, identifying policy violations.

Design security review
The design review capability analyzes architectural documents and product specifications to identify security risks before code is written. AppSec teams upload design documents through the AWS Security Agent console or ingest them from S3 and other connected services. AWS Security Agent assesses compliance with organizational security requirements and provides remediation guidance.

Before conducting design reviews, confirm you’ve configured the security requirements that AWS Security Agent will check. You can get started with AWS managed security requirements or define custom requirements tailored to your organization, as described in the Security requirements section.

To get started with the Design review, choose Admin access under Web app access to access the web app interface. When logged in, choose Create design review. Enter a Design review name to identify the assessment—for example, when assessing a new feature design that extends your application—and upload up to five design files. Choose Start design review to begin the assessment against your enabled security requirements.

After completing a design review, the design review detail page displays the review status, completion date, and files reviewed in the Details section. The Findings summary shows the count of findings across four compliance status categories:

  • Non-compliant – The design violates or inadequately addresses the security requirement.
  • Insufficient data – The uploaded files don’t contain enough information to determine compliance.
  • Compliant – The design meets the security requirement based on the uploaded documentation.
  • Not applicable – The security requirement’s relevance criteria indicate it doesn’t apply to this system design.

The Findings summary section helps you quickly assess which security requirements need attention. Non-compliant findings require updates to your design documents, while Insufficient data findings indicate gaps in the documentation where security teams should work with application teams to gather additional clarity before AWS Security Agent can complete the assessment.

The Files reviewed section displays all uploaded documents with options to search and download the original files.

The Review findings section lists each security requirement evaluated during the review along with its compliance status. In this example, the findings include Network Segmentation Strategy Defined, Customer-Managed Encryption Keys Required, and Short Session Timeouts for Privileged and PII Access. These are the custom security requirements defined earlier in the Security requirements section. You can search for specific security requirements or filter findings by compliance status to focus on items that require action.

When you choose a specific finding, AWS Security Agent displays detailed justification explaining the compliance status and provides recommended remediation steps. This context-aware analysis helps you understand security concerns specific to your design rather than generic security guidance. For designs with noncompliant findings, you can update your documentation to address the security requirements and create a new design review to validate the improvements. You can also choose Clone this design review to create a new assessment based on the current configuration or choose Download report to export the complete findings for sharing with your team.

After validating that your application design meets organizational security requirements, the next step is enforcing those same requirements as developers write code.

Code security review
The code review capability analyzes pull requests in GitHub to identify security vulnerabilities and organizational policy violations. AWS Security Agent detects OWASP Top Ten common vulnerabilities such as SQL injection, cross-site scripting, and inadequate input validation. It also enforces the same organizational security requirements used in design review, implementing code compliance with your team’s policies beyond common vulnerabilities.

When your application checks in new code, AWS Security Agent verifies compliance with organizational security requirements that go beyond common vulnerabilities. For example, if your organization requires audit logs to be retained for only 90 days, AWS Security Agent identifies when code configures a 365-day retention period and comments on the pull request with the specific violation. This catches policy violations that traditional security tools miss because the code is technically functional and secure.

To enable code review, choose Enable code review on the agent configuration page and connect your GitHub repositories. You can enable code review for specific repositories or connect repositories without enabling code review if you want to use them for penetration testing context instead.

For detailed setup instructions, visit the AWS Security Agent documentation.

On-demand penetration testing
The on-demand penetration testing capability executes comprehensive security testing to discover and validate vulnerabilities through multistep attack scenarios. AWS Security Agent systematically discovers the application’s attack surface through reconnaissance and endpoint enumeration, then deploys specialized agents to execute security testing across 13 risk categories, including authentication, authorization, and injection attacks. When provided source code, API specifications, and business documentation, AWS Security Agent builds deeper context about the application’s architecture and business rules to generate more targeted test cases. It adapts testing based on application responses and adjusts attack strategies as it discovers new information during the assessment.

AWS Security Agent tests web applications and APIs against OWASP Top Ten vulnerability types, identifying exploitable issues that static analysis tools miss. For example, while dynamic application security testing (DAST) tools look for direct server-side template injection (SSTI) payloads, AWS Security Agent can combine SSTI attacks with error forcing and debug output analysis to execute more complex exploits. AppSec teams define their testing scope—target URLs, authentication details, threat models, and documentation—the same as they would brief a human penetration tester. Using this understanding, AWS Security Agent develops application context and autonomously executes sophisticated attack chains to discover and validate vulnerabilities. This transforms penetration testing from a periodic bottleneck into a continuous security practice, reducing risk exposure.

To enable penetration testing, choose Enable penetration test on the agent configuration page. You can configure target domains, VPC settings for private endpoints, authentication credentials, and additional context sources such as GitHub repositories or S3 buckets. You must verify ownership of each domain before AWS Security Agent can run penetration testing.

After enabling the capability, create and run penetration tests through the AWS Security Agent Web Application. For detailed setup and configuration instructions, visit the AWS Security Agent documentation.

After creating and running a penetration test, the detail page provides an overview of test execution and results. You can run new tests or modify the configuration from this page. The page displays information about the most recent execution, including start time, status, duration, and a summary of discovered vulnerabilities categorized by severity. You can also view a history of all previous test runs with their findings summaries.

For each run, the detail page provides three tabs. The Penetration test run overview tab displays high-level information about the execution, including duration and overall status. The Penetration test logs tab lists all tasks executed during the penetration test, providing visibility into how AWS Security Agent discovered vulnerabilities, including the security testing actions performed, application responses, and the reasoning behind each test. The Findings tab displays all discovered vulnerabilities with complete details, including descriptions, attack reasoning, steps to reproduce, impact, and remediation guidance.

Join the preview
To get started with AWS Security Agent, visit the AWS Security Agent console and create your first agent to begin automating design reviews, code reviews, and penetration testing across your development lifecycle. During the preview period, AWS Security Agent is free of charge.

AWS Security Agent is available in the US East (N. Virginia) Region.

To learn more, visit the AWS Security Agent product page and technical documentation.

— Esra

AWS Security Hub now generally available with near real-time analytics and risk prioritization

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-security-hub-now-generally-available-with-near-real-time-analytics-and-risk-prioritization/

Today, AWS Security Hub is generally available, transforming how security teams identify and respond to critical security risks across their AWS environments. These new capabilities were first announced in preview at AWS re:Inforce 2025. Security Hub prioritizes your critical security issues and unifies your security operations to help you respond at scale by correlating and enriching signals across multiple AWS security services. Security Hub provides near real-time risk analytics, trends, unified enablement, streamlined pricing, and automated correlation that transforms security signals into actionable insights.

Organizations deploying multiple security tools need to manually correlate signals across different consoles, creating operational overhead that can delay detection and response times. Security teams use various tools for threat detection, vulnerability management, security posture monitoring, and sensitive data discovery, but extracting value from the findings these tools generate requires significant manual effort to understand relationships and determine priority.

Security Hub addresses these challenges through built-in integration that unifies your cloud security operations. Available for individual accounts or entire AWS Organizations accounts, Security Hub automatically aggregates and correlates signals from Amazon GuardDuty, Amazon Inspector, AWS Security Hub Cloud Security Posture Management (AWS Security Hub CSPM), and Amazon Macie, organizing them by threats, exposures, resources, and security coverage. This unified approach reduces manual correlation work, helping you quickly identify critical issues, understand coverage gaps, and prioritize remediation based on severity and impact.

What’s new in general availability
Since the preview announcement, Security Hub has added several new features.

Historical trends
Security Hub includes a Trends feature through the Summary dashboard that provides up to 1 year of historical data for findings and resources across your organization. The Summary dashboard displays an overview of your exposures, threats, resources, and security coverage through customizable widgets that you can add, remove, and arrange based on your operational needs.

The dashboard includes a Trends overview widget that displays period-over-period analysis for day-over-day, week-over-week, and month-over-month comparisons, helping you track whether your security posture is improving or degrading. Trend widgets for Active threat findings, Active exposure findings, and Resource trends provide visualizations of average counts over selectable time periods including 5 days, 30 days, 90 days, 6 months, and 1 year. You can filter these visualizations by severity levels such as critical, high, medium, and low, and hover over specific points in time to review detailed counts.

The Summary dashboard also includes widgets that display current exposure summaries prioritized by severity, threat summaries showing malicious or suspicious activity, and resource inventories organized by type and associated findings.

The Security coverage widget helps you identify gaps in your security service deployment across your organization. This widget tracks which AWS accounts and Regions have security services enabled, helping you understand where you might lack visibility into threats, vulnerabilities, misconfigurations, or sensitive data. The widget displays account coverage across security capabilities including vulnerability management by Amazon Inspector, threat detection by GuardDuty, sensitive data discovery by Amazon Macie, and posture management by AWS Security Hub CSPM. Coverage percentages show which security checks passed or failed across your AWS accounts and Regions where Security Hub is enabled.

You can apply filters to widgets using shared filters that apply across all widgets, finding filters for exposure and threat data, or resource filters for inventory data. You can create and save filter sets using and/or operators to define specific criteria for your security analysis. Dashboard customizations, including saved filter sets and widget layouts, are saved automatically and persist across sessions.

If you configure cross-Region aggregation, the Summary dashboard includes findings from all linked Regions when viewing from your home Region. For delegated administrator accounts in AWS Organizations, data includes findings for both the administrator account and member accounts. Security Hub retains trends data for 1 year from the date findings are generated. After 1 year, trends data is automatically deleted.

Near real-time risk analytics
Security Hub now calculates exposures in near real-time and includes threat correlation from GuardDuty alongside existing vulnerability and misconfiguration analysis. When GuardDuty detects threats, Amazon Inspector identifies vulnerabilities, or AWS Security Hub CSPM discovers misconfigurations, Security Hub automatically correlates these findings and updates associated exposures. This advancement provides immediate feedback on your security posture, helping you quickly identify new exposures and verify that remediation actions have reduced risk as expected.

Security Hub correlates findings across AWS Security Hub CSPM, Amazon Inspector, Amazon Macie, Amazon GuardDuty, and other security services to identify exposures that could lead to security incidents. This correlation helps you understand when multiple security issues combine to create critical risk. Security Hub enriches security signals with context by analyzing resource associations, potential impact, and relationships between signals. For example, if Security Hub identifies an Amazon Simple Storage Service (Amazon S3) bucket containing sensitive data with versioning disabled, Object Lock disabled, and MFA delete disabled, remediating any component triggers automatic calculation, helping you verify remediation effectiveness without waiting for scheduled assessments.

The Exposure page organizes findings by title and severity, helping you focus on critical issues first. The page includes an Overview section with a trends graph that displays the average count of exposure findings over the last 90 days, segmented by severity level. This visualization helps you track changes in your exposure posture over time and identify patterns in security risk.

Exposure findings are grouped by title with expandable rows showing the count of affected resources and overall severity. Each exposure title describes the potential security impact, such as “Potential Data Destruction: S3 bucket with versioning, Object Lock, and MFA delete disabled” or “Potential Remote Execution: EC2 instance is reachable from VPC and has software vulnerabilities.” You can filter exposures using saved filter sets or quick filters based on severity levels including critical, high, medium, and low. The interface also provides filtering by account ID, resource type, and accounts, helping you quickly narrow down exposures relevant to specific parts of your infrastructure.

Security Hub generates exposures as soon as findings are available. For example, when you deploy an Amazon Elastic Compute Cloud (Amazon EC2) instance that is publicly accessible and Amazon Inspector detects a highly exploitable vulnerability while AWS Security Hub CSPM identifies the public accessibility configuration, Security Hub automatically correlates these findings to generate an exposure without waiting for a scheduled assessment. This near-real time correlation helps you identify critical risks in newly deployed resources and take action before they can be exploited.

When you select an exposure finding, the details page displays the exposure type, primary resource, Region, account, age, and creation time. The Overview section shows contributing traits that represent the security issues directly contributing to the exposure scenario. These traits are organized by categories such as Reachability, Vulnerability, Sensitive data, Misconfiguration, and Assumability.

The details page includes a Potential attack path tab that provides a visual graph showing how potential attackers could access and take control of your resources. This visualization displays the relationships between the primary resource (such as an EC2 instance), involved resources (such as VPC, subnet, network interface, security group, AWS Identity and Access Management (IAM) instance profile, IAM role, IAM policy, and volumes), and contributing traits. The graph helps you understand the complete attack surface and identify which security controls need adjustment.

The Traits tab lists all security issues contributing to the exposure, and the Resources tab shows all affected resources. The Remediation section provides prioritized guidance with links to documentation, recommending which traits to address first to reduce risk most effectively. By using this comprehensive view, you can investigate specific exposures, understand the full context of security risks, and track remediation progress as your team addresses vulnerabilities, misconfigurations, and other security gaps across your environment.

Expanded partner integrations
Security Hub supports integration with Jira and ServiceNow for incident management workflows. When viewing a finding, you can create a ticket in your preferred system directly from the AWS Security Hub console with finding details, severity, and recommended remediation steps automatically populated. You can also define automation rules in Security Hub that automatically create tickets in Atlassian’s Jira Service Management and ServiceNow based on criteria you specify, such as severity level, resource type, or finding type. This helps you route critical security issues to your incident response teams without manual intervention.

Security Hub findings are formatted in the Open Cybersecurity Schema Framework (OCSF) schema, an open-source standard that enables security tools to share data seamlessly. Partners who have built integrations with the OCSF format with Security Hub include Cribl, CrowdStrike, Databee, DataDog, Dynatrace, Expel, Graylog, Netskope, Securonix, SentinelOne, Splunk a Cisco company, Sumo Logic, Tines, Upwind Security, Varonis, DTEX, and Zscaler. Additionally, service partners such as Accenture, Caylent, Deloitte, Optiv, PwC, and Wipro can help you adopt Security Hub and the OCSF schema.

Security Hub also supports automated response workflows through Amazon EventBridge. You can create EventBridge rules that identify findings based on criteria you specify and route them to targets such as AWS Lambda functions or AWS Systems Manager Automation runbooks for processing and remediation. This helps you act on findings programmatically without manual intervention.

Now available
If you currently use AWS Security Hub CSPM, Amazon GuardDuty, Amazon Inspector, or Amazon Macie, you can access these capabilities by navigating to the AWS Security Hub console. If you’re a new customer, you can enable Security Hub through the AWS Management Console and configure the security services appropriate for your workloads. Security Hub automatically consumes findings from enabled services, making the findings available in the unified console and creating correlated exposure findings based on the ingested security data.

For Regional availability, visit our AWS Services by Region page. Near real-time exposure calculation and the Trends feature are included at no additional charge. Security Hub uses a streamlined, resource-based pricing model that consolidates charges across integrated AWS security services. The console includes a cost estimator to help you plan and forecast security investments across your AWS accounts and Regions before deployment. For detailed information about capabilities, supported integrations, and pricing, visit the AWS Security Hub product page and technical documentation.

— Esra

Introducing Amazon Route 53 Global Resolver for secure anycast DNS resolution (preview)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/introducing-amazon-route-53-global-resolver-for-secure-anycast-dns-resolution-preview/

Today, we’re announcing Amazon Route 53 Global Resolver, a new Amazon Route 53 service that provides secure and reliable DNS resolution globally for queries from anywhere (preview). You can use Global Resolver to resolve DNS queries to public domains on the internet and private domains associated with Route 53 private hosted zones. Route 53 Global Resolver offers network administrators a unified solution to resolve queries from authenticated clients and sources in on-premises data centers, branch offices, and remote locations through globally distributed anycast IP addresses. This service includes built-in security controls including DNS traffic filtering, support for encrypted queries, and centralized logging to help organizations reduce operational overhead while maintaining compliance with security requirements.

Organizations with hybrid deployments face operational complexity when managing DNS resolution across distributed environments. Resolving public internet domains and private application domains often requires maintaining split DNS infrastructure, which increases cost and administrative burden especially when replicating to multiple locations. Network administrators must configure custom forwarding solutions, deploy Route 53 Resolver endpoints for private domain resolution, and implement separate security controls across different locations. Additionally, they must configure and maintain multi-Region failover strategies for Route 53 Resolver endpoints and provide consistent security policy enforcement across all Regions while testing failover scenarios.

Route 53 Global Resolver has key capabilities that address these challenges. The service resolves both public internet domains and Route 53 private hosted zones, eliminating the need for separate split-DNS forwarding. It provides DNS resolution through multiple protocols, including DNS over UDP (Do53), DNS-over-HTTPS (DoH), and DNS-over-TLS (DoT). Each deployment provides a single set of common IPv4 and IPv6 anycast IP addresses that route queries to the nearest AWS Region, reducing latency for distributed client populations.

Route 53 Global Resolver provides integrated security features equivalent to Route 53 Resolver DNS Firewall. Administrators can configure filtering rules using AWS Managed Domain Lists that provide flexible controls with lists classified by DNS threats (malware, spam, phishing) or web content (adult sites, gambling, social networking) that might not be safe for work or create custom domain lists by importing domains from a file. Advanced threat protection detects and blocks domain generation algorithm (DGA) patterns and DNS tunneling attempts. For encrypted DNS traffic, Route 53 Global Resolver supports DoH and DoT protocols to protect queries from unauthorized access during transit.

Route 53 Global Resolver only accepts traffic from known clients that need to authenticate with the Resolver. For Do53, DoT, and DoH connections, administrators can configure IP and CIDR allowlists. For DoH and DoT connections, token-based authentication provides granular access control with customizable expiration periods and revocation capabilities. Administrators can assign tokens to specific client groups or individual devices based on organizational requirements.

Route 53 Global Resolver supports DNSSEC validation to verify the authenticity and integrity of DNS responses from public nameservers. It also includes EDNS Client Subnet support, which forwards client subnet information to enable more accurate geographic-based DNS responses from content delivery networks.

Getting started with Route 53 Global Resolver
This walkthrough shows how to configure Route 53 Global Resolver for an organization with offices on the US East and West coasts that needs to resolve both public domains and private applications hosted in Route 53 private hosted zones. To configure Route 53 Global Resolver, go to the AWS Management Console, choose Global resolvers from the navigation pane, and choose Create global resolver.

In the Resolver details section, enter a Resolver name such as corporate-dns-resolver. Add an optional description like DNS resolver for corporate offices and remote clients. In the Regions section, choose the AWS Regions where you want the resolver to operate, such as US East (N. Virginia) and US West (Oregon). The anycast architecture routes DNS queries from your clients to the nearest selected Region.

After the resolver is created, the console displays the resolver details, including the anycast IPv4 and IPv6 addresses that you will use for DNS queries. You can proceed to create a DNS view by choosing Create DNS view to configure client authentication and DNS query resolution settings.

In the Create DNS view section, enter a DNS view name such as primary-view and optionally add a Description like DNS view for corporate offices. A DNS view helps you create different logical groupings for your clients and sources, and determine the DNS resolution for those groups. This helps you maintain different DNS filtering rules and private hosted zone resolution policies for different clients in your organization.

For DNSSEC validation, choose Enable to verify the authenticity of DNS responses from public DNS servers. For Firewall rules fail open behavior, choose Disable to block DNS queries when firewall rules can’t be evaluated, which provides additional security. For EDNS client subnet, keep Enable selected to forward client location information to DNS servers, which allows content delivery networks to provide more accurate geographic responses. DNS view creation might take a few minutes to become operational.

After the DNS view is created and operational, configure DNS Firewall rules to filter network traffic by choosing Create rule. In the Create DNS Firewall rules section, enter a Rule name such as block-malware-domains and optionally add a description. For Rule configuration type, you can choose Customer managed domain lists, AWS managed domain lists provided by AWS or DNS Firewall Advanced protection.

For this walkthrough, choose AWS managed domain lists. In the Domain lists dropdown, choose one or more AWS managed lists such as Threat – Malware to block known malicious domains. You can leave Query type empty to apply the rule to all DNS query types. In this example, choose A to apply this rule only to IPv4 address queries. In the Rule action section, select Block to prevent DNS resolution for domains that match the selected lists. For Response to send for Block action, keep NODATA selected to indicate that the query was successful but no response is available, then choose Create rules.

The next step is to configure access sources to specify which IP addresses or CIDR blocks are allowed to send DNS queries to the resolver. Navigate to the Access sources tab in the DNS view and then choose Create access source.

In the Access source details section, enter a Rule name such as office-networks to identify the access source. In the CIDR block field, enter the IP address range for your offices to allow queries from that network. For Protocol, select Do53 for standard DNS queries over UDP or choose DoH or DoT if you want to require encrypted DNS connections from clients. After configuring these settings, choose Create access source to allow the specified network to send DNS queries to the resolver.

Next, navigate to the Access tokens tab in the DNS view to create token-based authentication for clients and choose Create access token. In the Access token details section, enter a Token name such as remote-clients-token. For Token expiry, select an expiration period from the dropdown based on your security requirements, such as 365 days for long-term client access, or choose a shorter duration like 30 days or 90 days for tighter access control. After configuring these settings, choose Create access token to generate the token, which clients can use to authenticate DoH and DoT connections to the resolver.

After the access token is created, navigate to the Private hosted zones tab in the DNS view to associate Route 53 private hosted zones with the DNS view so that the resolver can resolve queries for your private application domains. Choose Associate private hosted zone and in the Private hosted zones section, select a private hosted zone from the list that you want the resolver to handle. After selecting the zone, choose Associate to enable the resolver to respond to DNS queries for these private domains from your configured access sources.

With the DNS view configured, firewall rules created, access sources and tokens defined, and private hosted zones associated, the Route 53 Global Resolver setup is complete and ready to handle DNS queries from your configured clients.

After creating your Route 53 Global Resolver, you need to configure your DNS clients to send queries to the resolver’s anycast IP addresses. The configuration method depends on the access control you configured in your DNS view:

  • For IP-based access sources (CIDR blocks) – Configure your source clients to point DNS traffic to the Route 53 Global Resolver anycast IP addresses provided in the resolver details. Global Resolver will only allow access from allowlisted IPs that you have specified in your access sources. You can also associate the access sources to different DNS views to provide more granular DNS resolution views for different sets of IPs.
  • For access token–based authentication – Deploy the tokens on your clients to authenticate DoH and DoT connections with Route 53 Global Resolver. You must also configure your clients to point the DNS traffic to the Route 53 Global Resolver anycast IP addresses provided in the resolver details.

For detailed configuration instructions for your specific operating system and protocol, refer to the technical documentation.

Additional things to know
We’re renaming the existing Route 53 Resolver to Route 53 VPC Resolver. This naming change clarifies the architectural distinction between the two services. VPC Resolver operates Regionally within your VPCs to provide DNS resolution for resources in your Amazon VPC environment. VPC Resolver continues to support inbound and outbound resolver endpoints for hybrid DNS architectures within specific AWS Regions.

Route 53 Global Resolver complements Route 53 VPC Resolver by providing internet-reachable, global and private DNS resolution for on-premises and remote clients without requiring VPC deployment or private connections.

Existing VPC Resolver configurations remain unchanged and continue to function as configured. The renaming affects the service name in the AWS Management Console and documentation, but API operation names remain unchanged. If your architecture requires DNS resolution for resources within your VPCs, continue using VPC Resolver.

Join the preview
Route 53 Global Resolver reduces operational overhead by providing unified DNS resolution for public and private domains through a single managed service. The global anycast architecture improves reliability and reduces latency for distributed clients. Integrated security controls and centralized logging help organizations maintain consistent security policies across all locations while meeting compliance requirements.

To learn more about Amazon Route 53 Global Resolver, visit the Amazon Route 53 documentation.

You can start using Route 53 Global Resolver through the AWS Management Console in US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Europe (London), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Tokyo), and Asia Pacific (Sydney) Regions.

— Esra

AWS Weekly Roundup: Amazon S3, Amazon EC2, and more (November 10, 2025)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-s3-amazon-ec2-and-more-november-10-2025/

AWS re:Invent 2025 is only 3 weeks away and I’m already looking forward to the new launches and announcements at the conference. Last year brought 60,000 attendees from across the globe to Las Vegas, Nevada, and the atmosphere was amazing. Registration is still open for AWS re:Invent 2025. We hope you’ll join us in Las Vegas December 1–5 for keynotes, breakout sessions, chalk talks, interactive learning opportunities, and networking with cloud practitioners from around the world.

AWS and OpenAI announced a multi-year strategic partnership that provides OpenAI with immediate access to AWS infrastructure for running advanced AI workloads. The $38 billion agreement spans 7 years and includes access to AWS compute resources comprising hundreds of thousands of NVIDIA GPUs, with the ability to scale to tens of millions of CPUs for agentic workloads. The infrastructure deployment that AWS is building for OpenAI features a sophisticated architectural design optimized for maximum AI processing efficiency and performance. Clustering the NVIDIA GPUs—both GB200s and GB300s—using Amazon EC2 UltraServers on the same network enables low-latency performance across interconnected systems, allowing OpenAI to efficiently run workloads with optimal performance. The clusters are designed to support various workloads, from serving inference for ChatGPT to training next generation models, with the flexibility to adapt to OpenAI’s evolving needs.

AWS committed $1 million through its Generative AI Innovation Fund to digitize the Jane Goodall Institute’s 65 years of primate research archives. The project will transform handwritten field notes, film footage, and observational data on chimpanzees and baboons from analog to digital formats using Amazon Bedrock and Amazon SageMaker. The digital transformation will employ multimodal large language models (LLMs) and embedding models to make the research archives searchable and accessible to scientists worldwide for the first time. AWS is collaborating with Ode to build the user experience, helping the Jane Goodall Institute adopt AI technologies to advance research and conservation efforts. I was deeply saddened when I heard that world-renowned primatologist Jane Goodall had passed away. Learning that this project will preserve her life’s work and make it accessible to researchers around the world brought me comfort. It’s a fitting tribute to her remarkable legacy.

Transforming decades of research through cloud and AI. Dr. Jane Goodall and field staff observe Goblin at Gombe National Park, Tanzania. CREDIT: the Jane Goodall Institute

Last week’s launches
Let’s look at last week’s new announcements:

  • Amazon S3 now supports tags on S3 Tables – Amazon S3 now supports tags on S3 Tables for attribute-based access control (ABAC) and cost allocation. You can use tags for ABAC to automatically manage permissions for users and roles accessing table buckets and tables, eliminating frequent AWS Identity and Access Management (IAM) or S3 Tables resource-based policy updates and simplifying access governance at scale. Additionally, tags can be added to individual tables to track and organize AWS costs using AWS Billing and Cost Management.
  • Amazon EC2 R8a Memory-Optimized Instances now generally available – R8a instances feature 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, and they deliver up to 30% higher performance and up to 19% better price-performance compared to R7a instances, with 45% more memory bandwidth. Built on the AWS Nitro System using sixth-generation Nitro Cards, these instances are designed for high-performance, memory-intensive workloads, including SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics, and electronic design automation (EDA) applications. R8a instances are SAP certified and offer 12 sizes, including two bare metal sizes.
  • EC2 Auto Scaling announces warm pool support for mixed instances policies – EC2 Auto Scaling groups now support warm pools for Auto Scaling groups configured with mixed instances policies. Warm pools create a pool of pre-initialized EC2 instances ready to quickly serve application traffic, improving application elasticity. The feature benefits applications with lengthy initialization processes, such as writing large amounts of data to disk or running complex custom scripts. By combining warm pools with instance type flexibility, Auto Scaling groups can rapidly scale out to maximum size while deploying applications across multiple instance types to enhance availability. The feature works with Auto Scaling groups configured for multiple On-Demand Instance types through manual instance type lists or attribute-based instance type selection.
  • Amazon Bedrock AgentCore Runtime now supports direct code deployment – Amazon Bedrock AgentCore Runtime now offers two deployment methods for AI agents: container-based deployment and direct code upload. You can choose between direct code–zip file upload for rapid prototyping and iteration or container-based options for complex use cases requiring custom configurations. AgentCore Runtime provides a serverless framework and model agnostic runtime for running agents and tools at scale. The direct code–zip upload feature includes drag-and-drop functionality, enabling faster iteration cycles for prototyping while maintaining enterprise security and scaling capabilities for production deployments.
  • AWS Capabilities by Region now available for Regional planning – AWS Capabilities by Region helps discover and compare AWS services, features, APIs, and AWS CloudFormation resources across Regions. This planning tool provides an interactive interface to explore service availability, compare multiple Regions side by side, and view forward-looking roadmap information. You can search for specific services or features, view API operations availability, verify CloudFormation resource type support, and check EC2 instance type availability including specialized instances. The tool displays availability states including Available, Planning, Not Expanding, and directional launch planning by quarter. The AWS Capabilities by Region data is also accessible through the AWS Knowledge MCP server, enabling automation of Region expansion planning and integration into development workflows and continuous integration and continuous delivery (CI/CD) pipelines.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

  • AWS re:Invent 2025 – Join us in Las Vegas December 1–5 as cloud pioneers gather from across the globe for the latest AWS innovations, peer-to-peer learning, expert-led discussions, and invaluable networking opportunities. Don’t forget to explore the event catalog.
  • AWS Builder Loft – A tech hub in San Francisco where builders share ideas, learn, and collaborate. The space offers industry expert sessions, hands-on workshops, and community events covering topics from AI to emerging technologies. Browse the upcoming sessions and join the events that interest you.
  • AWS Skills Center Seattle 4th Anniversary Celebration – A free, public event on November 20 with a keynote, learning panels, recruiter insights, raffles, and virtual participation options.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS led in-person and virtual events, developer-focused events, and events for startups.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Monitor, analyze, and manage capacity usage from a single interface with Amazon EC2 Capacity Manager

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/monitor-analyze-and-manage-capacity-usage-from-a-single-interface-with-amazon-ec2-capacity-manager/

Today, I’m happy to announce Amazon EC2 Capacity Manager, a centralized solution to monitor, analyze, and manage capacity usage across all accounts and AWS Regions from a single interface. This service aggregates capacity information with hourly refresh rates and provides prioritized optimization opportunities, streamlining capacity management workflows that previously required custom automation or manual data collection from multiple AWS services.

Organizations using Amazon Elastic Compute Cloud (Amazon EC2) at scale operate hundreds of instance types across multiple Availability Zones and accounts, using On-Demand Instances, Spot Instances, and Capacity Reservations. This complexity means customers currently access capacity data through various AWS services including the AWS Management Console, Cost and Usage Reports, Amazon CloudWatch, and EC2 describe APIs. This distributed approach can create operational overhead through manual data collection, context switching between tools, and the need for custom automation to aggregate information for capacity optimization analysis.

EC2 Capacity Manager helps you overcome these operational complexities by consolidating all capacity data into a unified dashboard. You can now view cross-account and cross-Region capacity metrics for On-Demand Instances, Spot Instances, and Capacity Reservations across all commercial AWS Regions from a single location, eliminating the need to build custom data collection tools or navigate between multiple AWS services.

This consolidated visibility can help you discover cost savings by highlighting underutilized Capacity Reservations, analyzing usage patterns across instance types, and providing insights into Spot Instance interruption patterns. By having access to comprehensive capacity data in one place, you can make more informed decisions about rightsizing your infrastructure and optimizing your EC2 spending.

Let me show you the capabilities of EC2 Capacity Manager in detail.

Getting started with EC2 Capacity Manager
On the AWS Management Console, I navigate to Amazon EC2 and select Capacity Manager from the navigation pane. I enable EC2 Capacity Manager through the service settings. The service aggregates historical data from the previous 14 days during initial setup.

The main Dashboard displays capacity utilization across all instance types through a comprehensive overview section that presents key metrics at a glance. The capacity overview cards for Reservations, Usage, and Spot show trend indicators and percentage changes to help you identify capacity patterns quickly. You can apply filtering through the date filter controls, which include date range selection, time zone configuration, and interval settings.

You can select different units to analyze data by vCPUs, instance counts, or estimated costs to understand resource consumption patterns. Estimated costs are based on published On-Demand rates and do not include Savings Plans or other discounts. This pricing reference helps you compare the relative impact of underutilized capacity across different instance types—for example, 100 vCPU hours of unused p5 reservations represents a larger cost impact than 100 vCPU hours of unused t3 reservations.

The dashboard includes detailed Usage metrics with both total usage visualization and usage over time charts. The total usage section shows the breakdown between reserved usage, unreserved usage, and Spot usage. The usage over time chart provides visualization that tracks capacity trends over time, helping you identify usage patterns and peak demand periods.

Under Reservation metrics, Reserved capacity trends visualizes used and unused reserved capacity across the selected period, showing the proportion of reserved vCPU hours that remain unutilized compared with those actively consumed, helping you track reservation efficiency patterns and identify periods of consistent low utilization. This visibility can help you reduce costs by identifying underutilized reservations and helping you to make informed decisions about capacity adjustments.

The Unused capacity section lists underutilized capacity reservations by instance type and Availability Zone combinations, displaying specific utilization percentages and instance types across different Availability Zones. This prioritized list helps you identify potential savings with direct visibility into unused capacity costs.

The Usage tab provides detailed historical trends and usage statistics across all AWS Regions for Spot Instances, On-Demand Instances, Capacity Reservations, Reserved Instances, and Savings Plans. Dedicated Hosts usage is not included. The Dimension filter helps you group by and filter capacity data by Account ID, Region, Instance Family, Availability Zone, and Instance Type, creating custom views that reveal usage patterns across your accounts and AWS Organizations. This helps you analyze specific configurations and compare performance across accounts or Regions.

The Aggregations section provides a comprehensive usage table across EC2 and Spot Instances. You can select different units to analyze data by vCPUs, instance counts, or estimated costs to understand resource consumption patterns. The table shows instance family breakdowns with total usage statistics, reserved usage hours, unreserved usage hours, and Spot usage data. Each row includes a View breakdown action for a detailed analysis.

The Capacity usage or estimated cost trends section visualizes usage trends, reserved usage, unreserved usage, and Spot usage. You can filter the displayed data and adjust the unit of measurement to view historical patterns. These filtering and analysis tools help you identify usage trends, compare costs across dimensions, and make informed decisions for capacity planning and optimization.

When you choose View breakdown from the Aggregations table, you access detailed Usage breakdown based on the dimension filters you selected. This breakdown view shows usage patterns for individual instance types within the selected family and Availability Zone combinations, helping you identify specific optimization opportunities.

The Reservations tab displays capacity reservation utilization with automated analysis capabilities that generate prioritized lists of optimization opportunities. Similar to the Usage tab, you can apply dimension filters by Account ID, Region, Instance Family, Availability Zone, and Instance Type along with additional options related to the reservation details. On each of the tabs you can drill down to see data for individual line items. For reservations specifically, you can view specific reservations and access detailed information about On-Demand Capacity Reservations (ODCRs), including utilization history, configuration parameters, and current status. When the ODCR exists in the same account as Capacity Manager, you can modify reservation parameters directly from this interface, eliminating the need to navigate to separate EC2 console sections for reservation management.

The Statistics section provides summary metrics, including total reservations count, overall utilization percentage, reserved capacity totals, used and unused capacity volumes, average scheduled reservations, and counts of accounts, instance families, and Regions with reservations.

This consolidated view helps you understand reservation distribution and utilization patterns across your infrastructure. For example, you might discover that your development accounts consistently show 30% reservation utilization while production accounts exceed 95%, indicating an opportunity to redistribute or modify reservations. Similarly, you could identify that specific instance families in certain Regions have sustained low utilization rates, suggesting candidates for reservation adjustments or workload optimization. These insights help you make data-driven decisions about reservation purchases, modifications, or cancellations to better align your reserved capacity with actual usage patterns.

The Spot tab focuses on Spot Instance usage and displays the amount of time your Spot instances run before being interrupted. This analysis of Spot Instance usage patterns helps you identify optimization opportunities for Spot Instance workloads. You can use Spot placement score recommendations to improve workload flexibility.

For organizations requiring data export capabilities, Capacity Manager includes data exports to Amazon Simple Storage Service (Amazon S3) buckets for capacity analysis. You can view and manage your data exports through the Data exports tab, which helps you create new exports, monitor delivery status, and configure export schedules to analyze capacity data outside the AWS Management Console.

Data exports extend your analytical capabilities by storing capacity data beyond the 90-day retention period available through the console and APIs. This extended retention enables long-term trend analysis and historical capacity planning. You can also integrate exported data with existing analytics workflows, business intelligence tools, or custom reporting systems to incorporate EC2 capacity metrics into broader infrastructure analysis and decision-making processes.

The Settings section provides configuration options for AWS Organizations integration, enabling centralized capacity management across multiple accounts. Organization administrators can enable enterprise-wide capacity visibility or delegate access to specific accounts while maintaining appropriate permissions and access controls.

Now available
EC2 Capacity Manager eliminates the operational overhead of collecting and analyzing capacity data from multiple sources. The service provides automated optimization opportunities, centralized multi-account visibility, and direct access to capacity management tools. You can reduce manual analysis time while improving capacity utilization and cost optimization across your EC2 infrastructure.

Amazon EC2 Capacity Manager is available at no additional cost. To begin using Amazon EC2 Capacity Manager, visit the Amazon EC2 console or access the service APIs. The service is available in all commercial AWS Regions.

To learn more, visit the EC2 Capacity Manager documentation.

— Esra

Announcing Amazon Quick Suite: your agentic teammate for answering questions and taking action

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/reimagine-the-way-you-work-with-ai-agents-in-amazon-quick-suite/

Today, we’re announcing Amazon Quick Suite, a new agentic teammate that quickly answers your questions at work and turns those insights into actions for you. Instead of switching between multiple applications to gather data, find important signals and trends, and complete manual tasks, Quick Suite brings AI-powered research, business intelligence, and automation capabilities into a single workspace. You can now analyze data through natural language queries, find critical information across enterprise and external sources in minutes, and automate processes from simple tasks to complex multi-department workflows.

Here’s a look into Quick Suite.

Business users often need to gather data across multiple applications—pulling customer details, checking performance metrics, reviewing internal product information, and performing competitive intelligence. This fragmented process often requires consultation with specialized teams to analyze advanced datasets, and in some cases, must be repeated regularly, reducing efficiency and leading to incomplete insights for decision-making.

Quick Suite helps you overcome these challenges by combining agentic teammates for research, business intelligence, and automation into a unified digital workspace for your day-to-day work.

Integrated capabilities that power productivity 
Quick Suite includes the following integrated capabilities:

  • Research – Quick Research accelerates complex research by combining enterprise knowledge, premium third-party data, and data from the internet for more comprehensive insights.
  • Business intelligence – Quick Sight provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations, helping everyone make faster decisions and achieve better business outcomes.
  • Automation – Quick Flows and Quick Automate help users and technical teams to automate any business process from simple, routine tasks to complex multi-department workflows, enabling faster execution and reducing manual work across the organization.

Let’s dive into some of these key capabilities.

Quick Index: Your unified knowledge foundation
Quick Index creates a secure, searchable repository that consolidates documents, files, and application data to power AI-driven insights and responses across your organization.

As a foundational component of Quick Suite, Quick Index operates in the background to bring together all your data—from databases and data warehouses to documents and email. This creates a single, intelligent knowledge base that makes AI responses more accurate and reduces time spent searching for information.

Quick Index automatically indexes and prepares any uploaded files or unstructured data you add to your Quick Suite, enabling efficient searching, sorting, and data access. For example, when you search for a specific project update, Quick Index instantly returns results from uploaded documents, meeting notes, project files, and reference materials—all from one unified search instead of checking different repositories and file systems.

To learn more, visit the Quick Index overview page.

Quick Research: From complex business challenges to expert-level insights
Quick Research is a powerful agent that conducts comprehensive research across your enterprise data and external sources to deliver contextualized, actionable insights in minutes or hours — work that previously could take longer.

Quick Research systematically breaks down complex questions into organized research plans. Starting with a simple prompt, it automatically creates detailed research frameworks that outline the approach and data sources needed for comprehensive analysis.

After Quick Research creates the plan, you can easily refine it through natural language conversations. When you are happy with the plan, it works in the background to gather information from multiple sources, using advanced reasoning to validate findings and provide thorough analysis with citations.

Quick Research integrates with your enterprise data connected to Quick Suite, the unified knowledge foundation that connects to your dashboards, documents, databases, and external sources, including Amazon S3, Snowflake, Google Drive, and Microsoft SharePoint. Quick Research grounds key insights to original sources and reveals clear reasoning paths, helping you verify accuracy, understand the logic behind recommendations, and present findings with confidence. You can trace findings back to their original sources and validate conclusions through source citations. This makes it ideal for complex topics requiring in-depth analysis.

To learn more, visit the Quick Research overview page.

Quick Sight: AI-powered business intelligence
Quick Sight provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations.

You can create dashboards and executive summaries using conversational prompts, reducing dashboard development time while making advanced analytics accessible without specialized skills.

Quick Sight helps you ask questions about your data in natural language and receive instant visualizations, executive summaries, and insights. This generative AI integration provides you with answers from your dashboards and datasets without requiring technical expertise.

Using the scenarios capability, you can perform what-if analysis in natural language with step-by-step guidance, exploring complex business scenarios and finding answers faster than before.

Additionally, you can respond to insights with one-click actions by creating tickets, sending alerts, updating records, or triggering automated workflows directly from your dashboards without switching applications.

To learn more, visit Quick Sight overview page.

Quick Flows: Automation for everyone
With Quick Flows, any user can automate repetitive tasks by describing their workflow using natural language without requiring any technical knowledge. Quick Flows fetches information from internal and external sources, takes action in business applications, generates content, and handles process-specific requirements.

Starting with straightforward business requirements, it creates a multi-step flow including input steps for gathering information, reasoning groups for AI-powered processing, and output steps for generating and presenting results.

After the flow is configured, you can share it with a single click to your coworkers and other teams. To execute the flow, users can open it from the library or invoke it from chat, provide the necessary inputs, and then chat with the agent to refine the outputs and further customize the results.

To learn more, visit the Quick Flows overview page.

Quick Automate: Enterprise-scale process automation
Quick Automate helps technical teams build and deploy sophisticated automation for complex, multistep processes that span departments, systems, and third-party integrations. Using AI-powered natural language processing, Quick Automate transforms complex business processes into multi-agent workflows that can be created merely by describing what you want to automate or uploading process documentation.

While Quick Flows handles straightforward workflows, Quick Automate is designed for comprehensive and complex business processes like customer onboarding, procurement automations, or compliance procedures that involve multiple approval steps, system integrations, and cross-departmental coordination. Quick Automate offers advanced orchestration capabilities with extensive monitoring, debugging, versioning, and deployment features.

Quick Automate then generates a comprehensive automation plan with detailed steps and actions. You will find a UI agent that understands natural language instructions to autonomously navigate websites, complete form inputs, extract data, and produces structured outputs for downstream automation steps.

Additionally, you can define a custom agent, complete with instructions, knowledge, and tools, to complete process-specific tasks using the visual building experience – no code required.

Quick Automate includes enterprise-grade features such as user role management and human-in-the-loop capabilities that route specific tasks to users or groups for review and approval before continuing workflows. The service provides comprehensive observability with real-time monitoring, success rate tracking, and audit trails for compliance and governance.

To learn more, visit the Quick Automate overview page.

Additional foundational capabilities
Quick Suite includes other foundational capabilities that deliver seamless data organization and contextual AI interactions across your enterprise.

Spaces – Spaces provide a straightforward way for every business user to add their own context by uploading files or connecting to specific datasets and repositories specific to their work or to a particular function. For example, you might create a space for quarterly planning that includes budget spreadsheets, market research reports, and strategic planning documents. Or you could set up a product launch space that connects to your project management system and customer feedback databases. Spaces can scale from personal use to enterprise-wide deployment while maintaining access permissions and seamless integration with Quick Suite capabilities.

Chat agents – Quick Suite includes insights agents that you can use to interact with your data and workflows through natural language. Quick Suite includes a built-in agent to answer questions across all of your data and custom chat agents that you can configure with specific expertise and business context. Custom chat agents can be tailored for particular departments or use cases—such as a sales agent connected to your product catalog data and pricing information stored in a space or a compliance agent configured with your regulatory requirements and actions to request approvals.

Additional things to know
If you’re an existing Amazon QuickSight customer – Amazon QuickSight customers will be upgraded to Quick Suite, a unified digital workspace that includes all your existing QuickSight business intelligence capabilities (now called “Quick Sight”) plus new agentic AI capabilities. This is an interface and capability change—your data connectivity, user access, content, security controls, user permissions, and privacy settings remain exactly the same. No data is moved, migrated, or changed.

Quick Suite offers per-user subscription-based pricing with consumption-based charges for the Quick Index and other optional features. You can find more detail on the Quick Suite pricing page.

Now available
Amazon Quick Suite gives you a set of agentic teammates that helps you get the answers you need using all your data and move instantly from answers to action so you can focus on high value activities that drive better business and customer outcomes.

Visit the getting started page to start using Amazon Quick Suite today.

Happy building
— Esra and Donnie

AWS Weekly Roundup: AWS Transform, Amazon Neptune, and more (September 8, 2025)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-transform-amazon-neptune-and-more-september-8-2025/

Summer has drawn to a close here in Utrecht, where I live in the Netherlands. In two weeks, I’ll be attending AWS Community Day 2025, hosted at the Kinepolis Jaarbeurs Utrecht on September 24. The single-day event will bring together over 500 cloud practitioners from across the Netherlands, featuring 25 breakout sessions across five technical tracks. The day will begin with virtual keynotes at 9:00 AM, followed by parallel breakout sessions focused on practical implementations of serverless architectures and container optimization strategies, providing valuable insights regardless of experience level.

Last year’s AWS Community Day Netherlands 2024 brought together a diverse group of cloud practitioners, speakers, and AWS enthusiasts who contributed to making the community-led conference a valuable knowledge-sharing platform. If you’re planning to attend, feel free to find me there to discuss AWS services or share your cloud implementation experiences!

Let’s look at last week’s new announcements.

Last week’s launches

AWS Transform assessments now includes detached storage analysis – AWS Transform has expanded its assessment capabilities to analyze on-premises detached storage infrastructure, helping customers determine migration total cost of ownership (TCO). The assessment now evaluates Storage Area Network (SAN), Network Attached Storage (NAS), file servers, object storage, and virtual environments, providing migration recommendations to appropriate AWS services including Amazon S3, Amazon EBS, and Amazon FSx. The tool delivers a comprehensive TCO comparison between current and AWS environments, along with performance and cost optimization recommendations. With storage accounting for up to 45% of total migration opportunities, this enhancement helps customers visualize various AWS migration options. AWS Transform assessment is available in US East (N. Virginia) and Europe (Frankfurt) Regions.

Amazon Bedrock introduces Global Cross-Region inference for Anthropic Claude Sonnet 4 – Anthropic’s Claude Sonnet 4 model in Amazon Bedrock now supports Global cross-Region inference, allowing inference requests to route to any supported commercial AWS Region for processing. This enhancement optimizes available resources and enables higher model throughput by distributing traffic across multiple Regions. Previously, you could select cross-Region inference profiles tied to specific geographies (US, EU, or APAC). The new Global cross-Region inference profile provides additional flexibility for generative AI use cases that don’t require geography-specific processing, helping manage unplanned traffic bursts and increase model throughput. For detailed implementation guidance, visit the Amazon Bedrock documentation.

Amazon Neptune Database adds Public Endpoints support – Amazon Neptune now supports Public Endpoints, enabling direct connections to Neptune databases from outside the VPC without complex networking configurations. This feature helps developers securely access their graph databases from development desktops without requiring VPN connections or bastion hosts, while maintaining security through IAM authentication, VPC security groups, and encryption in transit. Public Endpoints can be enabled for Neptune clusters running engine version 1.4.6 or above through the AWS Management Console, AWS CLI, or AWS SDK. The feature is available at no additional cost beyond standard Neptune pricing in all AWS Regions where Neptune Database is offered. Implementation details are available in the Amazon Neptune documentation.

ECS Exec now available in AWS Management Console – Amazon ECS now supports ECS Exec directly in the AWS Management Console, enabling secure, interactive shell access to running containers without requiring inbound ports or SSH key management. Previously available only through API, CLI, or SDKs, this feature streamlines troubleshooting by allowing container access directly from the console interface. You can enable ECS Exec when creating or updating services and standalone tasks, then connect to containers by selecting “Connect” on the task details page, which opens an interactive session through CloudShell. The console also displays the underlying AWS CLI command for use in local terminals. This feature is available in all AWS commercial Regions and documented in the ECS developer guide.

Organizational Notification Configurations for AWS User Notifications now generally available – AWS User Notifications now supports Organizational Notification Configurations, helping AWS Organizations users centrally configure and view notifications across their organization. Management accounts or delegated administrators can configure notifications for specific organizational units or all accounts in an organization. The service supports configuring notifications for any supported Amazon EventBridge event, such as console sign-ins without MFA, with notifications appearing in the admin’s Console Notifications Center and AWS Console Mobile Application. User Notifications supports up to five delegated administrators and is available in all AWS Regions where AWS User Notifications is offered. For implementation details, visit the AWS User Notifications user guide.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events.

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Zurich (September 11), Los Angeles (September 17), and Bogotá (October 9).

AWS re:Invent 2025 – Join us in Las Vegas between December 1–5 as cloud pioneers gather from across the globe for the latest AWS innovations, peer-to-peer learning, expert-led discussions, and invaluable networking opportunities. Don’t forget to explore the event catalog.

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: Baltic (September 10), Aotearoa (September 18), South Africa (September 20), Bolivia (September 20), Portugal (September 27).

Browse all upcoming AWS led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Amazon GuardDuty expands Extended Threat Detection coverage to Amazon EKS clusters

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/amazon-guardduty-expands-extended-threat-detection-coverage-to-amazon-eks-clusters/

Today, I’m happy to announce Amazon GuardDuty Extended Threat Detection with expanded coverage for Amazon Elastic Kubernetes Service (Amazon EKS), building upon the capabilities we introduced in our AWS re:Invent 2024 announcement of Amazon GuardDuty Extended Threat Detection: AI/ML attack sequence identification for enhanced cloud security.

Security teams managing Kubernetes workloads often struggle to detect sophisticated multistage attacks that target containerized applications. These attacks can involve container exploitation, privilege escalation, and unauthorized movement within Amazon EKS clusters. Traditional monitoring approaches might detect individual suspicious events, but often miss the broader attack pattern that spans across these different data sources and time periods.

GuardDuty Extended Threat Detection introduces a new critical severity finding type, which automatically correlates security signals across Amazon EKS audit logs, runtime behaviors of processes associated with EKS clusters, malware execution in EKS clusters, and AWS API activity to identify sophisticated attack patterns that might otherwise go unnoticed. For example, GuardDuty can now detect attack sequences in which a threat actor exploits a container application, obtains privileged service account tokens, and then uses these elevated privileges to access sensitive Kubernetes secrets or AWS resources.

This new capability uses GuardDuty correlation algorithms to observe and identify sequences of actions that indicate potential compromise. It evaluates findings across protection plans and other signal sources to identify common and emerging attack patterns. For each attack sequence detected, GuardDuty provides comprehensive details, including potentially impacted resources, timeline of events, actors involved, and indicators used to detect the sequence. The findings also map observed activities to MITRE ATT&CK® tactics and techniques and remediation recommendations based on AWS best practices, helping security teams understand the nature of the threat.

To enable Extended Threat Detection for EKS, you need at least one of these features enabled: EKS Protection or Runtime Monitoring. For maximum detection coverage, we recommend enabling both to enhance detection capabilities. EKS Protection monitors control plane activities through audit logs, and Runtime Monitoring observes behaviors within containers. Together, they create a complete view of your EKS clusters, enabling GuardDuty to detect complex attack patterns.

How it works
To use the new Amazon GuardDuty Extended Threat Detection for EKS clusters, go to the GuardDuty console to enable EKS Protection in your account. From the Region selector in the upper-right corner, select the Region where you want to enable EKS Protection. In the navigation pane, choose EKS Protection. On the EKS Protection page, review the current status and choose Enable. Select Confirm to save your selection.

After it’s enabled, GuardDuty immediately starts monitoring EKS audit logs from your EKS clusters without requiring any additional configuration. GuardDuty consumes these audit logs directly from the EKS control plane through an independent stream, which doesn’t affect any existing logging configurations. For multi-account environments, only the delegated GuardDuty administrator account can enable or disable EKS Protection for member accounts and configure auto-enable settings for new accounts joining the organization.

To enable Runtime Monitoring, choose Runtime Monitoring in the navigation pane. Under the Configuration tab, choose Enable to enable Runtime Monitoring for your account.

Now, you can view from the Summary dashboard the attack sequences and critical findings specifically related to Kubernetes cluster compromise. You can observe that GuardDuty identifies complex attack patterns in Kubernetes environments, such as credential compromise events and suspicious activities within EKS clusters. The visual representation of findings by severity, resource impact, and attack types gives you a holistic view of your Amazon EKS security posture. This means you can prioritize the most critical threats to your containerized workloads.

The Finding details page provides visibility into complex attack sequences targeting EKS clusters, helping you understand the full scope of potential compromises. GuardDuty correlates signals into a timeline, mapping observed behaviors to MITRE ATT&CK® tactics and techniques such as account manipulation, resource hijacking, and privilege escalation. This granular level of insight reveals exactly how attackers progress through your Amazon EKS environment. It identifies affected resources like EKS workloads and service accounts. The detailed breakdown of indicators, actors, and endpoints provides you with actionable context to understand attack patterns, determine impact, and prioritize remediation efforts. By consolidating these security insights into a cohesive view, you can quickly assess the severity of Amazon EKS security incidents, reduce investigation time, and implement targeted countermeasures to protect your containerized applications.

The Resources section of the Finding details page shows context about the specific assets affected during an attack sequence. This unified resource list provides you with visibility into the exact scope of the compromise—from the initial access to the targeted Kubernetes components. Because GuardDuty includes detailed attributes such as resource types, identifiers, creation dates, and namespace information, you can rapidly assess which components of your containerized infrastructure require immediate attention. This focused approach eliminates guesswork during incident response, so you can prioritize remediation efforts on the most critical affected resources and minimize the potential blast radius of Amazon EKS targeted attacks.

Now available
Amazon GuardDuty Extended Threat Detection with expanded coverage for Amazon EKS clusters provides comprehensive security monitoring across your Kubernetes environment. You can use this capability to detect sophisticated multistage attacks by correlating events across different data sources, identifying attack sequences that traditional monitoring might miss.

To start using this expanded coverage, enable EKS Protection in your GuardDuty settings and consider adding Runtime Monitoring for enhanced detection capabilities.

For more information about this new capability, refer to the Amazon GuardDuty Documentation.

— Esra

New AWS Shield feature discovers network security issues before they can be exploited (Preview)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/new-aws-shield-feature-discovers-network-security-issues-before-they-can-be-exploited-preview/

Today, I’m happy to announce AWS Shield network security director (preview), a capability that simplifies identification of configuration issues related to threats such as SQL injections and distributed denial of service (DDoS) events, and proposes remediations. This feature identifies and analyzes network resources, connections, and configurations. It compares them against AWS best practices to create a network topology that highlights resources requiring protection.

Organizations today face significant challenges in maintaining a robust network security posture. Security teams often struggle to efficiently discover all resources in their environments, understand how these resources are interconnected, and identify which security services are currently configured. Additionally, they find determining how well resources are configured relative to AWS best practices requires considerable expertise and effort. Many teams find it difficult to identify which network security services and rule sets would best protect their applications from common and emerging threats.

AWS Shield network security director addresses these challenges through three key capabilities. First, it performs comprehensive analysis to discover resources across your AWS accounts, identify connectivity between resources, and determine which network security services and configurations are currently in place. Second, it prioritizes resources by severity level based on AWS network security best practices and threat intelligence. Finally, it provides specific remediation recommendations such as step-by-step instructions for implementing the right AWS security services, including AWS WAF, Amazon Virtual Private Cloud (Amazon VPC) security groups, and Amazon VPC network access control lists (ACLs) to protect your resources.

The service supports critical network security use cases, including protecting applications against internet-born threats and controlling human access to resources based on port, protocol, or IP address range. It provides network analysis to discover assets and delivers analysis that eliminates time-consuming manual processes for identifying resources that need protection. The service offers resource prioritization by assigning security findings a severity level based on network context and adherence to AWS best practices, helping you focus on what matters most. Additionally, it supplies actionable recommendations with specific guidance on which services and configurations will address each security gap. You can also get answers, in natural language, from AWS Shield network security director from within Amazon Q Developer in the AWS Management Console and chat applications.

Getting started with AWS Shield network security director
To use AWS Shield network security director, I need to initiate a network analysis of my AWS resources. I go to the AWS WAF & Shield console and choose Getting started under AWS Shield network security director in the navigation pane. I choose Get started, which takes me to the configuration page. On this page, I can choose how to perform my first network analysis: I can assess findings from across all supported Regions or from my current Region only. I select Start network analysis.

After the analysis is completed, the dashboard page shows a breakdown of resource types by severity level and the most common categories of network security findings associated with their resources. Resources are categorized by type and severity level (critical, high, medium, low, informational), making it easy to identify which areas need immediate attention.

Next, I explore the Resources section to understand the distribution of my assets and filter by severity level in my environment. I can use Resource overview to review a specific severity level, which will redirect me to the Resources under Network security director with the associated severity level filter. I choose the resources that have Medium severity level.

I choose a specific resource to view its network topology map showing how it connects to other resources and associated findings. This visualization helps me understand the potential impact of security configurations and identify exposed paths. I review detailed findings such as “Allows unrestricted inbound access (0.0.0.0/0) on all ports” with severity ratings.

Next, I go to Findings under Network security director, which shows common configuration issues. For each finding, I receive detailed information and recommended remediation steps. The service rates the severity of findings (high, medium, low) to help me prioritize my response. Critical-severity findings such as “CloudFront origin is also internet accessible without CloudFront protections” or high-severity findings such as “Allows unrestricted inbound access (0.0.0.0/0) on all ports” are presented first, followed by medium- and low-severity issues.

You can analyze your network security configurations, in natural language, with AWS Shield network security director within Amazon Q Developer in the AWS Management Console and chat applications. For example, you can say “Do I have any network security issues on my CloudFront distributions?” or “Are any of my resources vulnerable to bots and scrapers?” This integration helps security teams quickly understand their security posture and receive guidance on implementing best practices without having to navigate through extensive documentation.

To explore this capability, I ask “What are my most critical network security issues?” in the Explore with Amazon Q section. Amazon Q analyzes my network security configuration and generates a response based on the security assessment of my AWS environment.

With this comprehensive view of your network security, you can now make data-driven decisions to strengthen your defenses against emerging threats.

Join the preview
AWS Shield network security director is available in the US East (N. Virginia) and Europe (Stockholm) Regions. The Amazon Q Developer capability to analyze network security configurations is available in preview in US East (N. Virginia). To begin strengthening your network security, visit the AWS Shield network security director console and initiate your first network security analysis.

For more information, visit the AWS Shield product page.

— Esra