10/9/2026, 12:00:00 AM ~ 10/12/2026, 12:00:00 AM (UTC)

Recent Announcements

AWS Security Hub now exports findings to S3 in CSV or JSON format

Today, AWS Security Hub announces support for exporting findings to Amazon S3 in CSV or JSON (OCSF) format. Security teams that need findings outside the console for use cases such as compliance reporting and audit evidence can now export findings from every findings page in the Security Hub console, including Threats, Exposure, Vulnerabilities, Posture Management, Sensitive Data, and All Findings, without building and maintaining their own extraction pipelines.\nWith this launch, you can start an export on demand from the findings page you are viewing and have Security Hub deliver the results to an S3 bucket in your account. You can choose CSV when you want to review or share findings in a spreadsheet or export in JSON if you need these findings in Open Cybersecurity Schema Framework (OCSF) format. Findings export is available in all AWS Regions where AWS Security Hub is available. To learn more, visit the AWS Security Hub User Guide at https://docs.aws.amazon.com/securityhub/latest/userguide/securityhub-v2-findings-export.html.

Amazon EC2 R8gd instances are now available in additional regions

Amazon Elastic Compute Cloud (Amazon EC2) R8gd instances are available in AWS European Sovereign Cloud (Germany) region. These instances feature up to 11.4 TB of local NVMe-based SSD block-level storage and are powered by AWS Graviton4 processors, delivering up to 30% better performance over Graviton3-based instances. These instances are built on the AWS Nitro System and are a great fit for applications that need access to high-speed, low latency local storage. \n To learn more, see R8gd instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program. To get started, see the AWS Management Console.

Amazon EC2 R8g instances now available in additional regions

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in the AWS European Sovereign Cloud (Germany) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.\nAWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). 

Amazon EC2 R8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.

Amazon Bedrock now supports reasoning summaries for OpenAI models

Amazon Bedrock now supports the reasoning.summary parameter for OpenAI models through the Responses API. This capability lets you request a human-readable summary of a model’s reasoning alongside its response, helping you understand its approach to complex tasks such as coding, analysis, and multi-step problem solving.\nReasoning summaries give developers additional context for evaluating responses, debugging applications, and showing users how a model approached a request. The summary is returned in the summary array of the reasoning output item, alongside the model’s answer. This capability is available for all OpenAI models on Amazon Bedrock in all AWS Regions where OpenAI GPT models are available, including in-Region inference, geographic (GEO) cross-Region inference, and global cross-Region inference. To learn more, see the OpenAI documentation on reasoning summaries. To get started with OpenAI GPT models on Amazon Bedrock, see documentation here.

Anthropic Claude Sonnet 5.5 and Claude Opus 5.5 are now available on Kiro in AWS GovCloud (US)

Two new Anthropic models are now available in the Kiro IDE and CLI for the AWS GovCloud (US) Regions.\nClaude Opus 5.5 is Anthropic’s most capable Opus model yet and a step up from Opus 5 on long-running agentic coding work. Opus 5.5 thinks adaptively on every request, deciding how much effort each task needs, and completes tasks with roughly 40% fewer tool calls and about half the tokens of Opus 5 in Kiro’s internal benchmarking. Available with a 1M context window and 2.0x credit multiplier, down from Opus 5’s 2.2x. Claude Sonnet 5.5 is Anthropic’s fastest Sonnet model to date and a natural upgrade for teams already building on Sonnet 5. Output is more than 30% faster, real-world coding and knowledge work approaches Opus 5.5 performance, and clearer writing makes it a stronger collaborator, giving developers a cost-performance dial between maximum capability and higher throughput. Available with a 1M context window and 1.3x credit multiplier. Ensure your IDE or CLI is updated to the latest version, then restart it to access the new models from the model selector. For more details, visit the GovCloud documentation, the monitoring and tracking guide, or contact your AWS account team. To learn more about Kiro, visit the Kiro product page.

Amazon Connect Customer now provides automated checks to improve performance evaluation forms

Amazon Connect Customer now suggests changes to performance evaluation forms that improve the accuracy of evaluations automatically filled by AI. While setting up an evaluation form, managers can run an automated check that compares the form against best practices with a single click. Managers then receive a list of evaluation questions that are missing context for AI to score reliably, along with suggestions to make them more specific. For example, the check might flag “Did the agent provide a sales disclosure?” and prompt the manager to define exactly what the agent must disclose before a sale. This helps managers automatically score human and AI agents fairly and accurately, while spending less time refining their evaluation forms.\nThis feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo). To learn more, please visit our documentation and our webpage. For information about Amazon Connect Customer pricing, please visit our pricing page.

Amazon S3 Vectors metadata pre-filtering is now available in AWS GovCloud (US) Regions

Amazon S3 Vectors metadata pre-filtering is now available in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Pre-filtering evaluates metadata filters before running similarity search, returning up to 5x more of the matching vectors when your filter is selective. S3 Vectors also adds a prefix match operator ($startsWith) for filtering on values like paths and URLs. Together, these improvements give your retrieval-augmented generation (RAG), agentic, and semantic-search applications more complete results when you filter, so your applications return more relevant answers.\nS3 Vectors provides native support to store and query vectors in Amazon S3, delivering purpose-built, cost-optimized vector storage and query at billion-vector scale. With this launch, indexes in new vector buckets in the AWS GovCloud (US) Regions use metadata pre-filtering by default, with no change to how you write vectors with PutVectors or run filtered queries with QueryVectors. To use pre-filtering on an existing index, update it in place with the UpdateIndexMode API. You can also compare pre-filtering against your current filtering on the same index, using a per-query parameter on QueryVectors, before you update. Metadata pre-filtering is available at no additional cost in all commercial AWS Regions where Amazon S3 Vectors is available, and in the AWS China Regions. We are in the process of deploying this change and plan to complete the deployment in the coming days. To get started, use the AWS CLI, AWS SDKs, or the Amazon S3 console. To learn more, visit the Amazon S3 Vectors documentation and the AWS News blog.

AWS Lambda supports OAuth authentication for self-managed Apache Kafka event sources

AWS Lambda now supports OAuth authentication for self-managed Apache Kafka event source mappings (ESM), including Kafka clusters that customers run themselves and managed offerings such as Confluent Cloud, Aiven, and Redpanda. With this launch, customers can authenticate their Lambda Kafka consumers through their enterprise identity provider, helping them meet the security and compliance requirements for their Kafka applications.\nCustomers building event-driven Kafka workloads for use cases such as payment processing, fraud detection, and real-time data pipelines use Kafka ESM to build serverless Kafka consumers. Kafka ESM provides automatic scaling, error handling, batching, and event filtering. Previously, Kafka ESM supported only SASL/PLAIN, SASL/SCRAM, and mutual TLS (mTLS) as authentication methods, so customers in regulated industries that require OAuth could not use Kafka ESM with their clusters. With OAuth support, customers can use their enterprise identity provider, such as Amazon Cognito or Okta, to authenticate their Kafka ESM and apply the same identity governance and access policies across their Kafka clusters and Lambda consumers. This capability is available in all AWS commercial Regions where self-managed Kafka ESM is available. To use OAuth authentication, create a new Kafka ESM with your authentication configuration through the AWS Management Console, Lambda API, AWS CLI, AWS CloudFormation, or AWS SAM. To learn more, see the AWS Lambda developer guide and AWS Lambda pricing.

Amazon Quick now supports brand templates for on-brand presentations and documents

Starting today, you can upload brand templates in Amazon Quick, allowing you to create presentations and documents that match your approved visual identity. Brand managers set up a template once and everyone gets on-brand output, without hunting for the right file in each chat.\nBrand managers upload PowerPoint or Word templates, or pull them from a connected file store such as SharePoint. Quick extracts the colors, fonts, logos, layouts and brand rules from each file. A brand manager reviews and edits what Quick found before publishing it to the whole organization. Quick also follows a template’s rules as it creates new assets. If a request breaks a rule, Quick explains why and suggests an on-brand alternative. For each task, Quick picks the best-fit template based on guidance the brand manager writes. Users can still choose a different template. Administrators decide who can create and publish brand templates using custom permissions, ensuring that only the proper members of brand, marketing, or other approved teams can set visual standards.  Brand assets are generally available today in the Amazon Quick desktop app. To learn more, see the Amazon Quick documentation, or create an account for free and start using Quick in minutes.

Amazon SageMaker Unified Studio now supports custom Tooling blueprints

Amazon SageMaker Unified Studio now supports custom Tooling blueprints, giving domain administrators the ability to define the foundation of every project by using their own AWS CloudFormation templates. Administrators can tailor project environments to meet organization-specific requirements, such as IAM role names that comply with company naming standards or custom permissions boundaries instead of AWS managed policies.\nAmazon SageMaker Unified Studio administrators start by authoring a CloudFormation template that describes the project environment they need, then register it as a custom Tooling blueprint. The service validates the template at registration and again after each project deployment, confirming that the required resources exist before any team member uses the project. Templates can include any CloudFormation-supported resource, such as AWS Lake Formation grants, Amazon Athena workgroups, or VPC security groups, and work across multiple AWS accounts and Regions without per-project edits. At deployment time, the service automatically populates reserved template parameters, such as the project ID and domain ID, so administrators define the template once and apply it everywhere. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Custom blueprints as Tooling documentation.

TwelveLabs Pegasus 1.5 model now available on Amazon Bedrock

Amazon Bedrock now supports TwelveLabs Pegasus 1.5, a video-to-text model that generates structured, time-coded metadata from video. Until now, finding a moment in a large video library meant knowing where to look before you could ask about it. Pegasus 1.5 reads an entire video and labels what happens and when, turning footage into data your applications can query.\nYou define what matters to your business, such as editorial segments, speaker changes, sports plays, or brand appearances, and Pegasus 1.5 finds those moments across the video and returns them with timestamps. It analyzes what it sees, hears, and reads on screen in a single pass, with no pre-indexing or ingestion pipeline to build first. Pegasus 1.5 is accessible through the US and Global cross-Region inference profiles. To get started, visit the Amazon Bedrock product page, access the model in the Amazon Bedrock console, or see the Amazon Bedrock documentation for supported Regions, APIs, features, and pricing.

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