Kategorie: Amazon AWS

AWS KMS now tracks last usage of all KMS keys

AWS Key Management Service (KMS) now provides visibility into the last cryptographic operation performed with your KMS keys, eliminating the need to manually query and analyze logs. This feature helps security administrators and compliance teams quickly determine when their KMS keys were last used for cryptographic operations. You can view the timestamp, the type of operation performed, and the associated AWS CloudTrail event ID from the AWS KMS management console, or via API.
You can use this feature to help identify unused keys for cleanup, verify that keys are actively used, and track down how your keys are used in AWS CloudTrail. In addition, you can use the new condition key (kms:TrailingDaysWithoutKeyUsage) that enables policy-based protection against accidental deletion of recently used keys.
The feature is available in all AWS Regions where AWS KMS is available, including all commercial AWS Regions, AWS GovCloud (US) Regions, and AWS China Regions. For more information, see Determine past usage of a KMS key in the AWS KMS Developer Guide.
Quelle: aws.amazon.com

Amazon Redshift Serverless is now available in the AWS Asia Pacific (Melbourne) and Canada West (Calgary) regions

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (Melbourne) and Canada West (Calgary) regions. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, Apache Iceberg in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.
Quelle: aws.amazon.com

Amazon Redshift Serverless AI-driven scaling is now the default for new workgroups

Amazon Redshift Serverless now makes AI-driven scaling and optimization the default for all new workgroups. AI-driven scaling uses machine learning to predict compute needs and automatically adjust resources before queries queue, delivering better price-performance without manual tuning. This release also expands support to workloads with a Base RPU range of 8–512 RPU, up from the previous 32–512 RPU, reducing the entry cost for AI-driven scaling. With AI-driven scaling and optimization, Amazon Redshift monitors your workload patterns and automatically adjusts compute resources based on query complexity, data volume, and expected data scan size. You can use the price-performance slider to choose whether to prioritize cost, performance, or a balance of both. Amazon Redshift also applies additional optimizations, including automatic materialized views and automatic table design optimization, to meet your selected target. To configure price-performance targets, use the AWS Management Console or Amazon Redshift API operations. You can also modify the target after you create the workgroup. Amazon Redshift Serverless AI-driven scaling and optimization is available in all AWS Regions where Amazon Redshift Serverless is available. For more information, see Amazon Redshift Serverless product page and AI-driven scaling and optimization documentation.
Quelle: aws.amazon.com

Amazon WorkSpaces Personal Supports Rocky 9, Red Hat Enterprise Linux 9, and Ubuntu 24.04

AWS announces availability of new Linux bundles for Amazon WorkSpaces Personal, including Rocky Linux 9, Red Hat Enterprise Linux 9, and Ubuntu 24.04. With these bundles, customers can launch WorkSpaces powered by the latest enterprise-grade Linux operating systems and take advantage of modern versions of Linux packages only available in these updated releases. While Rocky Linux 8, Red Hat Enterprise Linux 8, and Ubuntu 22.04 powered WorkSpaces bundles remain available, the new OS options bring access to the latest software ecosystems, improved security postures, and extended long-term support lifecycles offered by each respective distribution. These new bundles also provide a migration path for Amazon Linux 2 customers ahead of its end of life in June 2026. You can get started using managed Rocky Linux 9, Red Hat Enterprise Linux 9, or Ubuntu 24.04 WorkSpaces bundles by selecting one when creating a new Linux WorkSpace. These new bundles are available in all AWS Regions where Amazon WorkSpaces is available. For pricing information, visit the Amazon WorkSpaces pricing page.
Quelle: aws.amazon.com

Amazon SageMaker HyperPod now supports G7e and r5d.16xlarge instances

Amazon SageMaker HyperPod now supports G7e and r5d.16xlarge instances. SageMaker HyperPod is a purpose-built infrastructure for developing, training, and deploying foundation models at scale. It provides a resilient and performant environment with built-in fault tolerance, automated cluster recovery, and optimized distributed training libraries, reducing the undifferentiated heavy lifting of managing large-scale AI/ML infrastructure. 
G7e instances are powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and deliver up to 2.3x better inference performance than G6e instances, allowing you to process more requests per second while reducing latency. With up to 768 GB of total GPU memory, G7e instances let you deploy larger language models or run multiple models on a single endpoint. You can use these instances for deploying LLMs, agentic AI, multimodal generative AI, and physical AI models. G7e instances are also well suited for cost-efficient single-node fine-tuning or training of NLP, computer vision, and smaller generative AI models, with up to 1.27x the TFLOPs and up to 4x the GPU-to-GPU bandwidth compared to G6e. In addition, HyperPod now supports r5d.16xlarge as well. The r5d.16xlarge instance provides 64 vCPUs, 512 GB of memory, and 5 x 600 GB NVMe SSD instance storage, powered by Intel Xeon Platinum 8000 series processors with a sustained all-core turbo frequency of up to 3.1 GHz. This instance is well suited for distributed training data preprocessing especially with frameworks such as Ray, large-scale feature engineering, and running memory-heavy orchestration services alongside GPU compute. G7e instances are available in US East (N. Virginia), US East (Ohio), Asia Pacific (Tokyo), and US West (Oregon) and r5d.16xlarge is available in all regions Amazon SageMaker HyperPod is available in. 
Quelle: aws.amazon.com

Amazon Connect now provides eight new metrics to measure and improve AI agent performance

Amazon Connect now provides eight new metrics to measure and improve AI agent performance, including goal success rate, faithfulness score, and tool selection accuracy. These metrics offer visibility into the quality of AI-driven customer interactions, enabling measurement and continuous improvement of AI agent outcomes. With this launch, you can monitor whether AI agents successfully resolved customer requests, assess faithfulness and detect contextual hallucinations. You can also evaluate tool selection and utilization accuracy, and capture customer feedback through thumbs up/down ratings when enabled.  You can access these new metrics through Amazon Connect’s AI Agent Performance dashboard, or through the GetMetricDataV2 API and zero-ETL data lake for custom reporting or integration with your existing analytics workflows.
This feature is available in all AWS Regions where Amazon Connect AI Agents is supported. For more information, see the Amazon Connect Administrator Guide. To learn more about Amazon Connect, an AI-native solution that turns every customer interaction into a moment worth remembering, visit the Amazon Connect website
Quelle: aws.amazon.com

Amazon Bedrock AgentCore Gateway and Identity support VPC egress

Amazon Bedrock AgentCore Gateway and Identity now provide secure and controlled egress traffic management for your applications, enabling seamless communication with resources in your Virtual Private Cloud (VPC). VPC egress for AgentCore Gateway targets and Identity credential providers are offered in both managed and self-managed configurations.
With VPC egress support, customers can now invoke private resources (e.g., EKS-hosted MCP servers) directly from their AgentCore Gateway. Managed VPC egress covers most customer use cases. For more complex networking setups, customers can configure their own VPC Lattice resources. AgentCore Identity VPC egress supports connectivity to Identity Providers (IdPs) running inside a customer’s VPC. This enables two key capabilities: validating inbound access tokens issued by your private IdP and fetching tokens from your IdP for outbound request authentication. Finally, this launch supports private DNS resolution for managed VPC egress resources across Gateway and Identity.
AgentCore Gateway and Identity are available in fourteen AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and Europe (Stockholm).
Learn more about VPC egress capabilities through AgentCore Gateway documentation, and AgentCore Identity documentation. Get started with the AgentCore CLI.
Quelle: aws.amazon.com

Amazon Quick now integrates with Visier’s Vee agent for workforce intelligence

Amazon Quick now integrates with Vee, the AI assistant from Visier’s people analytics platform, through the model context protocol (MCP). HR business partners, finance managers, and operations leaders can now get governed access to live workforce intelligence from Visier directly within their Amazon Quick workspace without switching tools.
After setting up the connection in Quick using Visier’s remote MCP server, you can ask questions in natural language about headcount, attrition, tenure, and open requisitions and receive answers grounded in Visier’s governed workforce data model. Vee can also be invoked from automated Quick Flows to run recurring workforce reviews or draft documents. Quick intelligently routes relevant prompts to Vee and returns contextualized answers alongside enterprise knowledge – such as budgets, policies, and plans stored in Quick Spaces – so every answer reflects the full organizational picture.
The Visier integration with Amazon Quick is available in all AWS Regions where Amazon Quick is available.
To get started with Amazon Quick, visit the website. To learn more about the Visier integration, read the Visier integration guide, see the blog, and explore more integrations on the integrations page.
Quelle: aws.amazon.com

AWS Lambda Provisioned Mode for Kafka event source mappings (ESMs) now available in AWS Asia Pacific (Taipei) and AWS GovCloud (US) Regions

AWS Lambda now supports Provisioned Mode for event source mappings (ESMs) that subscribe to Apache Kafka event sources in the Asia Pacific (Taipei), AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions. Provisioned Mode allows you to optimize the throughput of your Kafka ESM by provisioning event polling resources that remain ready to handle sudden spikes in traffic, helping you build highly responsive and scalable event-driven Kafka applications with stringent performance requirements.
Customers building streaming data applications often use Kafka as an event source for Lambda functions, relying on Lambda’s fully managed ESM to automatically scale polling resources in response to events. However, for event-driven Kafka applications that need to handle unpredictable bursts of traffic, lack of control over the throughput of ESM can lead to delays in your users’ experience. Provisioned Mode for Kafka ESM enables customers to fine-tune the throughput of their Amazon Managed Streaming for Apache Kafka (MSK) ESM or self-managed Kafka ESM by provisioning and auto-scaling between a minimum and maximum number of polling resources called event pollers. With this launch, this feature is now available in three additional regions.  
You can activate Provisioned Mode for MSK ESM or self-managed Kafka ESM by configuring a minimum and maximum number of event pollers in the ESM API, AWS Console, AWS CLI, AWS SDK, and AWS CloudFormation. You pay for the usage of event pollers, along a billing unit called Event Poller Unit (EPU). To learn more, read the Lambda ESM documentation and AWS Lambda pricing. 
Quelle: aws.amazon.com

AWS Client VPN now supports native AWS Transit Gateway integration

AWS Client VPN now supports native integration with AWS Transit Gateway, simplifying centralized remote access for your end users across multiple VPCs and on-premises, and providing end-to-end source IP visibility. AWS Transit Gateway interconnects your Amazon Virtual Private Clouds (VPCs) and on-premises networks, while AWS Client VPN enables secure remote access to AWS and on-premises resources connected through your AWS network. Previously, connecting Client VPN to multiple VPCs required provisioning and managing an intermediate VPC, adding operational complexity as you needed to manage additional resources. Moreover, client source IPs were translated through Source Network Address Translation (SNAT), making it difficult to identify which remote user generated specific traffic and complicating security audits. Native Transit Gateway attachment eliminates the need for an intermediate VPC, letting you provide centralized remote access to multiple VPCs and on-premises networks directly from your Client VPN endpoint. Additionally, the end-user source IP is now preserved end-to-end, so you can create authorization rules based on actual client IPs and trace traffic back to specific users, simplifying security, compliance, and troubleshooting workflows. Furthermore, Transit Gateway flow logs capture connection-level details tied to preserved source IPs for improved troubleshooting and compliance audits. This integration is available in all AWS Regions where AWS Client VPN is available. There are no additional charges for this native integration beyond standard pricing of AWS Client VPN and AWS Transit Gateway.
To learn more about Client VPN:

Visit the AWS Client VPN product page
Read the AWS Client VPN documentation

Quelle: aws.amazon.com