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Introducing pre-fetching and IAM role assumption for AWS Secrets Manager Agent

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AWS Secrets Manager Agent now supports two new capabilities: pre-fetching secrets at startup and assuming an IAM role to retrieve secrets. With pre-fetching, you can specify a list of secrets or a tag value to retrieve and cache at agent startup, reducing application startup latency and optimizing cost through the BatchGetSecretValue API. With IAM role assumption, you can pass a role ARN in your pre-fetch configuration or HTTP requests for secret retrieval. The agent assumes the specified role to retrieve secrets, enabling cross-account secret retrieval by assuming a role in a different account.

Together, these enhancements strengthen your security posture through role-based secret access and reduce operational overhead by eliminating custom pre-loading logic. For example, a microservice that requires 20 secrets at startup can now pre-fetch them in a single batch operation, cutting startup latency by avoiding sequential GetSecretValue calls. IAM role assumption also simplifies multi-account architectures by enabling you to specify a different IAM role per secret.

AWS Secrets Manager Agent with pre-fetching and IAM role assumption is supported in all AWS Regions where AWS Secrets Manager is offered. To learn more, visit the AWS Secrets Manager Agent documentation .    

Amazon Managed Grafana now supports dual-stack connectivity (IPv6 and IPv4)

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Amazon Managed Grafana now supports dual-stack connectivity, enabling workspaces to communicate over both Internet Protocol version 4 (IPv4) and Internet Protocol version 6 (IPv6). Dual-stack mode is available for workspaces running Grafana version 10.4 or later.

With dual-stack support, customers can simplify their network stack by eliminating the need to manage overlapping address spaces in their VPCs. Customers migrating to IPv6 can connect to their Grafana workspaces over IPv6 while maintaining IPv4 compatibility, and those not yet on IPv6 can continue using IPv4-only connections. This is especially beneficial as the continued growth of the internet exhausts available IPv4 addresses. 

Support for dual-stack connectivity on Amazon Managed Grafana is available in all regions  where the service is generally available. To get started, update your workspace configuration via the Amazon Managed Grafana console, API, or CLI. For more information, see the Amazon Managed Grafana User Guide . To learn more about best practices for configuring IPv6 in your environment, visit the whitepaper on IPv6 in AWS .

Amazon Inspector is now available in the AWS Asia Pacific (Taipei) Region

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Today, AWS announces the availability of Amazon Inspector in the AWS Asia Pacific (Taipei) Region. Amazon Inspector is an automated vulnerability management service that continually scans AWS workloads including Amazon EC2 instances, container images, and AWS Lambda functions for software vulnerabilities and unintended network exposure across your AWS Organization. 

With this expansion, Amazon Inspector extends its security coverage to AWS Asia Pacific (Taipei) Region, designed to help customers automatically discover workloads, conduct continuous vulnerability assessments, and receive actionable security findings. The service is designed to detect newly launched Amazon EC2 instances, Lambda functions, and eligible container images pushed to Amazon Elastic Container Registry (ECR) and scan them for software vulnerabilities and unintended network exposure.

All accounts new to Amazon Inspector are eligible for a 15-day free trial to evaluate the service and estimate its cost. During the trial, all eligible Amazon EC2 instances, AWS Lambda functions, and container images pushed to Amazon ECR are continually scanned at no cost. After the trial period, you will be charged based on public pricing for Amazon Inspector. Visit the Amazon Inspector pricing page for more details.

To get started with Amazon Inspector visit our documentation or begin your free trial today .

Amazon ECS introduces pause and continue controls for service deployments

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Amazon Elastic Container Service (Amazon ECS) now enables you to pause service deployments at critical stages during deployment progression and continue deployments when ready. You can use these pause points to introduce manual decision points and interactive controls into your deployments for scenarios such as manual approval workflows, operational checks, integration tests, or custom automation, while continuing to use native Amazon ECS deployment strategies with managed traffic shifting, bake times, fast rollbacks, CloudWatch alarms, and deployment circuit breaker.

With this launch, you can configure a new PAUSE deployment lifecycle hook as part of your Amazon ECS service deployment configuration. When a deployment reaches a configured pause point, Amazon ECS pauses deployment progression and emits Amazon EventBridge events that you can use to trigger automation workflows, approval systems, or external validation processes. You can then continue or roll back the deployment using the new ContinueServiceDeployment API. With pause hooks, you can configure timeout durations up to 14 days and timeout actions to automatically continue or roll back the deployment if no action is received.

You can configure pause hooks for rolling, blue/green, linear, and canary deployment strategies using the Amazon ECS Console, AWS CLI, AWS SDKs, AWS CloudFormation, AWS CDK, and Terraform. You can use the ContinueServiceDeployment API through the Amazon ECS Console, AWS CLI, and AWS SDKs. This feature is available in all AWS commercial and AWS GovCloud (US) Regions . To learn more, see our documentation on pause hooks for service deployments and continuing service deployments .

Amazon SageMaker HyperPod now supports data capture for inference workloads

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Amazon SageMaker HyperPod now supports data capture for inference workloads, a new capability that records inference request and response payloads from production endpoints to Amazon S3. Customers deploying generative AI models on HyperPod need visibility into model inputs and outputs to detect drift, troubleshoot production issues, build evaluation datasets, and continuously improve their deployed models, but previously had to build custom logging pipelines outside of the service to obtain this visibility.

With data capture, customers can train speculative decoding draft models from their real production traffic for better performance than generic draft models, build evaluation pipelines from production data, feed fine-tuning jobs with real-world inputs, and maintain audit trails for compliance. Customers choose where to capture inference traffic on each endpoint, at the SageMaker endpoint, the load balancer, or the model pod. Captured data is delivered asynchronously to their Amazon S3 bucket without blocking inference, and supports configurable sampling and customer-managed AWS KMS encryption. You can enable data capture when deploying models through the HyperPod Inference Operator, and use the captured data with Amazon SageMaker Model Monitor and your existing evaluation, fine-tuning, and draft-model training workflows.

This feature is available for SageMaker HyperPod clusters using the EKS orchestrator in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more, see Data capture for inference on HyperPod .

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