AWS News - 2026-10-30

Amazon MSK Connect (Amazon Managed Streaming for Apache Kafka Connect) now lets you update the custom plugin and worker configuration of an existing connector. This is useful when you need to update the custom plugin to run a new version of your connector code, for example one with updated dependencies. You can also update the worker configuration to change worker-level settings, such as switching key and value converters or tuning producer and consumer properties for throughput and latency.

Amazon MSK Connect is a fully managed service that allows you to deploy, monitor, and scale Apache Kafka Connect connectors with Amazon MSK (Managed Streaming for Apache Kafka). Previously, updating a connector's plugin or worker configuration meant deleting the connector and recreating it - a disruptive operation that added risk and operational overhead. With this launch, you can update a connector's plugin and worker configuration in place. The connector keeps its committed offsets, so its tasks continue processing from where they left off once the update completes.

This capability is available at no additional cost in all AWS Regions where Amazon MSK Connect is supported. To learn more, visit Amazon MSK Connect developer guide.

Starting today, Amazon Aurora PostgreSQL-Compatible Edition introduces support for post-quantum TLS (PQ-TLS) key exchange, providing you with post-quantum cryptography options for encrypting your data in transit.

Amazon Aurora PostgreSQL versions 18 and higher support modification of the ssl_groups parameter. You can select cryptographic groups from the Amazon Aurora PostgreSQL allow list to align with your organization's security standards.

Amazon Aurora is designed for high performance and availability at global scale with full PostgreSQL compatibility. It provides scale-to-zero serverless compute, Aurora Global Database for multi-Region resilience, Aurora I/O-Optimized for improved price performance on I/O-intensive workloads, and built-in security and continuous backups. To get started, take a look at our getting started page.

Amazon RDS for SQL Server and Amazon RDS for Oracle now support RDS storage autoscaling on additional storage volumes. RDS storage autoscaling automatically scales storage capacity in response to growing database workloads, with zero downtime.

Additional storage volumes allow you to add up to three additional volumes to increase your storage capacity up to 256 TiB. Previously, you had to manually provision storage capacity on the additional storage volumes based on anticipated application capacity demands while autoscaling could be enabled on the primary storage volume. Under-provisioning could result in application downtime, and over-provisioning could result in under-utilized resources and higher costs. With RDS storage autoscaling, you simply set your desired maximum storage limit for each additional storage volume, and autoscaling takes care of the rest.

RDS storage autoscaling continuously monitors actual storage consumption, and scales capacity up automatically when actual utilization approaches provisioned storage capacity. Autoscaling works with new and existing database instances. Autoscaling has no additional charges and you only pay for the provisioned storage resources. You can enable it by using the AWS Management Console, AWS CLI, or Amazon RDS API.

RDS storage autoscaling for additional storage volumes is available for Amazon RDS for SQL Server and Amazon RDS for Oracle in all commercial AWS Regions and the AWS GovCloud (US) Regions. To learn more, please visit the Amazon RDS storage autoscaling documentation. 

Today, AWS announces that AWS partners who operate multiple legal entities can publish a product listing once in their primary account and enable their local subsidiaries to sell it through direct private offers — eliminating the need to recreate and maintain duplicate listings across every account.

From the primary account, which retains ownership of the listing, partners authorize which products each subsidiary can sell and which buyer countries it can extend private offers to. Each subsidiary transacts under its own local tax identity, selects the local currencies it supports, and sets its own disbursement preferences and bank account. The AWS Marketplace insights report in the primary account provides a consolidated view of offers, agreements, and renewals across all connected subsidiary accounts. Partners can revoke a subsidiary authorization or a subsidiary connection at any time, which immediately stops that subsidiary from creating new offers — agreements and offers already extended to buyers remain unchanged.

This capability is available today at no additional cost in all AWS Regions where AWS Marketplace is available. To get started, see  Managing subsidiary account connections  and  Creating private offers as a subsidiary account .

AWS Glue Data Catalog now supports automatic table optimization for Apache Iceberg tables in the US West (N. California) Region. Apache Iceberg is an open table format that delivers fast query performance over large tables in data lakes. With this launch, you can enable table optimization for individual tables or for all tables in your Data Catalog.

Apache Iceberg tables accumulate small files, snapshots, and unreferenced files with every write, which slows queries and increases storage costs over time. Table optimization continuously monitors your tables to compact small files in the background, remove snapshots that are past your retention settings, and delete unreferenced files that are no longer needed. You can monitor each run from the AWS Glue console, Amazon CloudWatch metrics, and logs.

You can enable table optimization using the AWS Glue console, AWS CLI, or AWS SDKs. For the full list of Regions where table optimization is available, see Supported Regions for table optimizers. To learn more, see Optimizing Iceberg tables in the AWS Glue Developer Guide. To get started, visit the AWS Glue product page.

Managed Prometheus collectors now support Amazon MSK consumer-group metrics for Amazon Managed Service for Prometheus and Amazon CloudWatch. The integration collects Kafka exporter metrics and consumer-group lag metrics from your Amazon MSK clusters.

Managed Prometheus collectors discover and collect Prometheus metrics from your workloads, so you don't run and operate collection agents yourself. You ingest the collected metrics into an Amazon Managed Service for Prometheus workspace or into Amazon CloudWatch, where you query and analyze them. You monitor Kafka stream health and consumer performance with consumer-group metrics. This helps you detect processing bottlenecks in your streaming applications.

This feature is available in all AWS Regions where managed Prometheus collectors are generally available. To learn more, see the documentation for Amazon Managed Service for Prometheus and Amazon CloudWatch.

Amazon SageMaker AI inference now supports GPU partitioning, enabling you to run several models on a single GPU instead of dedicating a whole accelerator to each one. Using NVIDIA Multi-Instance GPU (MIG) technology, SageMaker AI divides a GPU into as many as seven isolated partitions, each with its own memory and compute. Customers serving small and mid-size models, such as fine-tuned variants, embedding models, rerankers, and classifiers, previously had to give every model an entire accelerator, so an instance hosted no more models than it had GPUs and paid for GPU capacity those models could never use.

With GPU partitioning, you choose how to divide the GPUs behind your endpoint and place each model on a partition sized to fit it. Several models then share one accelerator with hardware-enforced isolation, so a busy model cannot take memory or throughput from the models beside it. Right-sizing an accelerator to a workload also opens up newer instance generations that would otherwise be more capacity than a single model needs. You configure GPU partitioning when you create an endpoint that hosts inference components, and you continue to invoke each model by name, so your application code doesn't change.

GPU partitioning is available on P4d, P5, G7, and G7e instances in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). To learn more, visit the Amazon SageMaker AI deployment page and see the Amazon SageMaker AI Developer Guide.

AI エージェント同士を組み合わせること自体は、いまではさまざまな方法で実現できます。2 つのコーディングエージェントを対話させる方法もあれば、Kiro のように公式の機能で複数のエージェントを段階的に動かす方法もあります。本記事が扱うのは、組み合わせ方そのものではありません。組み合わせた AI に書き込みを任せるとき、誰が権限を持ち、どこまで変更してよく、何を条件にマージするのか、という境界の置き方です。私は、自社プロダクトの開発のために、Kiro CLI を実装役とする仕組み（以下 BRIDGE）を作り、約 1 か月運用しました。本記事では、BRIDGE を作ったきっかけ、その構成と 4 つの境界、運用で分かったことを紹介し、最後に 2026 年 10 月時点の Kiro の標準機能と比較します。

2026 年 10 月 22 日、AWS Continuum for code vulnerabilities […]

数週間前、サンパウロにある AWS オフィスで、ブラジルの農業関連ビジネスに携わるビジネス部門と技術部門のリー […]

KDDI 株式会社では 2026 年 7 月、通信事業のお客さま向けサービスを支える Amazon EKS のクラスタを、マネージドノードグループから Amazon EKS Auto Mode へ移行しました。ノードが最長 21日で自動的に入れ替わる仕組みにより、脆弱性対応のたびに発生していたノードの更新作業がなくなりました。一方で、無人でのノード更新を前提にしたことで、起動時の CPU 需要の集中や、失効によるノード入れ替え時刻の制御といった課題が表面化しました。本稿では、Pod 配置の適正化と、カスタムコントローラ node-rotation-controller の開発による対処を、実測値とあわせて紹介します。

アマゾン ウェブ サービス ジャパン合同会社（AWS）は、2026 年 11 月 18 日（水）から 20 日 […]

re:Invent 2026 のクラウド運用トラックから、AI エージェントによるインシデント調査、修復、リリース検証、監査、モダナイゼーションを扱う AIOps セッションをテーマ別に紹介します。AWS DevOps Agent の事例やハンズオンを含め、目的に応じた推奨ルートもまとめます。

Bulletin ID: 2026-156-AWS
Publication Date: 10/29/2026 09:30 AM PDT

Description:

We are aware of CVE-2026-119115, an incorrect authorization issue in the issuer resolution logic. In affected versions, under certain conditions, an authenticated user whose approval policy only authorizes a namespace-scoped issuer could obtain certificates, including subordinate CA certificates, signed by a cluster-scoped issuer they are not authorized to use.

Impacted versions:
- aws-privateca-issuer before v1.10.1
- AWS Private CA Connector for Kubernetes EKS add-on before v1.10.1-eksbuild.1

Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.

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