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AWS adds the ability to centrally manage access to AWS Regions and AWS Local Zones

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Today, AWS announces the ability to manage access to AWS Regions and AWS Local Zones from a single place within the AWS Management Console. With this new capability, customers can now efficiently monitor and manage access to AWS Regions and AWS Local Zones globally.

AWS Global View enables customers to view resources across multiple Regions in a single console. To get started, customers can find “AWS Global View” in the AWS Management Console and navigate to the Regions and Zones page. The Regions and Zones page displays infrastructure location details, opt-in status, as well as any parent Region relationships, making it easier for customers to manage and monitor their global AWS footprint.

This capability is available in all AWS commercial Regions. To learn more, visit the AWS Global View  documentation, or navigate to the Regions and Zones page here .

Amazon SageMaker Catalog adds support for governed classification with restricted terms

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Amazon SageMaker Catalog now supports governed classification through Restricted Classification Terms, allowing catalog administrators to control which users and projects can apply sensitive glossary terms to their assets. This new capability is designed to help organizations enforce metadata standards and ensure classification consistency across teams and domains.

With this launch, glossary terms can be marked as “restricted”, and only authorized users or groups—defined through explicit policies—can use them to classify data assets. For example, a centralized data governance team may define terms like “Seller-MCF” or “PII” that reflect data handling policies. These terms can now be governed so only specific project members (e.g., trusted admin groups) can apply them, which helps support proper control over how sensitive classifications are assigned.

This feature is now available in all AWS regions where Amazon SageMaker Unified Studio is supported.

To get started and learn more about this feature, see SageMaker Unified Studio user guide .

AWS Clean Rooms ML now supports redacted error log summaries

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AWS Clean Rooms ML custom modeling enables you and your partners to train and run inference on a custom ML models using collective datasets at scale without having to share your sensitive data or intellectual property. With today’s launch, collaborators can configure a new privacy control that sends redacted error log summaries to specified collaboration members. Error log summaries include the exception type, error message, and line in the code where the error occurred. When associating the model to the collaboration, collaborators can decide and agree which members will receive error log summaries and whether those summaries will contain detectable Personally Identifiable Information (PII), numbers, or custom strings redacted.

AWS Clean Rooms ML helps you and your partners apply privacy-enhancing controls to safeguard your proprietary data and ML models while generating predictive insights—all without sharing or copying one another’s raw data or models. For more information about the AWS Regions where AWS Clean Rooms ML is available, see the AWS Regions table. To learn more, visit AWS Clean Rooms ML .

AWS Clean Rooms supports adding new data providers to existing collaborations

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AWS Clean Rooms now supports the ability to add data provider members to an existing collaboration, offering customers enhanced flexibility as they iterate on and develop new use cases with their partners. With this launch, you can collaborate with new data providers without having to set up a new collaboration. Collaboration owners can configure an existing Clean Rooms collaboration to add new members that only contribute data, while benefiting from the privacy controls existing members already configured within the collaboration. New data providers invited to an existing collaboration can be reviewed in the change history, enhancing transparency across members. For example, when a publisher creates a Clean Rooms collaboration with an advertiser, they can enable adding new data providers such as a measurement company, which allows the advertiser to enrich their audience segments with third-party data before activating an audience with the publisher. This approach reduces onboarding time while maintaining the existing privacy controls for you and your partners.

AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms .

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