AWS Audit Manager extends generative AI best practices framework to Amazon SageMaker

AWS Audit Manager extends generative AI best practices framework to Amazon SageMaker. Sometimes I hear from tech leads that they would like to improve visibility and governance over. Their generative artificial intelligence applications. How do you monitor and govern the usage and generation of data to address issues regarding security. Resilience, privacy, and accuracy or to validate against best practices of responsible. AI among other things? Beyond simply taking these into account during the implementation phase. How do you maintain long-term observability and carry out compliance checks throughout the software’s lifecycle.

Today, we are launching an update to the AWS

Audit Manager generative AI best practice framework on AWS Audit Manager. This framework simplifies evidence collection and enables you to Europe Cell Phone Number List continually audit and monitor the compliance posture of your generative AI workloads through 110 standard controls which are pre-configure to implement best practice requirements. Some examples include gaining visibility into potential personally identifiable information (PII) data that may not have been anonymize before being used for training models, validating that multi-factor authentication (MFA) is enforce to gain access to any datasets used, and periodically testing backup versions of customize models to ensure they are reliable before a system outage, among many others.

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These controls AWS Audit Manager extends generative compliance checks from

AWS Config and AWS Security Hub, gathering Benin Phone Number List user activity logs from AWS CloudTrail and capturing configuration data by making application programming interface (API) calls to relevant AWS services. You can also create your own custom controls if you need that level of flexibility. Previously, the standard controls included with v1 were pre-configured to work with Amazon Bedrock and now, with this new version, Amazon SageMaker is also included as a data source so you may gain tighter control and visibility of your generative AI workloads on both Amazon Bedrock and Amazon SageMaker with less effort.

 

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