Security, Compliance, and Governance for AI Solutions

Official references for this objective

• Amazon Web Services — Security and compliance - Machine Learning Best Practices for Public Sector Organizations

  communication between the VPC and the SageMaker API or Runtime is entirely and securely within the AWS network. VPC endpoint policies can be configured to

• Amazon Web Services — Data encryption - Amazon Bedrock

  Model customization jobs and their output custom models – During job creation in the console or by specifying the customModelKmsKeyId field in the CreateModelCustomizationJob API

• Amazon Web Services — Detect and filter harmful content by using Amazon Bedrock Guardrails - Amazon Bedrock

  a call center application to summarize conversation transcripts between users and agents can use guardrails to redact users’ personally identifiable information (PII) to protect user

• Amazon Web Services — MLSEC03-BP03 Protect sensitive data privacy - Machine Learning Lens

  Use automated sensitive data discovery in Amazon Macie to gain continuous, cost-efficient, organization-wide visibility into where sensitive data resides across your Amazon S3 environment.

• Amazon Web Services — AWS managed policies for Amazon SageMaker AI - Amazon SageMaker AI

  You can't change the permissions in AWS managed policies.

• Amazon Web Services — Monitoring and auditing with CloudTrail - Amazon SageMaker AI

  Under the Event record find onBehalfOf key. This contains the userId key and other user identification information that can be mapped to a specific IAM