FinSight has an automated ML pipeline in CodePipeline that stages new S3 datasets into training-buckets. Before any dataset is used for training, security requires detection of PII and automatic blocking of pipeline progress if PII is found. Which integration approach using Amazon Macie will allow the pipeline to conditionally proceed or fail based on PII findings?

The correct approach leverages Macie's ability to detect PII and integrates its findings into the pipeline's control flow. Macie can perform continuous classification or on-demand jobs to identify sensitive data in S3. Routing these findings to EventBridge allows for automated, event-driven reactions. A Lambda function or Step Functions workflow can then consume these events, analyze the Macie findings (e.g., checking for PII above a defined threshold), and programmatically halt the CodePipeline execution if PII is detected, preventing its use in training. Incorrect options: S3 Block Public Access and S3 Access Point policies control access, not content. Macie doesn't automatically prevent access based on PII findings in a way that directly fails a pipeline step; it reports findings. Amazon Comprehend is excellent for natural language processing and PII detection within text, but Macie is specifically designed for comprehensive data security and PII discovery across S3, including various file types, making it the primary tool for this use case. The statement that Macie is "only for discovery jobs" is incorrect. S3 Object Lock prevents object deletion or modification, not PII detection or automatic skipping by SageMaker based on content. SageMaker does not inherently integrate with Object Lock for PII-based skipping.