A model deployed on premises uses data stored in Amazon S3 and processes sensitive information to power a live conversational engine in a hybrid cloud setup. The ML engineer must detect and remove sensitive data with minimal operational overhead. Which solution meets this requirement?

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS. It automatically detects sensitive data in Amazon S3, such as personally identifiable information (PII), which directly addresses the requirement for detecting sensitive data with minimal operational overhead. Integrating Macie with AWS Lambda allows for automated removal of identified sensitive items, providing an efficient and scalable solution for the hybrid cloud setup. Deploying the model on Amazon SageMaker or an Amazon ECS cluster with Fargate focuses on model deployment, not sensitive data detection and removal from S3. While AWS Lambda can be used for data stripping, it would require significant custom development to replicate Macie's capabilities. AWS Batch is for batch processing, not real-time or continuous sensitive data detection. Amazon Comprehend is a natural language processing (NLP) service, useful for understanding text, but not specifically designed for discovering and classifying sensitive data across S3 at scale like Macie. Launching EC2 instances for removal adds unnecessary operational complexity compared to serverless Lambda functions.