RAIMON01-BP03 Preserve data privacy and set access controls on monitored data

Apply data governance processes that specify what AI monitoring data can be collected, processed, stored, and accessed throughout the monitoring lifecycle. This involves implementing policies that define permissible data collection scope, processing methods, storage requirements, and access protocols for AI model monitoring activities. For instance, a facial recognition AI system allows collection of prediction accuracy metrics and inference latency but prohibits storage of actual facial images or biometric features. Use AWS Config to enforce data governance rules and AWS CloudTrail to audit adherence with data collection policies.

Implement privacy-preserving techniques including anonymization, differential privacy, and secure computation methods that enable AI system oversight without exposing individual user information. This requires deploying technical safeguards that protect user privacy while maintaining monitoring capabilities. For example, a healthcare chatbot application could anonymize patient identifiers in conversation logs, apply differential privacy to response accuracy metrics, and encrypt the monitoring data. Use Amazon SageMaker AI Processing jobs to run anonymization and differential privacy implementations, Amazon Macie to identify and protect sensitive data in monitoring datasets, and AWS KMS for encryption and key management.

Create role-based access controls that limit AI monitoring data access to authorized personnel based on job function, with detailed audit trails tracking data access activities. This involves implementing granular permissions that restrict monitoring data visibility to specific roles and responsibilities. For example, data scientists access model accuracy metrics while security teams access only anomaly detection alerts, with access types logged and monitored. Use AWS IAM to implement role-based access controls and AWS CloudTrail to maintain detailed audit trails of monitoring data access.

Establish data retention policies that specify how long different types of AI monitoring data should be stored, with automated deletion processes and procedures for handling individual data requests. This requires defining lifecycle management rules for various monitoring data types and implementing automated compliance-aligned processes.