Policy goals: GDPR compliance, enable DSARs, maintain auditability. Components: 1) Data inventory & classification: catalog personal data, processors, retention policies. 2) Data retention & deletion: enforce purpose-based retention (e.g., store personal data only as long as needed), implement automated deletion workflows in data stores and feature store; flag datasets used for models and keep provenance metadata. 3) DSAR handling: provide processes to retrieve all personal data and model-inference records within legal timelines; support data export and deletion requests. For model outputs: retain inference logs (user id hash, input fingerprint, model version, prediction, timestamp) for X months (balanced with minimization) to answer DSARs. 4) Model explainability: maintain model cards and per-model documentation (training data scope, features, performance, known biases). Provide counterfactual explanations and local explanations (SHAP summaries) for subject requests where feasible. 5) Deletion handling: when data subject requests deletion, remove personal data from raw stores and mark affected model training data; schedule retrain pipeline to remove influence (if immediate removal required, consider targeted unlearning or retraining from retained data sans deleted records). 6) Audit trails: immutable logs of data access, model training runs, dataset and model version hashes, approvals, and deployments. 7) Governance: data protection officer oversight, periodic audits, role-based access, encryption, and retention review. Trade-offs: full retraining on deletion is expensive; implement selective unlearning or document limits and communicate to DSAR requesters. This policy balances compliance, explainability, and operational feasibility.
