AI governance platforms generate vast volumes of audit data—prompt/completion logs, model inference traces, user interaction records, and version lineage. Manually classifying this data for retention is impractical. An AI integration connects to platforms like Collibra Data Governance or OneTrust Privacy Management via their REST APIs and workflow engines to automate this lifecycle. The integration uses the governance platform's policy engine to apply intelligent rules: for example, logs containing PII from high-risk models are tagged for a 7-year retention to meet GDPR, while generic debugging traces from sandbox environments are flagged for 90-day automated deletion. This creates a policy-aware data lake for AI operations, where every log's retention schedule is dynamically determined and enforceable.




