ml-database-audit

Audit ML database schemas by running looped checks across pre-flight, schema-intent, table-contract, and persistence boundary stages to validate explicit writers, readers, and retention rules. This enforces governance and prevents illicit data promotion to cloud environments.

Schema-intent validation is a pre-flight check that verifies scope, designated writers, readers, and boundary conditions across training, forecasts, and analytics schemas. It ensures database contracts align with migration plans before deployment approval gates.

Enforce explicit writers and readers through table-contract checks that validate retention policies, guardrails against duplicate truth, and persistence boundaries. This structured audit process ensures safe handling of ML data without model training or leakage.

Yes, the database audit workflow supports an approval-gate process with re-audit loops to confirm fixes before deployment. It applies audit-driven governance to validate schema contracts and persistence boundaries against phase gate requirements.

Yes, you can validate migration plans for analytics and forecast schemas through controlled promotion checks and persistence boundary validation. The audit workflow explicitly prevents unintended data promotions and model training during the schema review process.

The schema audit workflow is limited to validating database contracts, persistence boundaries, and gating rules for ML data surfaces. It does not execute model training, handle data transformation, or manage infrastructure provisioning outside of schema and contract validation.