AI Governance Platform: What Complete Coverage Requires

Effective AI governance requires tracing model inputs beyond warehouse tables to their origins in Python pipelines, application code, ORM layers, APIs, and other upstream systems. The passage argues that conventional data catalogs and governance tools often rely on warehouse metadata and query logs, leaving gaps in lineage for engineered features and application-derived data unless those processes are manually documented. It identifies model-input provenance, source-code analysis, application-layer visibility, deterministic dependency-based lineage, and audit-ready evidence as necessary capabilities for responding to regulatory, audit, and accountability requirements. It presents Foundational as a platform that analyzes source code to create this broader lineage, citing Lemonade as an example of accelerated regulatory approval for AI underwriting, while emphasizing that complete governance depends on proving how specific model data was created and transformed rather than merely documenting policy.

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