AI Data Governance: Metadata Management, Data Catalogs, and AI-Ready Data Quality

Status: public · Confidence: medium (0.82) · Basis: verified_sources

## TL;DR
AI data governance is the discipline of documenting, classifying, tracing, and controlling data used by AI systems. Reliable public claims should focus on governance requirements, model documentation, metadata catalogs, and lineage rather than broad vendor performance promises.

## Core Explanation
Governance artifacts include dataset documentation, model cards, metadata catalogs, lineage graphs, data quality checks, and access controls. Regulation can make parts of this mandatory for high-risk systems, while documentation practices such as datasheets and model cards make data and model limitations easier to review.

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