## TL;DR
AI is transforming data governance from manual, reactive processes to automated, proactive intelligence. ML-powered platforms automatically discover, classify, and monitor data assets across enterprises, ensuring data quality, compliance, and discoverability. As AI systems consume and generate data at unprecedented scale, governance becomes the foundation of trustworthy AI.

## Core Explanation
Data governance pillars: (1) Metadata management -- cataloging data assets (tables, columns, dashboards, ML features) with business context (descriptions, owners, usage, quality scores). AI automates metadata harvesting from databases, data warehouses, and BI tools; (2) Data lineage -- tracing data from source to consumption, understanding dependencies. AI reconstructs lineage from query logs and pipeline definitions; (3) Data quality -- measuring completeness, accuracy, freshness, uniqueness, and consistency. AI monitors quality metrics, detects anomalies, and alerts data owners; (4) Access control and compliance -- classifying sensitive data (PII, PHI, PCI) via NLP-based classifiers, enforcing access policies. AI governance platforms: DataHub (LinkedIn open-source), Alation, Collibra, Atlan, Apache Atlas.

## Detailed Analysis
ML for data classification: fine-tuned models (BERT-based) scan column names, sample values, and descriptions to classify columns as PII (email, SSN, phone), financial (revenue, price), or healthcare (diagnosis codes). Regular expression + ML hybrid achieves 95%+ accuracy. Data lineage: AI parses SQL query history to build column-level lineage graphs. For complex ETL (Python/Spark), static code analysis tracks data transformations. Data quality monitoring: time-series anomaly detection on data metrics (row count, null percentage, value distribution drift) alerts when quality degrades. Feathr/Feast feature stores: governance for ML features used in training and inference, ensuring consistency between offline training and online serving. EU AI Act implications: Article 10 requires data governance for high-risk AI -- data must be relevant, representative, free of errors, and complete. Organizations must document data provenance, preprocessing, and quality assessments. Model cards (Google, 2019) provide standardized documentation of model training data, intended use, limitations, and bias evaluation.