The policies, procedures, and controls that ensure data quality, privacy, and usability across an organization’s data assets, including AI training datasets.
A superset of data governance that explicitly ties data handling to broader risk, privacy, and compliance objectives. It defines data classifications, access-control policies, retention schedules, and stewardship roles, ensuring that AI pipelines consume only vetted, compliant data and that lineage and usage are fully documented for audits.
A multinational sets up an Information Governance Council that enforces classification tags (e.g., “PII”, “Sensitive”), configures data-catalog tools to enforce access based on tags, and requires any dataset used in AI to pass a “data-privacy checklist” before ingestion - ensuring consistent treatment of all data assets.
