Information Governance
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.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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