AI initiatives often start with a model or assistant. Reliable outcomes start earlier—with decisions about who owns the data, who may use it, what it means, and when it should no longer be retained. This guide shows how those data governance decisions shape everyday AI use cases.
Data governance and AI governance have different jobs
Data governance sets responsibility and rules for the information itself: definitions, quality, classification, access, sharing, lifecycle, and traceability. AI governance adds oversight of AI use cases: appropriate purpose, review, testing, human supervision, incidents, and accountability for outputs. Neither works well in isolation.
For example, an approved AI assistant may still return outdated or overshared records unless the underlying information is properly governed.
Use case 1 — A service desk assistant uses obsolete instructions
Scenario
A support assistant retrieves old troubleshooting articles alongside current instructions. It recommends an obsolete access request process.
Governance response: Name the knowledge-base owner, define an approved source set, publish review dates internally, archive superseded guidance, and test retrieval before rollout.
Review measure: Rate of approved-source use and time to correct stale content.
Use case 2 — Contract pricing appears in the wrong search results
Scenario
Nonpublic proposal pricing is stored in a shared workspace with inherited access permissions. An AI search tool makes it easier for unauthorized internal readers to discover.
Governance response: Label the files, assign procurement ownership, remove broad access, define permitted use and sharing, and validate effective permissions with test accounts.
Review measure: Sensitive files with unrestricted access; unresolved permission exceptions.
Use case 3 — Department reports disagree on basic definitions
Scenario
One system defines an active customer differently from another. AI-generated summaries compare incompatible figures as if they were equivalent.
Governance response: Document shared terms, authoritative sources, ownership, transformations, and lineage; require the output to disclose source assumptions.
Review measure: Critical metrics with approved definitions and traceable lineage.
Use case 4 — Personnel records enter an unapproved AI workflow
Scenario
An employee pastes sensitive personnel information into a public generative AI tool to draft an internal communication.
Governance response: Define approved AI uses, sensitive content handling, access to sanctioned tools, staff training, and a reporting process for suspected exposure. Use DLP controls where available and appropriately configured.
Review measure: Policy exception trends, completion of targeted training, and evidence of response testing.
Use case 5 — Public records and retention obligations conflict with AI convenience
Scenario
A public-sector team wants to reuse historical case documents in a search assistant, but different records have distinct retention, disclosure, privacy, and access requirements.
Governance response: Identify record classes and applicable obligations, confirm authorized purposes and review processes, and limit indexing of restricted material. Obtain legal and records-management review for actual statutory requirements.
Review measure: Data sources with approved retention and access decisions; exception closure.
A practical decision model
- Business sponsor: Defines the purpose and accepts operational outcomes.
- Data owner: Authorizes source use and access; understands quality and sensitivity.
- Security and privacy: Evaluate data exposure, handling, and risk controls.
- Records or legal: Advise on retention, disclosure, and contractual restrictions where relevant.
- AI solution owner: Tests retrieval, output quality, misuse, monitoring, and escalation.
Document key decisions before launching, then review them as sources, permissions, and use cases change.
Governance KPIs worth tracking
Examples include sensitive sources with named owners; percentage of AI-indexed sources approved for use; percentage of critical definitions documented; stale access exposures resolved; and high-risk output findings closed after testing. Set a baseline before establishing targets.
Further reading
The NIST AI RMF Playbook organizes recommended practices around Govern, Map, Measure, and Manage. Use it as a voluntary framework for context-specific oversight rather than as a certification checklist.