How should data teams govern AI-generated column descriptions before they are published in a catalog?

Data teams should treat AI-generated column descriptions as drafts that still need human review. The workflow should capture the column name, source table, and schema, then route the result to a steward or custodian who can approve, correct, or regenerate it. This keeps business meaning in the loop and reduces the risk of inaccurate metadata becoming trusted by downstream users.

Why AI-Generated Column Descriptions Need a Review Gate

AI-generated column descriptions can be useful drafting aids, but they are not ready-made metadata. A catalog entry is part of the organisation’s shared semantic layer, so even small wording errors can misstate meaning, hide caveats, or suggest a certainty the model does not actually have. Treating the output as provisional preserves trust in the catalog and prevents accidental propagation of bad metadata.

The practical issue is not only accuracy, but authority. Once a description appears in a catalog, downstream analysts, engineers, and governance tools may rely on it as if it were approved business meaning. That is why review should focus on whether the description matches the source table, reflects the schema, and uses the right business context for the data asset.

Good governance also keeps the catalog from becoming a place where automated text is accepted simply because it is fast to produce. If the description cannot be traced back to the column name, source table, and schema, it should remain a draft until a human steward can validate or rewrite it.

What the Review Workflow Should Capture

The minimum review packet should carry enough context for a steward to judge whether the description is faithful to the data. At a minimum, that means the column name, the source table, and the schema, plus any obvious business definition or usage notes that help distinguish the field from similarly named columns elsewhere.

That context matters because many description errors are not linguistic, they are contextual. A model may produce a plausible sentence for a field name, but still miss whether the column is a code, a status, a derived value, a free-text note, or a technical surrogate. The reviewer should be able to see the data shape before deciding whether the wording is fit for publication.

The workflow should also preserve versioning so corrections are not lost. If a steward edits the AI draft, the catalog should retain the approved text and, where useful, the reason for change. That creates a repeatable approval path instead of a one-off editorial fix.

Where Governance Fails if AI Text Is Published Too Early

The main failure mode is false confidence. When a generated description looks polished, users tend to trust it even when it is vague, outdated, or semantically wrong. In a catalog, that can lead to incorrect joins, poor metric definitions, weak lineage interpretation, and governance decisions based on the wrong field meaning. NIST’s AI guidance on governance and content provenance is relevant here, because it reinforces the need to manage AI output before it is treated as authoritative NIST AI 600-1 GenAI Profile.

A second failure mode is scale. A single bad description is a nuisance; many bad descriptions become an institutional knowledge problem. If teams publish AI text without review, they can flood the catalog with inconsistent definitions, duplicated wording, and silent errors that are difficult to clean up later. That is why the control should be built into the publishing step, not left to informal post-publication cleanup.

For teams building a broader AI governance layer, a formal management system helps make that review step explicit and auditable ISO/IEC 42001:2023 AI Management System Standard. If the catalog is used across regulated or operationally sensitive data, the same discipline supports the governance expectations described in the NIST AI Risk Management Framework.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

NIST AI RMF and NIST SP 800-53 Rev 5 set the technical controls, while ISO/IEC 42001:2023 defines the regulatory obligations.

Framework	Control / Reference	Relevance
NIST AI RMF	Govern Map Measure Manage	AI-generated descriptions need governance, provenance and human oversight before publication.
Recommendation — Establish human review and provenance controls before AI text enters the catalog.
ISO/IEC 42001:2023	AI management system	Catalog description generation is an AI output that needs accountable review and release controls.
Recommendation — Define approval, accountability and change-control steps for AI-generated metadata.
NIST SP 800-53 Rev 5	CM-8 — System Component Inventory	Catalog entries depend on accurate asset and schema context before metadata is trusted.
	AU-6 — Audit Record Review, Analysis, and Reporting	Approved and corrected descriptions should be reviewable and traceable for governance.
	SA-8 — Security and Privacy Engineering Principles	Published descriptions should reflect controlled, verified information quality before release.
Recommendation — Maintain accurate schema inventory data to support metadata review and approval. Retain review decisions and corrections so metadata changes are traceable. Apply verification principles so generated descriptions are validated before publication.

Practitioner Guidance

What to prioritise: Put the approval gate on the path to publication, not after publication. The steward should be the control point for any description that will be visible to analysts or embedded in data discovery workflows.

What to verify: Check that the generated description matches the column’s actual type, source table context, and schema role. If the wording cannot be defended from those three inputs, treat it as unfit for release.

What good looks like: Approved descriptions read like concise business metadata, not model prose. They are specific enough to distinguish the field from neighbours, but cautious enough not to invent meaning that the data does not support.

Common mistake: Teams often review for grammar instead of governance. A polished sentence can still be wrong, so the review should test semantic accuracy, not just readability.

Practitioner takeaway: The safest pattern is to let AI draft metadata, but require a human to own the final meaning before the catalog becomes a source of truth.