ModelRefs / AI Governance — AI Glossary

AI Governance — AI Glossary

The policies, processes, roles, and controls organizations implement to manage AI risk, ensure accountability, and maintain compliance.

Overview

AI governance covers model selection, vendor risk, data provenance, usage policies, audit trails, incident response, and board-level oversight. Frameworks: NIST AI RMF, ISO 42001, EU AI Act. Operationally implemented through shadow AI audits, acceptable use policies, and model risk management.

Reference details

Topic	governance
Last reviewed	2026-06-24

Example: The question an auditor actually asks

Not “do you have a policy?” but “show me which model version answered this customer on 14 March, what data it saw, who approved that deployment, and what your evaluation said at the time.” Governance is whatever makes that answerable months later. If the answer requires archaeology, the governance is aspirational.

Commonly confused with

Governance is not the same as compliance. Compliance is meeting a specific external requirement; governance is the internal apparatus that makes compliance demonstrable and repeatable. You can pass an audit without governance — once.

When to use it

Reach for it when:

AI touches regulated decisions, personal data, or material spend
More than one team deploys models and consistency matters
You are subject to the EU AI Act, ISO 42001 or a sector regulator

Reach for something else when:

As a document nobody references — unenforced governance is worse than none, because it is evidence you knew
At a weight that stops experimentation on genuinely low-risk internal tools

Referenced by

This term is used by the following ModelRefs references:

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Frequently asked questions

What is AI Governance?