What is AI model governance?

AI model governance is the set of processes and controls for managing AI models throughout their lifecycle — covering selection, evaluation, documentation, access control, versioning, monitoring, and retirement — to ensure models perform as intended and that accountability for model behavior is clearly assigned.

What model governance covers

Model governance operates across the model lifecycle. At selection and procurement, it covers how models are evaluated before adoption — what performance benchmarks matter, what bias and safety testing is required, and what documentation the organization requires from model providers. At deployment, it covers access controls (who can query the model and for what purposes), version management (which model version is live, when updates are applied, and how regressions are caught), and documentation (what the model is, what it was trained on, what it can and cannot do). At monitoring, it covers how model behavior is tracked in production and what triggers a review or rollback. At retirement, it covers how models are decommissioned and what happens to applications that depend on them.

Why model governance differs from system governance

General AI governance applies to AI systems — the full stack of data, model, application, and deployment. Model governance focuses specifically on the model component. The distinction matters because model behavior is probabilistic and opaque in ways that application-level controls cannot fully compensate for: a model that produces biased outputs on a class of inputs will do so regardless of how well the application around it is built. Model governance ensures the model itself meets defined standards before it is integrated, and that its behavior is monitored after integration. This is distinct from, and complementary to, governance of the application that uses the model.

Model documentation requirements

Effective model governance requires documentation at each lifecycle stage. Model cards — structured documents describing a model's intended use, evaluation results, limitations, and known failure modes — are a widely adopted format for model-level documentation. For internally developed models, documentation should cover training data sources and preprocessing, evaluation methodology and metrics, known performance gaps across demographic or domain categories, and the decisions made during development. For third-party models, governance requires obtaining whatever documentation the provider makes available and supplementing it with the organization's own evaluation results.

What is AI model governance? — FAQ

Who is responsible for model governance in an organization?

Responsibility is typically distributed. Data scientists and ML engineers own technical documentation and evaluation. Risk or compliance teams own the review process for high-risk model uses. Business units own the decisions about which models to deploy for which purposes. Legal and privacy teams review training data and output implications. Effective model governance requires all these groups to have defined roles rather than assuming another team is handling it.

How does model governance apply to third-party foundation models?

Organizations cannot govern the internal development of third-party foundation models, but they can govern how those models are selected, evaluated, and used internally. That means: requiring model cards or equivalent documentation from providers, running your own evaluation on use-case-relevant tasks, maintaining records of which model version is in use and when updates are applied, monitoring for performance changes when providers update their models, and having a contingency plan if a provider changes terms or discontinues a model.