AI Glossary

AI Governance

AI governance is the set of policies, roles, controls, and documentation that organizations use to oversee AI systems across their lifecycle.

Definition

AI governance is the set of policies, roles, controls, and documentation that organizations use to oversee AI systems across their lifecycle.

Plain English explanation

Who decides what models can do, how they are checked, and how issues are escalated.

Technical explanation

Governance stacks include inventories, policies, approvals, monitoring, and escalation paths across the AI lifecycle, often aligned to frameworks such as NIST AI RMF.

Why it matters

As model counts grow, governance becomes the control plane for auditability and accountability.

Real-world applications

  • Model inventory and documentation
  • Policy enforcement workflows
  • Third-party AI risk review

Benefits

  • Clear ownership
  • Better audit readiness

Limitations

  • Process overhead if poorly designed
  • Must stay connected to engineering reality

Common misconceptions

  • Governance tools do not automatically create compliance

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Latest developments

FAQ

How is this different from MLOps?

MLOps ships and monitors models; governance sets who may ship what, under which evidence and controls.

Last reviewed

Sources

  • Enterprise model risk management practices (financial services)

Correction request

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