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All glossary terms

AI governance

AI Lifecycle Management

AI lifecycle management is the coordinated oversight of an artificial intelligence system as it is proposed, built or selected, validated, deployed, monitored, changed, and retired. It keeps the system’s purpose, ownership, evidence, and controls connected as technical and organizational conditions evolve.

Also known as AI lifecycle.

Why it matters in practice

A model’s risk profile can change when its data, users, integrations, or operating context changes. Lifecycle processes make those changes visible and create points for review rather than treating launch as the end of governance. The exact stages differ across organizations and system types.

Example

A team opens a review when it changes a model’s intended use, repeats relevant tests, updates the inventory, and records the next monitoring owner.

Educational note

This definition is general information, not legal advice. It may omit facts that matter to a specific situation and does not establish or guarantee compliance.