lifecycle phases

Lifecycle phases refer to the sequential stages of an AI system’s existence as defined in the framework: design, build, test, deploy, operate and retire. The framework requires assurance activities to be applied proportionately at each phase to manage risks and maintain documentation for auditability and continuous improvement.

The discrete phases of an AI system's life as defined in the statement — planning, data collection, model construction or adaptation, testing and validation, deployment, operation and monitoring, and decommissioning — each of which carries specific governance, assessment and documentation obligations.

A structured set of stages the framework uses to anchor obligations and responsibilities: design and procurement, development and testing, pre‑deployment assessment and approval, operational monitoring and maintenance, and end‑of‑life decommissioning and data clean‑up. Each phase carries specific recommended measures such as specifications, validation, impact assessments, continuous monitoring and retention/cleanup planning.

Defined stages in the development and operation of an AI system—typically design, development, testing, deployment and monitoring—used by the Code to structure proportionate risk management, documentation, testing and oversight activities across the system's life.