Lifecycle management is the continuous process of monitoring, updating, validating, and eventually retiring an AI system as its data, users, business role, and threat environment change. It starts before deployment and continues through operation, patching, retraining, access review, logging, and secure decommissioning.
In cyber security, lifecycle management matters because an AI model that was safe in testing can become risky in production. Data drift, new attack techniques, and changes in compliance rules can cause bad outputs or expose sensitive information. Attackers may also target older, unmaintained models because they lack patches, guardrails, or monitoring. Strong lifecycle management helps defenders detect degradation, revoke unsafe access, roll back flawed updates, and retire systems that can no longer be trusted.