A risk lifecycle is the ongoing process of identifying, assessing, treating, and monitoring risk throughout a system’s use. For AI, review should begin before deployment and continue as models, data, prompts, integrations, or business purposes change. A one-time approval can become outdated when the system or its operating conditions shift.
In cyber security, lifecycle reviews help organizations detect emerging weaknesses and keep accountability clear. Teams can assign an owner, set monitoring thresholds, document changes, and define when a system must be restricted, reviewed, or paused. In practice, a new data source or use case may trigger reassessment; monitoring may also reveal unreliable or harmful outputs that require escalation. This makes risk management an operational control, not just a compliance checklist.