AI Lifecycle
The full span of an AI system from design, development, and deployment through monitoring, retraining, and retirement.
The AI lifecycle is the complete set of stages an AI system goes through, from initial design and development through deployment, monitoring, retraining, and eventual retirement. Governance must cover the entire lifecycle, not just the launch decision.
The NAIC Model Bulletin expects insurers to govern AI systems across their lifecycle. That includes defining the intended use, validating before deployment, monitoring after deployment, reviewing changes, and retiring systems that no longer meet standards.
A common governance failure is to focus on pre-deployment validation and neglect post-deployment monitoring. Models drift, data changes, and business use cases evolve. A lifecycle approach keeps governance current. See our glossary entries on the AI Systems Program, model validation, and ongoing monitoring.