Risk Tiering
Classifying AI systems by the level of consumer harm they could cause, so governance and testing can match the risk.
Risk tiering is the practice of classifying AI systems based on how much consumer harm a wrong output could cause. A model that recommends marketing copy is low-risk. A model that recommends coverage denials, sets premiums, or makes claims decisions is high-risk.
The NAIC Model Bulletin and state rules expect carriers to apply governance proportional to risk. High-risk systems need more documentation, testing, human oversight, and monitoring. Low-risk systems still need basic security and privacy review, but not full model validation.
A clear risk-tiering framework helps carriers focus resources, satisfies examiners, and avoids the common mistake of applying the same governance process to every AI tool. See our guides to AI governance in insurance and AI vendor risk assessment.