AI risk assessment is most useful when it supports a decision: approve, restrict, redesign, monitor, pause or retire. Quantica Risk Modelling's approach starts with the use case and maps customer, prudential, operational, legal, conduct, privacy and cyber impacts before selecting a model or supplier.
Score impact and uncertainty separately
Impact measures the consequence if the system is wrong or misused. Uncertainty measures how confident the organisation is in its data, validation, monitoring and control evidence. A low-frequency but high-impact use case should not be treated as low risk merely because few incidents have occurred.
Assess control strength
Review human oversight, access, data quality, explainability, validation, monitoring, incident response, supplier resilience and exit. A control receives credit only when it is designed, implemented and evidenced. Residual risk is the remaining exposure after those controls operate.
Turn the assessment into action
Define thresholds for approval, escalation and re-assessment. Record the decision, owner, evidence and review date. The NIST AI RMF provides a complementary lifecycle reference. This article describes Quantica's analytical perspective, not a regulatory certification.