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A data-driven evaluation of potential AI threats, estimating likelihoods and impacts numerically to prioritize mitigation efforts.
Uses statistical and probabilistic methods - Monte Carlo simulations, value-at-risk calculations, Bayesian risk models - to assign numerical scores to identified risks (e.g., model drift, data breaches). Quantitative assessments allow direct comparison of disparate risks, support cost-benefit analyses of controls, and feed into enterprise risk dashboards. Governance mandates consistent risk-scoring methodologies, transparent assumptions, and periodic re-estimation as data evolves.
Real world example:
An insurer quantifies the risk of automated-underwriting errors by modeling the probability of misclassification (2%) and average claim cost ($10k), yielding an expected loss of $200 per policy. They compare this to control-implementation costs, deciding to invest in additional validation rather than manual reviews - optimizing risk mitigation spend.
