Episode 24 — Run AI Risk Assessments Consistently: Methods, Criteria, and Evidence Rules (Domain 2)
Consistency in running AI risk assessments is paramount to maintaining a defensible and objective risk posture, a core competency tested in Domain 2. This episode explores the methodologies used to evaluate AI systems, including qualitative assessments for ethical concerns and quantitative methods for measuring model performance and error rates. For the AAIR certification, candidates must understand the criteria for determining risk levels and the strict evidence rules required to support audit findings. We examine how to conduct deep-dive reviews of data lineage, model architecture, and algorithmic fairness, ensuring that every assessment is backed by verifiable artifacts. Challenges in this area often stem from "black box" models where internal logic is opaque, requiring the use of proxy measures or third-party validation reports. Establishing a standardized assessment template ensures that all AI systems are held to the same rigorous standards regardless of which department developed them. This disciplined approach provides leadership with a comparable view of risk across the entire enterprise portfolio. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
