Episode 28 — Define AI Controls and Testing Plans: What to Verify and How Often (Domain 2)
The effectiveness of any AI risk program rests on the strength of its controls and the rigor of its testing plans, a key area of expertise for Domain 2. This episode defines the difference between preventive, detective, and corrective controls specifically as they apply to AI systems, such as input filters, performance alerts, and automatic failovers. For the AAIR certification, understanding what to verify—such as data integrity, model accuracy, and security posture—is just as important as knowing how often to test, whether that be continuous, monthly, or triggered by a model update. We discuss the development of comprehensive test scripts that simulate both normal operations and adversarial scenarios, such as prompt injection or data poisoning attacks. Best practices include using independent testing teams to avoid bias and ensuring that the results of every test are documented as evidence of control effectiveness. By defining these controls and testing cadences clearly, organizations can move from a "trust me" model to a "show me" model, providing tangible proof that their AI systems are operating within safe and expected parameters. 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.
