Episode 43 — Test for Safety Failures: Hallucinations, Toxicity, and Unsafe Recommendations (Domain 3)
Safety testing is a non-negotiable step in Domain 3, particularly for generative models and autonomous systems that interact directly with humans. This episode examines the detection and mitigation of safety failures such as hallucinations, where the AI generates plausible but false information, and toxicity, where the output is harmful, biased, or inappropriate. For the AAIR exam, candidates must know how to implement "red teaming" exercises that intentionally attempt to provoke unsafe responses from the system. We also discuss the risks of unsafe recommendations in specialized fields like healthcare or industrial safety, where an AI error can lead to physical harm. Mitigation strategies involve the use of content filters, output sanitization, and strict temperature settings to limit the model's creative variance. Understanding how to measure these risks through automated benchmarks and human review is essential for maintaining trust and compliance. By prioritizing safety testing, organizations protect themselves from the severe reputational and legal consequences that arise when an AI system behaves in an unpredictable or dangerous manner. 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.
