Episode 46 — AI Failure Risks: Data Loss, Bias, Explainability, Hallucinations, and Ethics
This episode explains AI risks that can occur even when there is no traditional attacker. Data loss may happen when sensitive information is entered into tools, retained improperly, or exposed through connected systems. Bias can lead to unfair or unreliable outcomes, while poor explainability makes it difficult to understand why a system produced a result. Hallucinations occur when AI generates incorrect or unsupported information with confidence, creating risk when users treat output as authoritative. Ethical concerns may involve privacy, accountability, discrimination, transparency, and misuse. For Security+ scenarios, students should recognize that AI governance, human review, data handling rules, approved use cases, and monitoring are important security controls. 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. And dont forget Cyberauthor.me for the companion study guide and flash cards!
