Episode 52 — Assess AI threats by likelihood and impact, not hype and fear (Task 5)
This episode explains how to assess AI threats using likelihood and impact so your conclusions are defensible, which AAISM often tests by presenting dramatic scenarios and asking for a measured, risk-based response. You will learn how to estimate likelihood by looking at exposure, attacker effort, control strength, and detection capability, and how to estimate impact by considering data sensitivity, business criticality, regulatory exposure, and harm to users. We use examples like a public-facing model endpoint versus an internal tool, and a regulated dataset versus low-sensitivity content, to show how the same threat can have very different risk outcomes. Troubleshooting focuses on common errors, such as assuming worst-case impact without evidence, ignoring existing controls, and failing to explain why a threat is prioritized or deprioritized. 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.
