Episode 82 — Understand data poisoning, evasion, and model theft in plain language (Domain 2F)
This episode breaks down three high-yield AI attack categories—data poisoning, evasion, and model theft—in plain language so you can recognize them in AAIA scenarios and select realistic controls. You’ll learn how poisoning alters training data or labels so the model learns the wrong patterns, how evasion manipulates inputs at inference time to trick outputs without changing the model, and how model theft targets the model artifact or recreates it through repeated queries. We’ll connect each attack type to audit implications: what controls reduce exposure, what monitoring detects abnormal behavior, and what evidence proves the organization can respond. You’ll also learn common exam traps, such as confusing poisoning with drift, or treating model theft as just “data loss” without addressing API abuse and query logging. By the end, you should be able to match the threat to the right prevention and detection controls, expressed in auditable evidence terms. 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.
