Episode 5 — Protect AI Integrity Privacy and Availability Against Model Poisoning Risks
This episode examines how AI systems can be weakened when training data, prompts, retrieval sources, or supporting workflows are manipulated in ways that distort outputs or expose sensitive information. For the exam, it is useful to treat AI risk through familiar security principles by asking what threatens integrity, what could leak private data, and what might reduce the system’s reliability or safe use. We will look at examples such as poisoned datasets, unsafe prompt handling, and overexposed model workspaces, along with practical safeguards like validation, access control, monitoring, and change discipline that support both exam answers and real deployments. 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!
