Episode 51 — Identify the AI threat landscape using realistic abuse cases (Task 5)
This episode teaches how to identify the AI threat landscape by focusing on realistic abuse cases instead of generic fear, because AAISM questions reward threat thinking that is tied to assets, workflows, and likely attacker goals. You will learn to build threat awareness around how AI systems are actually used, including data pipelines, model endpoints, prompts, integrations, and downstream business decisions. We walk through abuse patterns such as prompt injection to manipulate outputs, data exfiltration through prompts and logs, model theft through exposed endpoints, poisoning of training data sources, and misuse by insiders who have legitimate access but unsafe intent. Troubleshooting focuses on avoiding threat lists that are disconnected from your environment, and on documenting threats in a way that supports later risk assessment, control selection, and monitoring priorities. 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.
