Episode 4 — Map the AI Landscape for Security: ML, Deep Learning, and Generative Systems

SecAI+ expects you to speak clearly about AI system types and where security risk shows up, so this episode builds a practical map of machine learning, deep learning, and generative systems from a defender’s point of view. You will learn how ML pipelines differ from traditional software pipelines, why deep learning shifts risk toward data quality and model behavior rather than deterministic logic, and how generative systems introduce unique exposure through prompts, tools, and output handling. We will connect each system type to security-relevant assets like training data, embeddings, weights, and inference endpoints, then discuss what can go wrong at each step, from poisoned inputs and weak access control to leakage through outputs and logs. You will also practice describing these systems in exam-ready language that is accurate but not overly academic, using examples like classification for fraud, clustering for anomaly discovery, and LLM-based assistants for triage or coding. 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.