Using language models and machine learning inside a security team without creating a new incident class.
The other half of AI security: not defending the AI, but using it. Security teams are drowning in alerts and short of people, which makes automation attractive — and makes the failure modes expensive.
This course covers where AI genuinely helps a security team, where it does not, how to evaluate detection honestly, and how to respond when the AI system itself is the thing that went wrong.
4 lessons · 1 hr
Summarising, enriching and drafting, versus deciding — and which is which.
Designing the pipeline so a confident wrong answer costs a minute, not an incident.
Why a 99%-accurate detector can still be useless, and what to build instead.
Responding to prompt injection, leakage and agent misbehaviour — and what evidence you need.
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What actually changes when a model joins a system, the attacks that follow from it, and how to threat model an AI feature before you ship it.
How injection works, why filtering fails, and the design patterns that actually contain it.
Access control across a retrieval index, the confused deputy problem in tool use, and keeping an agent inside its blast radius.