AI Security Architecture: Threat Model LLMs, Agents & RAG

About this course

Almost every AI incident so far has been an architecture failure wearing a model costume. This course is about AI security architecture: reading AI systems, naming every trust boundary, and finding the design flaws before anyone writes code.

Not a jailbreak and not a clever prompt. A retrieval system that never checked who was asking. A tool wired to a service account. A data flow nobody mapped.

You already threat-model. This is the part where your mental model runs out.

You can take apart a web application or a cloud account. Then three teams start shipping things with models in them, nobody draws you a diagram, and you are the one asked whether it is safe. The vocabulary is new - embeddings, agents, RAG, context windows - and it hides the fact that the security questions underneath are ones you already know how to ask.

What you will be able to doDraw the seven layers of any AI system on sight: interface, orchestration, retrieval, model, tools, data and identity.

Mark every trust boundary, name what crosses it, and say which control belongs at that crossing.

Run a five-question interrogation that surfaces most AI design flaws in ten minutes, even when the team cannot tell you which model they are using.

Review an AI agent in twenty minutes by reading its tool definitions as a security boundary specification.

Write findings as architecture rather than anecdote, so the engineering team can action them.

Sign off, or refuse, with reasons mapped to the OWASP LLM Top 10, NIST AI RMF and MITRE ATLAS.The seven sections1. Why architecture is the boundary. What AI changes about threat modelling, and the five questions you will use for therest of the course.2. The modern AI stack, end to end. Seven layers, one request traced through eleven hops, the three deployment shapes you will actually meet, and the seven places your data lands.3.

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