Module 0 — Summary

Type: Theory · Duration: ~3 min · Status: Mandatory

Video script

[SLIDE 1 — Module 0 wrap]

Quick recap of Module 0. You now have a working definition of AI Security Engineering as a discipline, you know why it became its own role between 2022 and 2026, you've seen the four frameworks we'll map every lesson against — OWASP Top 10 for LLMs, MITRE ATLAS, NIST AI RMF, EU AI Act — and you have a verified lab environment that you can hammer for the next thirty-plus hours.

[SLIDE 2 — What's next]

Module 1 is AI/ML Foundations for Security Engineers. We're going to spend three and a half hours on just enough machine-learning internals to attack and defend these systems competently. If you came in from the security side, this is where you build the mental model you'll need. If you came in from the ML side, you'll be ahead — feel free to skim the primer lessons and dive into the threat-modeling material in Module 2.

Two mandatory labs in Module 1: you'll run a local LLM and inspect what's actually inside a model card, then you'll build a tiny RAG system from scratch. That RAG system isn't throwaway — you'll attack it in Module 3 and defend it in Module 7. Build it carefully.

See you in Module 1.

Slide outline

1. Module 0 wrap — three-checkmark recap (Discipline · Frameworks · Environment).
2. What's next — Module 1 teaser: lesson list, two mandatory labs called out, callout that the Module 1 RAG app reappears in Modules 3 and 7.

Production notes

• Recording time: 2–3 min raw. Short on purpose; module summaries are bridges, not lectures.
• Reusable visual element: end every module-summary slide with the same "Module N → Module N+1" pointer so learners get a navigation cue.