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Module 0 — Orientation & Environment Setup

Duration: ~1.5 hrs · Status: Mandatory (gates access to Module 1) Lessons: 3 (2 theory · 1 lab) · Quiz: none (no quiz on orientation) Framework coverage: course-wide framing — full OWASP / ATLAS / NIST / EU AI Act crosswalk introduced

Module outcomes

By the end of this module, the learner can: 1. State the course promise, structure, and how to navigate theory vs lab lessons. 2. Articulate why AI security is a distinct discipline in 2026 — not a relabeling of AppSec or MLOps. 3. Successfully run the course lab environment and verify all prerequisites (LLM access, Python toolchain, container runtime, course repo).

Lesson list

  • L0.1 — Welcome & how this course works (Theory, ~10 min, mandatory)
  • L0.2 — The AI security landscape in 2026 (Theory, ~25 min, mandatory)
  • L0.3 — Environment setup & sanity check (Lab, ~45 min, mandatory)

Why this module exists

Two things kill course completion rates: vague positioning ("what am I even learning?") and Day-1 environment failures ("I can't get the lab to start"). Module 0 closes both. The lab is mandatory and gating because a learner who hasn't proved their environment works will get stuck in Module 1's first lab and churn.

What's next

Module 1 — AI/ML Foundations for Security Engineers. We cover just enough ML/LLM internals to attack and defend them, with two mandatory labs (run an LLM locally; build a tiny RAG system you'll attack in Module 3).