Follow the AI Security & Red-Teaming learning path on AI Builders Network - a free, structured roadmap with curated resources and step-by-step progress tracking.
This path is for security engineers extending their practice to AI systems, and for AI builders who own systems handling untrusted input or sensitive data. AI security is the fastest-emerging specialization in the field: every company shipping LLM features needs someone who can think like an attacker, and almost none have one.
By the end you'll be able to: Threat-model LLM systems using OWASP LLM Top 10 and MITRE ATLAS; Execute and defend against direct and indirect prompt injection; Design agents with least privilege, approval gates, and sandboxing; Enforce document-level access control in RAG retrieval; Run structured red-team exercises and ship findings as regression tests; Map controls to NIST AI RMF and EU AI Act requirements.
Prompt injection is when attacker-controlled text, in a user message or in content the system retrieves, overrides the developer's instructions. It can't be fully patched because LLMs process instructions and data in the same channel. Defense means layered mitigation: input handling, least-privilege tools, output validation, and human gates on consequential actions.
Traditional pentesting probes deterministic systems where a vulnerability reproduces reliably. AI red-teaming attacks probabilistic behavior, the same attack may work one run in five, so it requires automated attack suites, statistical evaluation, and regression testing. The deliverable shifts from a list of CVEs to an evolving adversarial test suite.
Yes: it sits at the intersection of two talent shortages. Regulation (EU AI Act enforcement, NIST AI RMF adoption) is forcing companies to take AI risk formally, while agent deployments multiply the attack surface. Security engineers who can also build with LLMs command a significant premium over either skill alone.