AI Security Engineer Path
Engineers who want to build the defenses that keep AI and LLM systems safe in production.
4 stepsHands-on labsCapstone + portfolioA named role
Assessed work, a capstone and a verified credential
What the path adds
Membership covers foundation reading. A path is where the work is assessed and the credential is earned.
- Marked work
- Your written submissions are assessed against a rubric, not ticked off.
- Cumulative assessment
- One exam across the whole path, not a quiz per course.
- Capstone
- A final piece of real work, reviewed.
- Portfolio artefact
- Something you can put in front of a hiring manager.
- Verified credential
- A Professional Path Credential with a public verification page.
By the end, you can
What this path makes you able to do.
- Threat model AI architectures and secure the model development lifecycle
- Build guardrails, adversarial robustness, and secure retrieval into AI systems
- Secure the ML pipeline: registries, CI/CD, and model provenance
A guided journey, not a pile of courses
The 4-step path.
- 1View course
AI Security Engineering Foundations
Secure ML lifecycle and threat modeling
- 2View course
Building AI Defenses
Guardrails, robustness, secure RAG
- 3View course
Securing the ML Pipeline
MLOps security and provenance
Capstone: engineer defenses for an AI system
A secured reference design with guardrails, adversarial hardening, and a secured ML pipeline
Every path is backed by
Hands-on labs. A final exam & capstone. A real role.
You finish with a portfolio you can show, a cumulative exam, and the skills to land the AI Security Engineer role.
Not sure this is the one? Take the free fit and aptitude check first: twelve short questions, instant answer.