AI Security
Threat modeling, secrets, RBAC, and compliance for AI systems.
Maturity level: L2 — Can Build
Six perspectives on AI Security
Roadmap
Learn before shipping anything to enterprise customers.
Architecture
Security boundary around data, models, and tools.
Company
Growing requirement in FDE, platform, and LLMOps roles.
Projects
Security review checklist for every capstone.
Interview
AI-specific threat scenarios.
Career
Enterprise readiness.
What & Why
What: Security practices specific to ML/LLM systems: data leakage, prompt injection, access control.
Why: AI systems expose new attack surfaces — prompt injection, model theft, data exfiltration.
Build this
Threat model a RAG system and implement mitigations for top 3 risks.
Production reality
- ! API keys in logs
- ! Over-permissive IAM
- ! Shadow AI usage
Interview preparation
- Prompt injection mitigations
- Securing vector DB access
Connected skills
Explore AI Security in the interactive universe or train with live cohorts.
Want this AI Security learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
Prefer a structured live course? Join the MLOps · AIOps · LLMOps · Agentic AI masterclass →