AI Security for Engineers
Learn to build, audit, and secure LLM-powered applications. From prompt injection to production monitoring.
LLM Application Architecture: What You're Actually Building
System prompts, context windows, tool calling, and the trust boundary problem — the architectural foundation for everything else in this course.
Prompt Injection in Practice: Testing Your Own Applications
Hands-on methodology for testing your LLM applications against prompt injection — with payloads, test frameworks, and evaluation metrics.
Securing the RAG Pipeline: A Practical Checklist
A systematic checklist for securing retrieval-augmented generation — from vector database access control to output filtering.
LLM Agent Security: Designing for Minimal Blast Radius
How to design LLM agent architectures that limit damage when (not if) prompt injection succeeds. Privilege separation, sandboxing, and tool scoping.
Monitoring Production LLM Systems: Observability and Anomaly Detection
Build observability into your LLM stack — from token-level logging to behavioral anomaly detection for prompt injection in production.
Capstone: Security Review for a Production LLM Application
Apply everything from this course to a realistic application scenario. Conduct a structured security review, identify weaknesses, and produce a remediation plan.
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