Udemy – Threat Modeling for GenAI: LLM Security & Risk Scoring

Generative AI applications present entirely new attack surfaces that traditional application security frameworks fail to address.

Who this course is for:

• Application Security Engineers designing controls for GenAI deployments.
• Cybersecurity Architects evaluating risk in RAG and Agentic systems.
• AI/ML Engineers building LLM-powered applications in production.
• CISOs and IT Risk Managers needing clear residual risk evaluation metrics.
• Penetration Testers transitioning into AI red teaming and security reviews.

What you will learn:

• Conduct end-to-end threat modeling tailored specifically to Generative AI architectures
• Identify and prevent direct and indirect prompt injection attacks
• Secure Retrieval-Augmented Generation pipelines and vector storage layers
• Apply OWASP Top 10 for LLMs and STRIDE-AI to production environments
• Implement context-aware input/output guardrails and model firewalls
• Calculate risk scores for non-deterministic model failure modes using DREAD-AI
• Design secure trust boundaries between LLMs, APIs, and autonomous tools
• Quantify residual risk and create executive-ready compliance documentation