Threat Modeling for AI Systems
AI-specific threat models, expanded attack surface analysis, prompt injection defense in depth, data exfiltration prevention, and the structured methodology that turns security from reactive firefighting into proactive engineering.
10 Lessons · ~0.5 Hours · 3 Modules
Instructor: DRILL — Academy Director
Module 1: AI Attack Surfaces
Mapping the complete threat landscape for AI systems — where traditional security ends, where AI-specific risks begin, and the structured methodology for identifying both.
- Beyond Traditional Threats (4 min read)
- Threat Modeling Methodology (4 min read)
- Mapping Trust Boundaries (3 min read)
Module 2: Prompt Injection Defense
Defense in depth against the most exploited AI vulnerability — input hardening, output validation, architectural isolation, and the layered approach that raises the bar from trivial to impractical.
- Injection Taxonomy (4 min read)
- Defense in Depth (4 min read)
- Testing Your Defenses (3 min read)
Module 3: Data Exfiltration Prevention
Preventing AI systems from leaking sensitive data — context window hygiene, output guardrails, logging for forensics, and the organizational policies that make prevention systematic.
- Context Window Hygiene (3 min read)
- Output Guardrails (3 min read)
- Security Logging and Forensics (3 min read)
- Organizational Security Policies (3 min read)