AI-aware threat modeling and red teaming service for LLM applications
Offer a proactive security practice that threat models AI systems before code is written and then attacks them the way a real adversary would, adapting classic STRIDE-style threat modeling to AI-specific assets and trust boundaries.
online B2B Service / Consulting / Agency
From AI Engineer — AI Security Engineer Foundations + Certificate — Micah Silverman, Snyk at 42:27
Problem: Traditional reactive security finds vulnerabilities only after deployment (patch-and-repeat cycles like Log4Shell), and traditional threat modeling falls short for AI because behavior is non-deterministic and new assets like prompt templates, context windows, memory stores, model weights, RAG pipelines and MCP servers create new attack surfaces.
For: Teams building LLM applications, chatbots and agents — from MVP-stage projects to production deployments — plus security teams that already know classic appsec but not AI.
Products from this video
AI Security Engineer Foundations & Certificate Evo by Snyk Gandalf (Lakera) GitHub Actions gstack Nightshade OpenClaw Snyk Snyk Agent Scan Vulnerable AI Student Assistant
Examples
- An ATLAS threat-matrix study of OpenClaw generated 37 total threats including direct prompt injection and skills-based persistence