Checklist

5 Ways AI Breaks Threat Modeling

Most engineering teams deploying AI in 2026 already have a security process. That process was not designed for AI. Here are the five specific ways, with what you need to add before you ship.

Download PDF →

PDF · 6 pages · Free · No sign-up required

What's Inside

• 1. Outputs Are Probabilistic, Not Deterministic
  You cannot unit-test a distribution with a single pass. Your test suite has no predictive power over behavior it did not observe.
• 2. The Attack Surface Includes Training Data
  You can audit source code and dependencies. You cannot audit a learning process that has already concluded.
• 3. Agents Take Actions, Not Just Produce Output
  A standalone LLM producing harmful text is a content moderation problem. An agent acting on that text is an operational security problem.
• 4. Prompt Injection Has No Equivalent Fix
  SQL injection was solved at the parser. Parameterized queries tell the database: this is code, this is data. Language models have no parser.
• 5. The Supply Chain Extends Beyond Code
  Traditional supply chain security has a defined scope: source code, dependencies, build artifacts, container images. AI adds datasets, model weights, MCP servers, and skill marketplaces.
• 6. What This Means for Your Process
  None of this means STRIDE is wrong. It means STRIDE is not sufficient.

Need more? Explore all free resources or get the next one delivered.

Browse All Resources →