Threat Modeling for AI Applications

Understanding Threat Modeling for AI

Modern software changes faster than traditional review cycles. A useful security program has to answer four questions quickly: what is exploitable, who owns the fix, did the fix work, and what proof can we show later?

Threat Modeling for AI matters because security work often stalls between detection and closure. Continuous validation keeps the work close to the code, the owners, and the evidence buyers or auditors will ask for.

The best security programs do not stop at finding risk. They make closure easy to prove.

Implementation Notes

Start with one narrow workflow. Pick the application, control, or service where unresolved findings create the most drag, then wire validation and proof around that path.

// AI-specific threat categories
const aiThreats = {
  dataPoison: { likelihood: "medium", impact: "critical" },
  modelExtraction: { likelihood: "high", impact: "high" },
  adversarialInput: { likelihood: "high", impact: "critical" },
  privacyLeakage: { likelihood: "medium", impact: "high" }
};
// Traditional STRIDE misses these entirely

The goal is not more dashboards. The goal is a shorter path from signal to fix to evidence.