How to Audit Your AI Models for Security in 2026

Your AI model might be your biggest security blind spot You spent weeks fine-tuning your model, shipping it to production, and watching adoption grow. But have you ever checked what happens when someone feeds it a carefully crafted prompt designed to extract training data? Most developers haven't — and that's exactly the kind of gap attackers are starting to exploit. Why traditional security audits miss AI-specific risks Classic application security focuses on SQL injection, XSS, and authentication flaws. These matter, but they don't cover the attack surface introduced by AI components. Prompt injection, training data leakage, model inversion attacks, and adversarial inputs are fundamentally different threat categories. OWASP released its Top 10 for LLM Applications, yet most teams still treat AI components as black boxes that "just work." The reality: if you're deploying AI without auditing it specifically for AI risks, you're flying blind. Step 1: Map your AI attack surface Before ru