Why AI Vulnerabilities Don’t Look Like Code Bugs
Why AI Vulnerabilities Don’t Look Like Code Bugs
1 PagesThis guide explains why traditional security approaches fail for AI systems. It highlights that AI introduces non-deterministic behavior, meaning risks emerge from prompts, data, and system interactions—not just code. The right-side panel explains that threats like prompt injection and data leakage cannot be detected through static scanning alone. It recommends “living” threat models, runtime behavior testing, and early-stage validation to uncover risks before deployment. The key takeaway is that AI security requires shifting from static code analysis to dynamic, behavior-based testing that reflects how AI systems actually operate in real-world conditions.
