AI Security and Governance: Comprehensive Threat Modeling for Generative AI Applications

Artificial Intelligence systems, particularly Large Language Models (LLMs) and generative AI applications, introduce unique security and privacy challenges that require specialized threat modeling approaches. This report provides a comprehensive framework for AI security and governance, integrating multiple threat modeling methodologies to identify, assess, and mitigate risks. As AI adoption accelerates across industries, proper security governance becomes essential to protect both organizations and users from emerging threats.

Understanding AI Threat Modeling Approaches

Threat modeling for AI systems requires specialized frameworks that address the unique vulnerabilities and attack vectors introduced by machine learning models. Unlike traditional software, AI systems face distinct challenges related to training data, model architecture, and inference processes.

STRIDE-AI for Security Assessment

STRIDE-AI adapts the traditional STRIDE threat modeling framework specifically for Machine Learning (ML) assets. Originally developed by Microsoft, this approach maps potential ML failure modes to threats and violations of security properties1. The framework is conceptually…