Effective threat modeling techniques to identify and mitigate security vulnerabilities in generative AI applications

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Executive Summary

The deployment of generative AI applications has accelerated dramatically, with 78% of organizations now using AI in at least one business function as of 2025. This rapid adoption has exposed organizations to unprecedented security vulnerabilities that traditional threat modeling approaches fail to address adequately. Generative AI systems introduce unique attack vectors including prompt injection, data poisoning, model theft, and supply chain vulnerabilities that require specialized threat modeling techniques.

This whitepaper synthesizes the latest research and industry best practices to provide Chief Information Security Officers (CISOs) with a comprehensive framework for implementing effective threat modeling for generative AI applications. Key findings reveal that prompt injection attacks affect 86% of tested commercial LLM applications, while AI-related CVEs increased 1,025% in 2024. Organizations implementing structured threat modeling approaches report 40% faster incident response times and save an average of $1.76 million on breach costs.

The most effective techniques include the AWS four-stage methodology, the Models-As-Threat-Actors (MATA) approach, STRIDE-AI framework adaptation, and the OWASP Top 10 for LLMs. Success requires treating AI models as potential threat actors, implementing dynamic service level agreements, and establishing robust governance with CEO-level oversight. Organizations must move beyond traditional security approaches to address the non-deterministic nature of AI outputs, the complexity of multi-vendor environments, and the evolving regulatory landscape.

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