Threat modeling your generative AI workload to evaluate security risk

This article provides a practical approach for threat modeling generative AI workloads involving large language models (LLMs). It highlights the importance of identifying and mitigating security risks associated with these workloads, which can generate customized and non-deterministic outputs based on user prompts.

Specifically, the article covers:

• The four stages of threat modeling: 1) Understanding the business context and application architecture, 2) Identifying potential threats, 3) Defining mitigation strategies, and 4) Validating the effectiveness of the process and mitigations.
• Detailed guidance on each stage, including examples of deliverables like data flow diagrams, threat statements, attack steps, and mitigations.
• The use of resources like OWASP Top 10 for LLMs, MITRE ATLAS, and STRIDE to identify threats and controls.
• The importance of continuously testing and validating the identified mitigations and the threat modeling process itself.
• Conclusion emphasizing the value of threat modeling in maintaining a high security bar while adopting generative AI technologies.