Scaling Responsible Generative AI: Automating Red Teaming of LLM Applications

Large Language Models (LLMs) present both significant business potential and substantial risks. This paper addresses the critical need for robust and scalable red teaming processes to identify and mitigate risks before LLM solution deployment. First, we define a comprehensive set of 48 LLM-associated risks reflecting emerging AI threats. Additionally, we introduce an automated adversarial prompt generation pipeline involving five types of generators that cover diverse AI risks at varying complexity levels. Finally, we implement a LLM-as-a-judge evaluation system to streamline testing. When applied to red-team two finance-based LLM applications, our approach achieved perfect recall in identifying failed outputs while reducing manual evaluation by over 90%. The developed features halved the time required for red teaming exercises, enhancing scalability and thoroughness while maintaining effectiveness. This work enhances LLM safety, accelerates deployment, and adds business value through improved risk management and responsible AI use.