Why You Need to Red Team Your Enterprise AI

This post details the application of AI red teaming to enterprise AI systems, extending beyond model-level testing to encompass the entire system architecture including prompts, context retrieval, tool integration, agent orchestration, and rule sets. It introduces a quantitative approach to measuring system failures by differentiating between adversarial and ordinary user behavior, highlighting that ordinary users can trigger violations almost as frequently as skilled attackers. The post quantifies failure rates across different personas and system layers, using a case study of a global professional services firm to demonstrate the effectiveness of a cyclical test-patch-retest methodology against a defined harm taxonomy. It emphasizes the importance of a written harm taxonomy with specific risk levels and response modes for effective testing and risk management, and introduces the concept of 'packaging' attacks where seemingly harmless inputs can be combined to exploit system vulnerabilities. The post also highlights the challenge of irreversible actions in enterprise systems and the need to score attempted reach as well as success.