Your AI Safety Tests Are Lying to You

Effective AI risk evaluation requires a holistic, high-dimensional approach rather than relying on simplistic, low-dimensional tests. Current industry practices often fail because they utilize single-model "LLM as a judge" frameworks, which produce inaccurate results and hinder deployment speed. Organizations must implement granular sub-provisions that allow for the precise identification and remediation of specific risks, such as deceptive practices or hidden subscription fees. This process necessitates deep subject matter expertise, specifically "legal engineering," to bridge the gap between technical performance and regulatory compliance. Furthermore, the shift toward open-weights models offers a more efficient, customizable alternative to general-purpose frontier models for targeted risk assessments. Maintaining these systems requires continuous, automated evaluation to address model drift and evolving regulatory landscapes, ensuring that AI systems remain safe and trustworthy throughout their entire operational lifecycle.