Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs

As organizations increasingly deploy LLMs in sensitive domains such as legal, financial, and medical settings, ensuring alignment with internal organizational policies has become a priority. Existing content moderation frameworks remain largely confined to the safety domain and lack the robustness to capture nuanced organizational policies. LLM-as-a-judge and fine-tuning approaches, though flexible, introduce significant latency and training cost. To address these limitations, we frame policy violation detection as an out-of-distribution (OOD) problem in the model's activation space. We propose a training-free method that operates directly on the LLM internal representations, leveraging prior evidence that decision-relevant information is encoded within them. Inspired by whitening techniques, we apply a linear transformation to decorrelate and standardize the model's hidden activations, and use the Euclidean norm in this transformed space as a compliance score for detecting policy violations. Our method requires only the policy text and a small number of illustrative samples, making it lightweight and easily deployable. We extensively evaluate our method across multiple LLMs and challenging policy benchmarks, achieving 86.0% F1 score while outperforming fine-tuned baselines by up to 9.1 points and LLM-as-a-judge by 16 points, with significantly lower computational cost. Code is available at: https://github.com/FujitsuResearch/LLM-policy-violation-detection