PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
For organizations needing to verify document compliance with specific policies, PolicyGuard provides a more inspectable and maintainable alternative to end-to-end LLM prompting.
PolicyGuard introduces a neuro-symbolic framework that converts organizational policies into executable review engines, enabling explicit, maintainable, and testable compliance review of documents. Evaluated on NDA compliance, it separates policy formalization, local document interpretation, and symbolic evaluation.
Policy-grounded document review requires determining whether a target document complies with organization-specific policies, guidelines, or playbooks. While large language models can assist with policy interpretation and document analysis, end-to-end prompting leaves the applied policy logic implicit, making compliance decisions difficult to inspect, update, and test. We present PolicyGuard, a neuro-symbolic framework for policy-grounded document compliance review. PolicyGuard converts organizational policy guidance into an executable review engine consisting of typed relational logic rules and atom-level extraction questions. During review, LLMs answer these local questions using retrieved document evidence, and a symbolic evaluator applies the formal rules to detect non-compliance. We instantiate and evaluate PolicyGuard on company-specific NDA compliance review, where contract clauses must be checked against organization-specific negotiation policies. By separating policy formalization, local document interpretation, and symbolic compliance evaluation, PolicyGuard makes document review more explicit, maintainable, and systematically testable.