PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines

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.