PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Sameer Malik, Ayush Singh, Amar Prakash Azad
• Venue: arXiv: cs.AI
• Published: 2026-06-30
• Collected: 2026-07-01
• URL: http://arxiv.org/abs/2606.32004v1
• PDF: https://arxiv.org/pdf/2606.32004v1
• DOI: UNKNOWN

Plain-Language Brief

PolicyGuard is a framework that uses neuro-symbolic technology to help organizations ensure documents comply with their specific policies, guidelines, or playbooks. It separates policy formalization, local document interpretation, and symbolic compliance evaluation, making the review process more explicit, maintainable, and testable.

What the Document Says

• PolicyGuard is a neuro-symbolic framework for policy-grounded document compliance review.
• It converts organizational policy guidance into an executable review engine consisting of typed relational logic rules and atom-level extraction questions.
• During review, large language models (LLMs) answer these local questions using retrieved document evidence, and a symbolic evaluator applies the formal rules to detect non-compliance.
• PolicyGuard was instantiated and evaluated on company-specific NDA compliance review.

Key Concepts Mentioned

• PolicyGuard
• neuro-symbolic framework
• organizational policy
• document compliance review
• large language models (LLMs)
• typed relational logic rules
• atom-level extraction questions
• symbolic evaluator
• NDA compliance

Claims Made by the Document

• PolicyGuard makes document review more explicit, maintainable, and testable.

Evidence Used

• The text explains that PolicyGuard separates policy formalization, local document interpretation, and symbolic compliance evaluation.
• It mentions that this separation is designed to make the review process more explicit, maintainable, and testable.

Methods / Approach

• PolicyGuard uses a neuro-symbolic approach to convert organizational policy guidance into an executable review engine.
• LLMs are used to answer local questions about documents, and a symbolic evaluator applies formal rules to detect non-compliance.

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

It's unclear whether the method is scalable to large amounts of data or various types of policies. It's also unknown if the framework can handle complex, ambiguous, or contradictory policies.

Open Questions

• Can PolicyGuard scale to large amounts of data?
• How does it handle complex, ambiguous, or contradictory policies?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide information about the scalability of PolicyGuard or how it handles complex or contradictory policies.

Raw Abstract / Excerpt

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.