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

The researchers have developed a framework called PolicyGuard to ensure documents comply with an organization's specific policies or guidelines. Instead of relying on large language models for end-to-end prompting, PolicyGuard separates policy formalization, local document interpretation, and symbolic compliance evaluation, making it easier to inspect, update, and test the applied policy logic.

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, LLMs answer local questions using retrieved document evidence.
• A symbolic evaluator applies formal rules to detect non-compliance.
• PolicyGuard was instantiated and evaluated on company-specific NDA compliance review.
• By separating policy formalization, local document interpretation, and symbolic compliance evaluation, PolicyGuard makes document review more explicit, maintainable, and systematically testable.

Key Concepts Mentioned

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

Claims Made by the Document

• PolicyGuard provides a more explicit, maintainable, and systematically testable method for document review.
• It separates policy formalization, local document interpretation, and symbolic compliance evaluation.

Evidence Used

• The researchers describe the development and evaluation of PolicyGuard in their work.

Methods / Approach

• PolicyGuard uses a combination of neuro-symbolic methods for policy formalization, local document interpretation, and symbolic compliance evaluation.

Limitations Stated by the Authors

• Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

• The effectiveness and scalability of PolicyGuard for various types of policies or documents beyond NDA compliance may not be fully understood.
• The impact of different LLMs on PolicyGuard's performance is unknown.

Open Questions

• What are the potential improvements in PolicyGuard's performance with different LLMs?
• How well does PolicyGuard perform for various types of policies or documents beyond NDA compliance?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The text focuses on the development and evaluation of PolicyGuard for company-specific NDA compliance review, so the performance and applicability to other policy areas remain unclear.

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.