arXiv:2606.32004v1 Announce Type: cross Abstract: 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 en
The proliferation of complex AI systems and organizational policies necessitates robust, transparent, and auditable compliance mechanisms.
This development addresses a critical need for transparent and explainable AI in regulated environments, bridging the gap between LLM capabilities and practical institutional requirements.
Policy compliance, previously a manual and opaque process, can now be augmented by an auditable neuro-symbolic engine, offering greater consistency and explainability.
- · Regulatory compliance software providers
- · Large enterprises
- · Legal and audit sectors
- · AI governance platforms
- · Manual policy review services
- · Companies with opaque compliance processes
Organizations can more efficiently and reliably ensure document compliance with internal and external policies.
The demand for expertise in formalizing policies into neuro-symbolic logic increases, fostering a new specialized skill set.
Enhanced compliance automation could reduce legal and regulatory risks, potentially leading to faster adoption of AI in highly regulated industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG