Executable Governance for AI: Translating Policies into Rules Using LLMs

• URL: http://arxiv.org/abs/2512.04408v1
• Date: Thu, 04 Dec 2025 03:11:54 GMT
• Title: Executable Governance for AI: Translating Policies into Rules Using LLMs
• Authors: Gautam Varma Datla, Anudeep Vurity, Tejaswani Dash, Tazeem Ahmad, Mohd Adnan, Saima Rafi,
• Abstract summary: Policy-to-Tests (P2T) is a framework that converts natural policy documents into normalized, machine-readable rules.<n>To test the framework beyond a single policy, we apply it across general frameworks, sector guidance, and enterprise standards.<n>These AI-generated rules closely match strong human baselines on span-level and rule-level metrics, with robust inter-annotator agreement on the gold set.
• Score: 1.388831902854619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI policy guidance is predominantly written as prose, which practitioners must first convert into executable rules before frameworks can evaluate or enforce them. This manual step is slow, error-prone, difficult to scale, and often delays the use of safeguards in real-world deployments. To address this gap, we present Policy-to-Tests (P2T), a framework that converts natural-language policy documents into normalized, machine-readable rules. The framework comprises a pipeline and a compact domain-specific language (DSL) that encodes hazards, scope, conditions, exceptions, and required evidence, yielding a canonical representation of extracted rules. To test the framework beyond a single policy, we apply it across general frameworks, sector guidance, and enterprise standards, extracting obligation-bearing clauses and converting them into executable rules. These AI-generated rules closely match strong human baselines on span-level and rule-level metrics, with robust inter-annotator agreement on the gold set. To evaluate downstream behavioral and safety impact, we add HIPAA-derived safeguards to a generative agent and compare it with an otherwise identical agent without guardrails. An LLM-based judge, aligned with gold-standard criteria, measures violation rates and robustness to obfuscated and compositional prompts. Detailed results are provided in the appendix. We release the codebase, DSL, prompts, and rule sets as open-source resources to enable reproducible evaluation.

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