将自然语言权限规则自动转为可执行代码，提升安全策略部署效率
Prose2Policy (P2P): A Practical LLM Pipeline for Translating Natural-Language Access Policies into Executable Rego
- 分步处理自然语言策略，生成符合规范的Rego代码
- 在ACRE数据集上编译成功率95.3%，测试通过率达82.2%以上
- 适合零信任与合规场景，支持自动化审计与验证
Prose2Policy（P2P）是一个基于大模型的实用工具，可将自然语言访问控制策略（NLACP）自动转化为可执行的Rego代码（Open Policy Agent, OPA的策略语言）。该工具提供模块化端到端流程，涵盖策略检测、组件提取、模式验证、代码检查、编译、自动测试生成与执行。P2P旨在弥合人类可读权限需求与机器可执行的策略即代码（PaC）之间的差距，强调部署可靠性与可审计性。我们在ACRE数据集上评估了该方法，结果显示，对于被接受的策略，95.3%可成功编译；自动测试中，正向测试通过率为82.2%，负向测试通过率为98.9%。结果表明，P2P生成的Rego策略在语法上稳健、行为上一致，适用于零信任和合规驱动环境。
原文摘要 · Abstract (English)
Prose2Policy (P2P) is a LLM-based practical tool that translates natural-language access control policies (NLACPs) into executable Rego code (the policy language of Open Policy Agent, OPA). It provides a modular, end-to-end pipeline that performs policy detection, component extraction, schema validation, linting, compilation, automatic test generation and execution. Prose2Policy is designed to bridge the gap between human-readable access requirements and machine-enforceable policy-as-code (PaC) while emphasizing deployment reliability and auditability. We evaluated Prose2Policy on the ACRE dataset and demonstrated a 95.3\% compile rate for accepted policies, with automated testing achieving a 82.2\% positive-test pass rate and a 98.9\% negative-test pass rate. These results indicate that Prose2Policy produces syntactically robust and behaviorally consistent Rego policies suitable for Zero Trust and compliance-driven environments.
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