Prose2Policy (P2P): A Practical LLM Pipeline for Translating Natural-Language Access Policies into Executable Rego

2026-03-18 · Source: Apple Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Advanced, quick

Summary

Prose2Policy (P2P) is a new LLM-based tool designed to convert natural-language access control policies (NLACPs) into executable Rego code, which is used by Open Policy Agent (OPA). This tool features a comprehensive, modular pipeline that includes policy detection, component extraction, schema validation, linting, compilation, and automatic test generation and execution. P2P aims to enhance deployment reliability and auditability by bridging the gap between human-readable requirements and machine-enforceable policy-as-code (PaC). Evaluation on the ACRE dataset showed a 95.3% compile rate for accepted policies, an 82.2% positive-test pass rate, and a 98.9% negative-test pass rate, confirming its ability to produce robust and consistent Rego policies for Zero Trust and compliance-focused settings.

Key takeaway

For security architects and compliance officers implementing Zero Trust frameworks, Prose2Policy offers a robust solution to automate the conversion of natural language access policies into auditable, executable Rego code. You can significantly reduce manual effort and error rates in policy deployment, ensuring consistent enforcement and simplifying compliance audits. Consider integrating P2P to streamline your policy-as-code initiatives.

Key insights

Prose2Policy translates natural language access policies into executable Rego code with high reliability and test coverage.

Principles

• Bridge human language to machine-enforceable code.
• Emphasize deployment reliability and auditability.

Method

P2P employs a modular pipeline: policy detection, component extraction, schema validation, linting, compilation, and automatic test generation/execution.

In practice

• Automate NLACP conversion to Rego code.
• Generate tests for access control policies.

Topics

• Prose2Policy (P2P)
• Large Language Models
• Access Control Policies
• Rego Policy Language
• Open Policy Agent

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Researcher, AI Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Apple Machine Learning Research.