Structured Decomposition for Reliable LLM-Generated Access Control Policies
An LLM system translates natural-language access control policies into executable Rego code with 50.3% correctness rate.
Researchers present an LLM-based system that translates natural-language access control policies into executable Rego code for Open Policy Agent. The system uses structured decomposition and schema-aware validation to achieve a 50.3% end-to-end correctness rate, a 3.3x improvement over a direct single-prompt baseline. It demonstrates significant improvements in generating correct deny semantics and ownership conditions.
- The system translates natural-language access control policies into executable Rego code for Open Policy Agent.
- It achieves 50.3% end-to-end policy correctness, a 3.3x improvement over baseline.
- Structured decomposition and schema-aware validation significantly improve LLM-generated policy reliability.
From the original article
1 min read at arxiv.orgThis paper presents an LLM-based system that translates natural-language access control policies (NLACPs) into executable Rego code for Open Policy Agent (OPA). It provides a modular, end-to-end pipeline for policy detection, component extraction, schema validation, linting, compilation, and automated test generation and execution.
By Vatsal Gupta · arxiv.org
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