# Synthesizing Access Control Policies using Large Language Models

arXiv: https://arxiv.org/abs/2503.11573 · HTML: https://mlsanity.com/paper/2503.11573/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Built on by (most cited first, 6 of 6)

- [Say What You Mean: Natural Language Access Control with Large Language Models for Internet of Things](https://mlsanity.com/paper/2505.23835/) (2025)
- [AutoCedar: An Agentic Framework for Verifier-Guided Access Control Policy Synthesis](https://mlsanity.com/paper/2607.03656/) (2026)
- [LLMAC: A Global and Explainable Access Control Framework with Large Language Model](https://mlsanity.com/paper/2602.09392/) (2026)
- [LLM-Driven Auto Configuration for Transient IoT Device Collaboration](https://mlsanity.com/paper/2507.03064/) (2025)
- [Neurosymbolic Characterization for Reliable Access Control Policy Analysis](https://mlsanity.com/paper/2510.20692/) (2025)
- [RAISE: Reinforcing Access Control Policy Synthesis in LLMs via Symbolic Evaluation](https://mlsanity.com/paper/2609.33796/) (2026)

## Abstract (arXiv)

Cloud compute systems allow administrators to write access control policies that govern access to private data. While policies are written in convenient languages, such as AWS Identity and Access Management Policy Language, manually written policies often become complex and error prone. In this paper, we investigate whether and how well Large Language Models (LLMs) can be used to synthesize access control policies. Our investigation focuses on the task of taking an access control request specification and zero-shot prompting LLMs to synthesize a well-formed access control policy which correctly adheres to the request specification. We consider two scenarios, one which the request specification is given as a concrete list of requests to be allowed or denied, and another in which a natural language description is used to specify sets of requests to be allowed or denied. We then argue that for zero-shot prompting, more precise and structured prompts using a syntax based approach are necessary and experimentally show preliminary results validating our approach.