LLMs show promise in analyzing cloud access control policies, but struggle with generation

A new research paper explores the application of large language models (LLMs) in the domain of access control policies for cloud computing. The study found that while LLMs can generate syntactically correct policies, they struggle with permissiveness issues, achieving a 45.8% accuracy for non-reasoning models and 93.7% for reasoning models in matching specified access controls. However, LLMs show promise when combined with symbolic approaches for analyzing and summarizing existing policies. AI

IMPACT LLMs show potential for improving the analysis and understanding of complex access control policies in cloud environments.

RANK_REASON Research paper published on arXiv detailing the effectiveness of LLMs for specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Access Control Policy Summarization
• Access Control Policy Synthesis
• Adarsh Vatsa
• arXiv
• cloud computing
• large-language models

• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →