LLMAC: A Global and Explainable Access Control Framework with Large Language Model

LLMAC is a new access control framework that uses Large Language Models to unify traditional access control methods (RBAC, ABAC, DAC) into a single comprehensive system. Using Mistral 7B trained on synthetic datasets representing complex real-world scenarios, the system achieved 98.5% accuracy, significantly outperforming traditional methods (RBAC: 14.5%, ABAC: 58.5%, DAC: 27.5%) while providing human-readable explanations for decisions.