Today's business organizations need access control systems that can handle complex, changing security requirements that go beyond what traditional methods can manage. Current approaches, such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Discretionary Access Control (DAC), were designed for specific purposes. They cannot effectively manage the dynamic, situation-dependent workflows that modern systems require. In this research, we introduce LLMAC, a new unified approach using Large Language Models (LLMs) to combine these different access control methods into one comprehensive, understandable system. We used an extensive synthetic dataset that represents complex real-world scenarios, including policies for ownership verification, version management, workflow processes, and dynamic role separation. Using Mistral 7B, our trained LLM model achieved outstanding results with 98.5% accuracy, significantly outperforming traditional methods (RBAC: 14.5%, ABAC: 58.5%, DAC: 27.5%) while providing clear, human readable explanations for each decision. Performance testing shows that the system can be practically deployed with reasonable response times and computing resources.

翻译：当今的商业组织需要能够处理复杂多变安全需求的访问控制系统，这些需求已超出传统方法的管理能力。当前方法，如基于角色的访问控制（RBAC）、基于属性的访问控制（ABAC）和自主访问控制（DAC），均针对特定目的设计，无法有效管理现代系统所需的动态、情境依赖的工作流程。本研究提出LLMAC，这是一种利用大型语言模型（LLMs）的新型统一方法，将不同的访问控制方法整合为一个全面、可理解的系统。我们使用了一个代表复杂现实场景的广泛合成数据集，包括所有权验证、版本管理、工作流程和动态角色分离等策略。采用Mistral 7B训练的LLM模型取得了优异成果，准确率达到98.5%，显著优于传统方法（RBAC：14.5%，ABAC：58.5%，DAC：27.5%），同时为每个决策提供清晰、人类可读的解释。性能测试表明，该系统可在合理的响应时间和计算资源下实际部署。