AI-Powered Role-Based Access Control (RBAC): Automating Policy Enforcement in Enterprise Environments

Abstract

The growing complexity of modern cloud infrastructures has made traditional methods of managing access control increasingly inadequate. Role-Based Access Control (RBAC) has been a widely adopted method to manage user permissions; however, as organizations scale, managing these roles manually becomes more difficult, leading to security vulnerabilities and operational inefficiencies. To address these challenges, AI-powered RBAC systems are being integrated into enterprise environments. By leveraging machine learning algorithms and advanced analytics, these AI-driven systems automate role assignments, continuously monitor user behavior, and dynamically adjust access controls in real-time. This approach improves policy enforcement, reduces the risk of privilege creep, enhances security, and ensures compliance with regulatory standards such as GDPR and HIPAA. AI-powered RBAC not only optimizes access management but also adapts to evolving business needs, ensuring secure and efficient access to critical resources. This article explores the methodology, benefits, and real-world applications of AI-powered RBAC systems and examines the future trends in access control management driven by AI.