Context Secure AI For Role Based Access Control

Abstract: The project is introduced based on traditional role-based access control environments are being challenged in a cyber threat era due to the static nature of their permission systems, which do not merit any flexibility for dynamic contextual factors. In this context, the paper proposes a novel method providing an additional degree of integration of context-aware artificial intelligence (AI) within a role-based authentication system for enhanced security and flexibility. In the proposed method, AI dynamically applies access permission changes based on real-time contextual data, utilizing information such as user behaviour, location, or device particulars. The research shows the promising role of context-aware AI in reducing the setbacks of static authentication systems while opening avenues for the future of dynamic access control systems. The findings emphasize integrating AI with contextual data for maximizing cybersecurity in a connected ecosystem.

Keywords: Cyber Security, Role Based Access Control, Context Aware Mechanism, Machine Learning, Artificial Intelligence, Anomaly Detection