The paper submits a system for authentication and secure file management, a hybrid of security measures, and role-based access control (RBAC) to support data protection and user management. The research work utilizes various biometric authentication methods, behavioral analysis, and traditional credential verification to ensure strong user identification. Furthermore, the analysis is driven by the AI system that detects user behavior patterns and discovers deviations, strengthening the security protocols. The proposed AI File-Based Access Control feature is dynamic in the sense of changing access permissions according to factors such as user role, file sensitivity, and real-time risk assessment. This unique technique can make not only the data management process seamless but also cut down the threat level by granting only approved users access to confidential information. A comprehensive audit trail mechanism and reporting features are the backbone of the framework, making it possible to track when and who has accessed and modified files effectively. The customers have become able to handle their digital tokens in a secure location and have adhered to the standards required; this has been assured through the employment of up-to-date technologies.