A Sophisticated Authentication Coupled with A Multi Modal Security Integration and Role based Access Control for Secure File Management

With the increasing complexity of cyber threats, traditional single-factor authentication systems have become highly vulnerable to attacks such as phishing, brute force, and social engineering. This research proposes an advanced multi- modal authentication system that integrates face recognition, voice recognition, and image grid selection, coupled with randomized challenge tasks to prevent unauthorized access. The system also incorporates Role-Based Access Control (RBAC) for secure file management, ensuring that sensitive data is accessible only to authorized users based on predefined roles. The integration of audit trails provides full traceability of user activities, enhancing accountability and compliance. The proposed system was evaluated for authentication accuracy, processing time, and security efficiency. Results indicate that face recognition achieved 95.6% accuracy, voice recognition 89.3%, and image grid selection 100% accuracy. The system also successfully restricted unauthorized access through RBAC, demonstrating improved security, reduced false positives (0%), and efficient file access management. This research presents a scalable and adaptable security framework for organizations seeking a robust authentication system with enhanced data protection and user accountability.