ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM125
doi: 10.71141/30485037/ICETETI-MSM125

Combining AI and RBAC for Preventing Data Loss in Cloud Backup Systems

Amelia Brown, Syed Ali Fathima

Citation:
Amelia Brown, Syed Ali Fathima, "Combining AI and RBAC for Preventing Data Loss in Cloud Backup Systems", International Journal of Multidisciplinary on Science and Management, pp. 196-202, 2024.

Abstract:
Data security in cloud backup systems has become a critical challenge due to the increasing volume of sensitive data being stored remotely. Role-Based Access Control (RBAC) has been widely adopted to restrict unauthorized access, but its static nature often fails to detect advanced threats. Integrating Artificial Intelligence (AI) with RBAC enhances security by dynamically adapting access controls based on real-time analysis of user behavior. This paper explores the synergy between AI and RBAC, presenting a novel framework to prevent data loss in cloud environments. The proposed system utilizes machine learning (ML) models for anomaly detection, predictive analytics, and automated policy adjustments. The study also discusses real-world applications, performance evaluation, and security implications. The results indicate that AI-enhanced RBAC significantly reduces unauthorized access incidents while maintaining system efficiency. Future research directions and challenges are also highlighted.

Keywords: AI in Cloud Security, Role-Based Access Control (RBAC), Data Loss Prevention, Machine Learning, Anomaly Detection, Cloud Backup Systems.

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