Data Reliability Assurance in AI-Driven Cloud Architectures: A Novel Framework

Abstract

In the rapidly evolving landscape of cloud computing, ensuring data reliability within AI-driven architectures has become paramount. This paper introduces a novel framework designed to enhance data reliability assurance in cloud environments powered by artificial intelligence. The proposed framework integrates advanced AI techniques with traditional cloud reliability mechanisms to address challenges related to fault detection, data integrity, and system resilience. Key components of the framework include predictive analytics for proactive fault management, anomaly detection for data integrity verification, and automated recovery processes to minimize downtime. Through a series of experimental evaluations, the framework's performance is compared against conventional methods, demonstrating significant improvements in fault detection accuracy, data integrity maintenance, and system uptime. The results indicate that the AI-driven framework not only enhances the reliability of cloud systems but also optimizes resource utilization and operational efficiency. This study provides valuable insights into the application of AI in cloud data reliability and proposes a robust approach for addressing the evolving demands of modern cloud architectures.