AI-Driven Strategies for Ensuring Data Reliability in Multi-Cloud Ecosystems

Abstract

As multi-cloud ecosystems continue to gain traction in organizations for their flexibility and scalability, ensuring data reliability across diverse cloud platforms has become a critical challenge. This research explores AI-driven strategies to enhance data reliability within multi-cloud environments, focusing on techniques that address data consistency, availability, fault tolerance, and recovery. By leveraging AI technologies such as anomaly detection, predictive analytic, and automated fault tolerance, the study highlights how AI can monitor, predict, and mitigate data disruptions in real-time. Through an analysis of case studies and industry applications, this paper demonstrates the effectiveness of AI in preventing data failures and optimizing data redundancy across multiple cloud infrastructures. Despite the promising advantages, challenges such as integration complexities, data security concerns, and resource constraints are discussed, along with future directions for AI innovation in multi-cloud data management. The findings underscore the transformational potential of AI in ensuring robust data reliability in dynamic, multi-cloud environments.