Investigating the Influence of Artificial Intelligence on the Enhancement of Data Warehouse Efficiency and Security within Multi-Cloud Frameworks

This paper examines the impact of Artificial Intelligence (AI) on improving the efficiency and security of data warehouses operating within multi-cloud frameworks.As organizations increasingly adopt multi-cloud strategies, managing the complexity of data security and operational efficiency across different cloud environments becomes a critical challenge.To address these issues, we implemented machine learning techniques, including XG-Boost and anomaly detection algorithms, to monitor data flows and detect potential security threats in real time.By integrating AI-driven anomaly detection systems with AWS Macie, the study achieved a 95% success rate in identifying security vulnerabilities, while also improving data retrieval speeds by 20%.The multi-cloud architecture utilized Amazon Web Services (AWS), Microsoft Azure and Google Cloud to ensure redundancy and data availability, allowing the AI algorithms to process data across platforms without compromising performance.This paper demonstrates that AI can significantly enhance both the security and efficiency of data warehouses in a multi-cloud environment, providing an essential solution for businesses managing sensitive data across multiple cloud providers.The research highlights the potential for AI to address the growing concerns around data security and operational inefficiencies in cloud computing.