CLOUD SECURITY AUTOMATION: ENHANCING DATA MONITORING IN A MULTI-CLOUD ECOSYSTEM

Abstract

As organizations increasingly adopt multi-cloud strategies, ensuring robust security and monitoring of cloud-based data becomes a pressing challenge. This paper proposes a cloud security automation framework that leverages machine learning and automation to enhance data monitoring in multi-cloud ecosystems. Our framework integrates security controls and monitoring tools across multiple cloud providers, enabling real-time threat detection, anomaly identification, and automated incident response. We demonstrate the effectiveness of our approach through a case study, showcasing significant improvements in security posture, reduced false positives, and enhanced compliance. Our research contributes to the development of cloud security automation, providing a scalable and adaptable solution for securing multi-cloud environments.