Abstract

As the adoption of cloud computing continues to increase, securing cloud environments has become an ever-growing concern. Traditional security models struggle to keep up with the evolving nature of cyber threats, making it essential for organizations to explore innovative approaches. This paper explores how artificial intelligence (AI) and machine learning (ML) can enhance cloud security by automating threat detection, response, and mitigation in real-time. Through the application of advanced ML algorithms, AI-driven security systems can identify and predict security incidents, classify threats, and adapt to new attack strategies. The paper examines the various ML techniques, such as anomaly detection, supervised learning, and deep learning, used to enhance cloud security. It also explores the challenges, benefits, and potential future directions of AI-driven security in cloud computing. Case studies from industry leaders demonstrate the impact of these technologies in improving the robustness and efficiency of cloud security frameworks.