Leveraging Artificial Intelligence for Enhanced Data Protection: A Comprehensive Review of Cloud Security amid Emerging threats

Abstract

Cloud computing offers scalable, adaptable, and affordable solutions that spur innovation across multiple industries, it has fundamentally changed how industries function. However, with this widespread adoption comes the growing challenge of protecting sensitive data, especially as more sophisticated cyberattacks become common. Advanced Persistent challenges (APTs), insider assaults, data breaches, and Distributed Denial of Service (DDoS) attacks are just a few of the challenges that modern cloud environments must contend with. These threats highlight flaws in conventional security paradigmsThe integration of cutting-edge technologies like artificial intelligence (AI) and machine learning (ML) into cloud security is becoming more and more important in response to these issues. These technologies are proving to be effective instruments for increasing prediction accuracy, automating threat detection, and enabling real-time encryption protocol modifications. We can improve cloud security by utilising AI and ML to detect anomalies, find zero-day vulnerabilities, and employ predictive models that assist in addressing problems before they become more serious. A thorough analysis of the present uses of AI and ML in cloud security is provided in this work including how these tools are being used to enhance traditional methods like encryption and access control. It also evaluates the latest research in AI-driven threat detection, behavioral analysis, and adaptive encryption. Additionally, we highlight critical gaps in current AI/ML security frameworks, particularly in terms of scalability, false positive rates, and the challenges of real-time implementation. The primary goals of this review are threefold: first, to systematically analyze the emerging threats to cloud data security; second, to propose the development of more adaptive and robust algorithms that use AI and ML to enhance cloud protection; and third, to present a framework for integrating these algorithms into existing cloud security infrastructures. Ultimately, we hope this review contributes valuable insights that can shape the future of AI/ML-driven cloud security, helping to tackle the evolving challenges that come with modern cloud computing.