Data Leakage Detection in Cloud Computing Environment

Insider threats pose critical security threats through the possession of privileged and access to valuable information and resources. Data leakage is one of the significant challenges faced in cloud computing environments, emphasising the criticality of data protection across various sectors such as education, finance, healthcare, and more. Cloud computing offers numerous advantages, including reliable data storage and transfer, yet it also presents significant risks, including data breaches, server failures, and malicious insider threats. This paper's primary objective is to evaluate the effectiveness of data leakage detection methods, particularly focusing on insider threats. Through the exploration of machine learning models and anomaly detection techniques, the study seeks to identify the most effective strategies for mitigating unauthorised data exposure. The research encom-passes a comprehensive analysis of current security measures, identification of vulnerabilities, and the proposition of enhanced detection methods. By examining real-world data leakage incidents and employing advanced detection tools, the findings aim to contribute to the development of more robust data protection frameworks. This research is poised to offer valuable insights and recommendations that will aid organisations in leveraging cloud services securely and efficiently, thereby enhancing their overall data security posture. The study not only underscores the importance of the CIA triad-Confidentiality, Integrity, and Availability-but also highlights the significance of innovative detection methods such as machine learning algorithms and behavioural analytics in safeguarding sensitive information in the ever-evolving landscape of cloud computing.