Achieving Regulatory Compliance in Cloud Computing through ML

Abstract

In today's dynamic cloud computing landscape, achieving regulatory compliance presents significant challenges for organizations due to evolving security threats and complex legal requirements. This research paper explores the role of machine learning (ML) in enhancing regulatory compliance within cloud environments. The study reviews current regulatory frameworks, compliance challenges, and the impact of non-compliance on organizations. By analysing real-world case studies, including Microsoft Azure Sentinel and Google Cloud's Data Loss Prevention (DLP) API, this paper demonstrates how ML technologies can automate compliance tasks, enhance security, and improve reporting accuracy. Key benefits of ML integration include efficiency gains, cost reductions, enhanced security, and improved auditability. Furthermore, emerging trends in ML techniques, such as deep learning and federated learning, are discussed along with actionable recommendations for successful ML implementation in cloud compliance strategies. The findings emphasize the importance of investing in data governance, continuous monitoring, and interpretability of ML models to ensure ethical and effective compliance management. Overall, this research sheds light on the transformative potential of ML in optimizing regulatory compliance practices and outlines future directions for leveraging advanced technologies to address evolving compliance challenges.