Achieving Regulatory Compliance in Cloud Computing through ML

Abstract

Organizations face substantial obstacles in attaining regulatory compliance in today’s ever-changing cloud computing ecosystem
as a result of complicated legal requirements and ever-evolving security concerns. In order to better understand how machine
learning (ML) might improve cloud regulatory compliance, this research paper investigates this topic. The report delves into the
present regulatory landscape, examines the obstacles to compliance, and assesses the consequences for firms that fail to comply.
This article shows how ML technologies may simplify compliance activities, boost security, and improve reporting accuracy by
analyzing real-world case studies, such as Microsoft Azure Sentinel and Google Cloud’s Data Loss Prevention (DLP) API. Improving
productivity, cutting costs, increasing security, and making it easier to audit are all major advantages of ML integration.
In addition, practical suggestions for ML deployment in cloud compliance methods are covered, along with discussion of new
ML trends like federated learning and deep learning. In order to guarantee effective and ethical compliance management, the
results stress the significance of data governance, ongoing monitoring, and the interpretability of ML models. The study concludes
by outlining future options for using advanced technologies to solve emerging compliance difficulties and shedding light
on the revolutionary potential of ML in improving regulatory compliance operations.