Enhancing Cloud Compliance: A Machine Learning Approach

Abstract

The intersection of machine learning (ML) and cloud computing presents significant opportunities to enhance cloud compliance and security practices. This research paper explores the role of ML in improving cloud compliance, focusing on proactive threat detection, automated incident response, and adaptive security controls. The importance of ML-driven approaches lies in their ability to analyse large datasets, detect anomalies, and mitigate risks in dynamic cloud environments. Methods employed include case studies and experiments showcasing real-world applications of ML in cloud security, such as Google Cloud's Context-Aware Access and AWS GuardDuty for threat detection. Experimental findings demonstrate the effectiveness of ML models in reducing mean time to detect (MTTD) security incidents and improving incident response capabilities. Results highlight the transformative impact of ML technologies in bolstering cloud security effectiveness and resilience. ML-powered compliance monitoring systems, like Netflix's, have significantly improved compliance posture while reducing operational costs. Implications of this research include enhanced security governance, reduced compliance risks, and improved operational efficiencies within cloud infrastructures. Future directions entail exploring advanced ML techniques, addressing ethical considerations, and integrating ML-driven security frameworks into holistic cloud governance strategies.