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Transparent Compliance Analytics for Hybrid Cloud Infrastructure Using Explainable Machine Learning and Predictive Intelligence

Prof Kuthadi Venu Madhav

Department of Computing and Informatics, Botswana International University of Science and Technology, Botswana, South Africa

Abstract

Hybrid cloud infrastructure has become an essential component of modern enterprise computing, enabling organizations to distribute applications, data, and workloads across private data centers and public cloud platforms. However, maintaining regulatory compliance across heterogeneous environments presents significant challenges involving data protection, access governance, configuration management, auditability, and continuous risk assessment. Traditional compliance approaches frequently depend on periodic audits, manually defined rules, and fragmented monitoring systems, limiting their ability to identify emerging violations and explain complex security risks. This paper proposes a transparent compliance analytics framework integrating explainable machine learning, predictive intelligence, automated policy evaluation, and continuous cloud monitoring. The framework collects infrastructure configurations, identity events, audit logs, resource utilization records, and security telemetry to evaluate compliance conditions across hybrid cloud environments. Machine learning techniques identify anomalous configurations and predict potential compliance deviations, while explainable artificial intelligence provides interpretable evidence supporting individual predictions. A centralized policy engine maps detected conditions to organizational controls and applicable regulatory requirements, enabling prioritized remediation and traceable audit reporting. Predictive risk scoring further supports proactive decision-making by estimating the likelihood and potential impact of future compliance violations. The proposed methodology emphasizes model interpretability, data quality, interoperability, privacy, and human oversight. Its effectiveness should be assessed through comparative experiments measuring detection accuracy, false-positive rates, prediction latency, explanation quality, and remediation efficiency. The framework aims to improve compliance visibility, strengthen accountability, and support continuous, risk-informed governance across distributed enterprise cloud infrastructures.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2023.0806012

Pages

14782-14790

Published

November 15, 2023
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Prof Kuthadi Venu Madhav (%2023). Transparent Compliance Analytics for Hybrid Cloud Infrastructure Using Explainable Machine Learning and Predictive Intelligence. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14782-14790. https://doi.org/10.15662/ijetemr.2023.0806012

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