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Predictive Machine Learning and Advanced Cloud Intelligence for Real-Time Cyber Risk Assessment in Enterprise Systems

Subrahmanyasarma Chitta

Sr Software Engineer, Access2care LLC (MTM), saint Louis, MO, USA

Abstract

The rapid expansion of cloud computing, distributed applications, APIs, remote services, and interconnected enterprise infrastructures has significantly increased the complexity of cybersecurity risk management. Conventional security assessment approaches frequently depend on static rules, periodic audits, predefined signatures, and manually interpreted security events, limiting their ability to respond effectively to continuously changing cyber threats. This paper proposes a predictive machine learning and advanced cloud intelligence architecture for real-time cyber risk assessment in enterprise systems. The proposed framework integrates cloud-native telemetry collection, security event processing, machine learning-based anomaly detection, predictive risk modeling, threat intelligence, behavioral analytics, and automated risk prioritization. Security data from endpoints, networks, applications, APIs, identity systems, cloud workloads, and enterprise databases are continuously collected and transformed into contextual features. Machine learning models analyze behavioral patterns to identify anomalies, estimate threat probabilities, forecast emerging risks, and classify security incidents. A cloud intelligence layer correlates heterogeneous security events with asset criticality, historical incidents, vulnerability information, and threat intelligence to generate dynamic risk scores. The framework also supports real-time alert prioritization and policy-controlled automated response. Continuous feedback and model monitoring enable adaptation to changing attack patterns and infrastructure conditions. The proposed architecture aims to improve cyber risk visibility, detection speed, predictive accuracy, response efficiency, scalability, and enterprise resilience while reducing false positives and dependence on manually configured security rules.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2026.1105003

Pages

21986-21997

Published

September 6, 2026
Copyright

Open Access

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

How to Cite

Subrahmanyasarma Chitta (%2026). Predictive Machine Learning and Advanced Cloud Intelligence for Real-Time Cyber Risk Assessment in Enterprise Systems. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21986-21997. https://doi.org/10.15662/ijetemr.2026.1105003

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