Machine Learning Based Anomaly Detection and Predictive Risk Management for Enterprise Cloud Platforms

Abstract

Enterprise cloud platforms generate enormous volumes of operational, security, application, network, and infrastructure data that must be continuously analyzed to identify abnormal behavior and emerging risks. Conventional rule-based monitoring approaches often struggle to detect previously unknown anomalies, adapt to changing workloads, and distinguish legitimate variations from genuine threats. This research proposes a machine learning-based anomaly detection and predictive risk management framework for enterprise cloud platforms that combines real-time telemetry collection, intelligent feature engineering, anomaly detection, predictive analytics, risk scoring, and automated response. The proposed framework collects metrics and events from cloud infrastructure, virtual machines, containers, Kubernetes clusters, applications, APIs, networks, authentication systems, and security services. Machine learning algorithms are applied to identify deviations from normal operational behavior and predict potential failures, security incidents, performance degradation, and resource-related risks. Supervised, unsupervised, and hybrid learning approaches are incorporated according to data availability and risk characteristics. A predictive risk engine converts model outputs into prioritized risk scores using severity, probability, exposure, and business impact. Cloud-native deployment using microservices and container orchestration enables scalable and resilient analytics. The methodology also incorporates explainable AI, continuous model monitoring, data-quality validation, and feedback-driven model refinement. The framework is expected to improve anomaly detection accuracy, reduce false positives, accelerate risk identification, enhance operational resilience, and support proactive enterprise cloud management.