AI-Powered Enterprise Risk Analytics for Hybrid Cloud Infrastructure with Predictive Security Intelligence
DOI:
https://doi.org/10.21590/ijtmh.10.04.50Keywords:
Artificial Intelligence, Enterprise Risk Analytics, Hybrid Cloud, Predictive Security Intelligence, Machine Learning, Cybersecurity, Cloud Security, Anomaly Detection, Threat Intelligence, Risk Prediction, Security Analytics, Cloud Infrastructure, Vulnerability Management, Behavioral Analytics, Predictive Risk ManagementAbstract
Hybrid cloud infrastructure has become an essential component of enterprise digital transformation because it enables organizations to combine private infrastructure, public cloud services, edge resources, and distributed applications according to operational requirements. However, the resulting complexity increases exposure to cybersecurity threats, configuration weaknesses, service disruptions, data leakage, and compliance risks. Conventional security monitoring approaches primarily identify threats after suspicious activity has occurred and may have limited ability to predict emerging risks across heterogeneous environments. This paper proposes an AI-powered enterprise risk analytics framework that integrates predictive security intelligence with hybrid cloud monitoring and risk management. The proposed approach combines machine learning, anomaly detection, behavioral analytics, threat intelligence, cloud telemetry, vulnerability information, and contextual risk scoring to identify potential security and operational threats before they result in significant impact. The framework collects data from cloud platforms, virtual machines, containers, applications, network infrastructure, identity systems, and security controls and transforms heterogeneous observations into a unified risk representation. Predictive models are used to forecast abnormal behavior, identify emerging vulnerabilities, and prioritize risks according to likelihood and potential business impact. The research methodology adopts a design-science and experimental approach involving data preparation, predictive model development, risk-scoring design, hybrid-cloud simulation, and security evaluation. Performance is assessed using predictive accuracy, detection rate, false-positive rate, response time, risk-prioritization accuracy, and operational resilience. The proposed framework aims to provide enterprises with proactive, contextual, and continuously updated security intelligence for managing hybrid cloud risks.
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