AI-Driven Security Intelligence for Real-Time Threat Detection and Predictive Risk Analytics in Distributed Cloud Enterprise Environments

Abstract

The rapid adoption of distributed cloud computing has transformed enterprise information systems while introducing complex cybersecurity challenges across multi-cloud platforms, hybrid infrastructures, distributed applications, and remote access environments. Traditional security mechanisms often struggle to identify sophisticated attacks, correlate security events across heterogeneous systems, and predict emerging threats before significant damage occurs. This research explores an artificial intelligence (AI)-driven security intelligence framework for real-time threat detection and predictive risk analytics in distributed cloud enterprise environments. The proposed approach integrates machine learning, deep learning, behavioral analytics, anomaly detection, and security event correlation to identify malicious activities and assess potential risks continuously. Security data collected from cloud workloads, identity management systems, network traffic, application logs, and endpoint devices are processed to establish behavioral baselines and detect deviations associated with cyberattacks. Predictive analytics subsequently evaluates threat likelihood, vulnerability exposure, asset criticality, and potential business impact to prioritize security interventions. The methodology emphasizes scalable data processing, explainable AI, privacy preservation, and automated incident response. Evaluation is designed around detection accuracy, precision, recall, false-positive rate, processing latency, and predictive risk performance. The proposed framework aims to improve threat visibility, accelerate incident investigation, and support proactive cybersecurity decision-making. The research contributes a systematic model for integrating intelligent threat detection and risk prediction into distributed enterprise cloud security operations.