Intelligent Threat Prediction and Adaptive Risk Analytics Across Multi-Cloud Enterprise Workflows Using Deep Learning

Abstract

The increasing adoption of multi-cloud enterprise environments has introduced complex cybersecurity challenges involving distributed workloads, interconnected applications, dynamic user identities, and continuously evolving attack patterns. Traditional security mechanisms often depend on predefined rules and reactive threat detection, limiting their ability to identify sophisticated attacks across heterogeneous cloud infrastructures. This research proposes an intelligent threat prediction and adaptive risk analytics framework using deep learning to strengthen security monitoring and proactive risk management across multi-cloud enterprise workflows. The proposed framework integrates cloud activity logs, network traffic records, identity access events, application transactions, and infrastructure monitoring data to identify abnormal behavioral patterns and predict potential security threats. Deep learning techniques, including Long Short-Term Memory networks, Convolutional Neural Networks, and Autoencoders, are considered for temporal analysis, attack classification, and anomaly detection. An adaptive risk assessment mechanism evaluates threat severity by considering attack probability, asset sensitivity, user behavior, and operational impact. The framework also supports continuous risk scoring, contextual threat prioritization, and automated security recommendations across distributed cloud platforms. By combining predictive intelligence with adaptive security analytics, the proposed approach aims to improve threat visibility, detection accuracy, incident response efficiency, and enterprise cyber resilience while supporting scalable and secure multi-cloud operations.