Behavioral Anomaly Detection in Enterprise Cloud Workloads using AI-Driven Profiling and Predictive Risk Assessment

Abstract

Enterprise cloud workloads exhibit dynamic behavioral patterns due to fluctuating resource utilization, distributed application execution, changing user activities, and automated infrastructure management. Conventional security mechanisms primarily rely on predefined rules and known threat signatures, limiting their effectiveness in identifying previously unseen attacks and subtle deviations from legitimate workload behavior. This study investigates an artificial intelligence-driven framework for behavioral anomaly detection in enterprise cloud environments through continuous profiling and predictive risk assessment. The proposed framework integrates telemetry collection, behavioral feature extraction, baseline profiling, machine learning-based anomaly detection, and contextual risk prediction to identify suspicious activities across cloud workloads. Historical and real-time observations, including CPU utilization, memory consumption, network traffic, authentication events, process activities, and API access patterns, are analyzed to establish behavioral profiles for users, applications, containers, and virtual machines. Anomaly scores are combined with asset criticality, privilege levels, threat intelligence, and deviation severity to prioritize potential security incidents. The research methodology adopts a design-oriented approach supported by controlled experiments and quantitative performance evaluation. Detection precision, recall, F1-score, false-positive rate, detection latency, and predictive risk accuracy are used to assess system effectiveness. The framework also incorporates model explainability, adaptive learning, privacy protection, and human-supervised response mechanisms. The proposed approach aims to improve the early identification of abnormal workload activities, reduce unnecessary security alerts, and strengthen proactive threat management while maintaining the operational reliability and scalability of enterprise cloud infrastructure.