Behavioral Anomaly Analysis for Enterprise Cloud Workloads Using AI-Based Risk Detection and Predictive Modeling

Abstract

Enterprise cloud workloads operate within dynamic, distributed environments where changing resource utilization, complex application dependencies, and evolving cyber threats create significant challenges for security monitoring and risk management. Traditional detection mechanisms frequently depend on static thresholds and predefined rules, limiting their ability to identify previously unseen anomalies and predict emerging operational risks. This research proposes an artificial intelligence-based framework for behavioral anomaly analysis, risk detection, and predictive modeling across enterprise cloud workloads. The framework integrates infrastructure telemetry, application logs, network activity, identity events, and workload performance metrics to establish behavioral baselines and identify deviations from normal operational patterns. Machine learning algorithms, including random forests, isolation forests, autoencoders, and recurrent neural networks, are considered for anomaly classification, unsupervised detection, and temporal risk prediction. A risk assessment component combines anomaly severity, contextual information, asset criticality, and predicted threat probability to prioritize security events. The methodology incorporates data preprocessing, feature engineering, model training, comparative evaluation, and controlled validation under different workload conditions. Evaluation metrics include precision, recall, F1-score, false-positive rate, prediction error, detection latency, and computational overhead. The proposed framework aims to improve threat visibility, support proactive incident response, and strengthen operational resilience in enterprise cloud environments. Its effectiveness must be established through reproducible experiments and comparisons with conventional detection techniques. The research provides a structured foundation for developing adaptive, scalable, and explainable cloud security analytics.