Intelligent AI-Driven Behavioral Anomaly Detection and Predictive Threat Mitigation for Secure Enterprise Networks and Cloud Environments

The increasing sophistication of cyberattacks and the rapid expansion of cloud computing have created significant challengesfor enterprise network security. Traditional security mechanisms, which primarily depend on predefined rules, signatures, andstatic thresholds, often struggle to identify unknown threats, advanced persistent attacks, and subtle deviations in user or systembehavior. Artificial intelligence (AI)-based anomaly detection offers a proactive approach by analyzing behavioral patterns acrossnetwork traffic, cloud workloads, user activities, and system events. This study examines an integrated security framework thatcombines machine learning, behavioral analytics, and real-time monitoring to detect suspicious activities and prevent cloudbasedthreats. The proposed approach incorporates supervised and unsupervised learning techniques to identify known attackpatterns and previously unseen anomalies. Behavioral profiles are constructed using network flows, authentication events, accesshistories, resource utilization, and cloud audit logs. The methodology emphasizes data preprocessing, feature engineering, modeltraining, anomaly scoring, and comparative evaluation using established cybersecurity metrics. Detection performance is assessedthrough precision, recall, F1-score, false-positive rate, and detection latency. The framework also considers automated responsemechanisms, explainable AI, privacy protection, and adversarial resilience. By integrating contextual behavioral analysis withadaptive threat detection, the proposed framework aims to strengthen enterprise security monitoring, improve incident response,and reduce the operational impact of cyber threats. The study provides a methodological foundation for evaluating AI-drivenanomaly detection in increasingly distributed, dynamic, and complex enterprise network and cloud environments.