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

Abstract

The increasing sophistication of cyberattacks and the rapid expansion of cloud computing have created significant challenges
for enterprise network security. Traditional security mechanisms, which primarily depend on predefined rules, signatures, and
static thresholds, often struggle to identify unknown threats, advanced persistent attacks, and subtle deviations in user or system
behavior. Artificial intelligence (AI)-based anomaly detection offers a proactive approach by analyzing behavioral patterns across
network traffic, cloud workloads, user activities, and system events. This study examines an integrated security framework that
combines machine learning, behavioral analytics, and real-time monitoring to detect suspicious activities and prevent cloudbased
threats. The proposed approach incorporates supervised and unsupervised learning techniques to identify known attack
patterns and previously unseen anomalies. Behavioral profiles are constructed using network flows, authentication events, access
histories, resource utilization, and cloud audit logs. The methodology emphasizes data preprocessing, feature engineering, model
training, anomaly scoring, and comparative evaluation using established cybersecurity metrics. Detection performance is assessed
through precision, recall, F1-score, false-positive rate, and detection latency. The framework also considers automated response
mechanisms, explainable AI, privacy protection, and adversarial resilience. By integrating contextual behavioral analysis with
adaptive 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-driven
anomaly detection in increasingly distributed, dynamic, and complex enterprise network and cloud environments.