An Intelligent Generative AI Framework for Adaptive Threat Monitoring and Secure Cloud Computing

Abstract

The rapid evolution of cloud computing has enabled organizations to deploy scalable applications and distributed services with greater efficiency; however, it has also increased the complexity of protecting cloud infrastructures against sophisticated and continuously evolving cyber threats. Conventional cloud security monitoring solutions primarily rely on static rules, predefined signatures, and manually crafted detection policies, making them less effective against zero-day attacks, advanced persistent threats, and dynamic attack behaviors. To address these limitations, this research proposes an intelligent Generative AI-driven framework for adaptive cloud security monitoring that delivers proactive threat analysis and continuous security assessment across modern cloud environments.The proposed framework integrates security telemetry from multiple cloud sources, including system logs, network traffic, authentication records, application events, and resource utilization metrics, to establish a comprehensive view of cloud security operations. Advanced generative artificial intelligence techniques are employed to model complex behavioral patterns, correlate security events, generate contextual threat intelligence, and identify previously unseen attack scenarios that cannot be effectively recognized using conventional signaturebased approaches. By combining intelligent anomaly detection with adaptive learning, the framework continuously refines its detection capability based on newly observed attack patterns, thereby improving its resilience against evolving cybersecurity threats.Furthermore, the proposed system incorporates automated risk assessment and intelligent alert prioritization to reduce false-positive notifications and assist security analysts in making timely and informed decisions. Experimental evaluation is performed using standard performance metrics, including accuracy, precision, recall, F1- score, detection rate, and false-positive rate, to assess the effectiveness of the proposed framework. The results demonstrate that the integration of Generative AI with adaptive security analytics significantly enhances threat detection performance, strengthens situational awareness, and improves the overall security posture of cloud computing environments. The proposed framework offers a scalable, intelligent, and future-ready solution for safeguarding cloud infrastructures while supporting automated cyber defense against emerging and sophisticated security threats.