Proactive Enterprise Defense Powered by Autonomous Threat Intelligence and Generative AI for Continuous Cloud Security Monitoring

Abstract

Enterprise cloud environments have become increasingly dynamic, distributed, and interconnected, creating complex security challenges that traditional monitoring approaches cannot adequately address. Continuous workloads, rapidly changing identities, application programming interfaces, containers, microservices, and hybrid infrastructure generate large volumes of heterogeneous security telemetry that require timely interpretation and coordinated response. This research proposes a proactive enterprise defense framework powered by autonomous threat intelligence and generative artificial intelligence (GenAI) for continuous cloud security monitoring. The framework integrates threat intelligence, machine learning, security analytics, cloud observability, behavioral analysis, and generative AI to identify emerging threats, correlate security events, contextualize risks, and support automated defensive actions. Autonomous intelligence components continuously analyze logs, network events, identity activities, vulnerability information, and threat indicators to detect anomalous behavior and prioritize security incidents. GenAI is incorporated to synthesize complex evidence, generate security explanations, summarize incidents, and assist adaptive response orchestration. The research methodology combines architectural design, telemetry preprocessing, feature engineering, anomaly detection, threat correlation, generative analysis, and experimental validation using representative cloud-security scenarios. Evaluation focuses on detection accuracy, false-positive reduction, response latency, threat-context quality, scalability, and operational efficiency. The proposed approach establishes a continuous security intelligence cycle that strengthens visibility, accelerates threat identification, and supports proactive enterprise cyber defense across modern cloud environments.