Intelligent Enterprise Cyber Defense Through Continuous Zero-Trust Verification and AI-Driven Cloud Threat Detection

Abstract

The increasing adoption of hybrid cloud infrastructures, distributed applications, microservices, and remote access technologies has expanded the attack surface of modern enterprises. Conventional perimeter-based security mechanisms are insufficient to address sophisticated threats involving compromised identities, unauthorized privilege escalation, lateral movement, and cloud workload exploitation. This research proposes an intelligent enterprise cyber defense framework integrating continuous Zero-Trust verification with artificial intelligence-driven cloud threat detection. The framework evaluates identity attributes, device posture, workload behavior, access context, and real-time threat intelligence to make adaptive authorization decisions. Machine learning models analyze authentication logs, network flows, cloud audit records, and application telemetry to identify anomalous activities and emerging attack patterns. A policy enforcement mechanism dynamically adjusts access privileges, strengthens authentication requirements, and isolates suspicious workloads according to calculated risk levels. The proposed architecture incorporates identity-centric access control, behavioral analytics, automated incident prioritization, and continuous security monitoring across hybrid cloud environments. Its effectiveness is evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, policy enforcement latency, and resource overhead. Comparative experiments examine conventional rule-based detection, AI-based monitoring without continuous verification, and the integrated framework. The research aims to establish whether combining adaptive access decisions with intelligent threat analytics improves enterprise security resilience while maintaining operational efficiency, scalability, and compliance. Experimental validation is required to quantify benefits and identify limitations associated with model bias, adversarial manipulation, and heterogeneous cloud deployments.