Zero-Trust Enforcement for Hybrid Cloud Enterprise Systems Through Continuous Identity Verification and AI-Driven Security Analytics

Abstract

The increasing adoption of hybrid cloud infrastructure has introduced complex security challenges involving distributed applications, remote access, identity management, and cross-platform data exchange. Traditional perimeter-based security mechanisms are insufficient for protecting enterprise environments where users, devices, workloads, and applications operate across private data centers and public cloud platforms. This research proposes a zero-trust security framework that integrates continuous identity verification with artificial intelligence (AI)-driven security analytics to strengthen hybrid cloud enterprise protection. The proposed framework follows the principle of never trust, always verify by evaluating identity credentials, device posture, access context, behavioral patterns, and resource sensitivity before granting or maintaining access. Machine learning techniques analyze authentication logs, network traffic, endpoint activities, and application events to identify suspicious behaviors, detect potential threats, and support adaptive security policy enforcement. The framework incorporates multifactor authentication, least-privilege access, continuous session assessment, anomaly detection, and automated incident response to improve security visibility and reduce unauthorized access risks. Its methodological design includes data collection, preprocessing, identity risk scoring, threat classification, policy evaluation, and performance assessment using detection accuracy, false-positive rate, response latency, and access decision consistency. The proposed approach aims to provide scalable, context-aware, and resilient security for hybrid cloud enterprises. The framework offers a foundation for integrating intelligent threat detection with continuous authorization while maintaining operational efficiency, data confidentiality, and regulatory compliance.