Zero Trust Architecture for Machine Learning-Enabled Digital Enterprise Security and Adaptive Operational Resilience

Abstract

The rapid adoption of cloud computing, machine learning, distributed applications, remote workforces, and interconnected enterprise platforms has increased the complexity of digital security and operational resilience. Traditional perimeter-based security models are increasingly inadequate because enterprise resources, users, applications, and data are distributed across heterogeneous environments. This study proposes a Zero Trust Architecture (ZTA) integrated with machine learning (ML) to strengthen digital enterprise security and enable adaptive operational resilience. The proposed framework continuously verifies identities, evaluates contextual risk, monitors behavioral patterns, and dynamically adjusts access privileges according to changing security conditions. Machine learning models are employed to identify anomalous user activities, detect deviations in application behavior, classify potential threats, and support adaptive risk scoring. The architecture combines identity-aware access control, micro-segmentation, continuous monitoring, encryption, policy enforcement, security analytics, and automated incident response. A research methodology based on architecture design, synthetic enterprise security data generation, ML model development, simulation, and comparative evaluation is presented. Performance is assessed using detection accuracy, precision, recall, F1-score, false-positive rate, response time, policy enforcement effectiveness, and resilience indicators. The proposed approach provides a foundation for continuously adaptive enterprise protection while supporting secure business operations across hybrid, multi-cloud, and distributed digital environments.