The rapid digital transformation of modern organizations has significantly expanded the attack surface of information systems, particularly with the widespread adoption of distributed architectures, Internet of Things ecosystems, and microservices-based applications. Traditional perimeter-based security approaches have proven insufficient for protecting highly interconnected digital infrastructures where trust assumptions can be easily exploited by sophisticated adversaries. As a result, the Zero Trust Architecture (ZTA) paradigm has emerged as a fundamental cybersecurity model that eliminates implicit trust and continuously verifies identities, devices, and services before granting access to resources. This research article presents an extensive theoretical and analytical investigation into the integration of Zero Trust Architecture with intelligent threat detection mechanisms in distributed computing environments.

The study synthesizes insights from contemporary cybersecurity research, focusing particularly on the convergence of ZTA with anomaly detection systems, cyber threat intelligence mining, machine learning-based intrusion detection, and secure microservices architectures. Drawing upon a structured multivocal literature analysis and theoretical modeling approach, the research explores how zero trust principles can enhance security resilience across cloud-native systems, cyber-physical infrastructures, and IoT environments. Particular emphasis is placed on the role of continuous authentication, behavioral analytics, federated learning, and blockchain-enabled trust frameworks in strengthening distributed security mechanisms.

Findings reveal that while Zero Trust Architecture significantly reduces insider threats, lateral movement, and unauthorized data access, its effectiveness depends heavily on the integration of intelligent detection mechanisms capable of analyzing network behaviors and contextual data in real time. The research also highlights emerging innovations such as anomaly-based intrusion detection, artificial intelligence-driven trust evaluation, and decentralized security enforcement frameworks. Additionally, the article explores the security implications of microservices ecosystems, where service-to-service authentication and policy enforcement become critical elements of a zero trust implementation strategy.

The analysis concludes that a hybrid cybersecurity model combining Zero Trust Architecture with adaptive threat intelligence and machine learning techniques offers a promising pathway toward resilient and scalable security frameworks for modern digital infrastructures. The article further identifies significant challenges, including architectural complexity, scalability limitations, policy management difficulties, and operational costs associated with large-scale zero trust deployment. Finally, the research proposes future research directions centered on explainable security analytics, federated cybersecurity intelligence sharing, and the application of large language models for automated security policy generation and incident response support.