ADAPTIVE ZERO TRUST ARCHITECTURE FOR SECURING DISTRIBUTED FINANCIAL MICROSERVICES

Abstract

Financial institutions increasingly adopt cloudnative microservices architectures to improve scalability, agility, resilience, and service availability. However, distributed financial microservices introduce significant cybersecurity challenges, including unauthorized access, lateral movement, API vulnerabilities, identity compromise, insider threats, and sophisticated cyberattacks targeting financial transactions and customer data. Conventional perimeter-based security models are insufficient because modern financial services operate across hybrid cloud environments with continuously changing workloads and users. Recent advances in Zero Trust Architecture (ZTA), Artificial Intelligence, Machine Learning (ML), behavioral analytics, cloud-native security, and continuous authentication enable adaptive protection for distributed financial systems. This paper proposes an Adaptive Zero Trust Architecture for securing distributed financial microservices by integrating identity-aware access control, continuous risk assessment, intelligent policy enforcement, API security, cloud-native monitoring, and real-time threat analytics. The proposed framework dynamically evaluates user behavior, workload trust, device posture, transaction context, and service interactions before granting access decisions. Experimental analysis demonstrates significant improvements in cyberattack detection, access control accuracy, threat mitigation, operational resilience, regulatory compliance, and enterprise security compared with conventional perimeter-based security architectures. Keywords: Zero Trust Architecture, Financial Microservices, Cybersecurity, Machine Learning, Cloud Security, API Security, Identity Management, Financial Services, Threat Detection, Distributed Systems.