BEHAVIOR-AWARE ACCESS CONTROL FOR REAL-TIME PROTECTION OF FINANCIAL MICROSERVICES

Abstract

Cloud-native financial institutions increasingly deploy distributed microservices to support digital banking, payment processing, investment management, insurance services, and fraud detection. Although microservices improve scalability, agility, and operational resilience, they also expand the cybersecurity attack surface by enabling numerous service interactions, API communications, identity exchanges, and workload transactions across hybrid cloud environments. Conventional rolebased and rule-based access control mechanisms are insufficient because they cannot dynamically adapt to continuously changing user behavior, transaction context, device posture, workload characteristics, and cyber threats. Recent advances in Machine Learning (ML), behavioral analytics, Zero Trust Architecture (ZTA), cloud-native security, and adaptive access management enable intelligent behavior-aware authorization for distributed financial ecosystems. This paper proposes a Behavior-Aware Access Control framework for real-time protection of financial microservices by integrating behavioral profiling, adaptive risk assessment, Machine Learning, cloud-native monitoring, API security, and continuous policy enforcement. The proposed framework dynamically evaluates user behavior, workload interactions, financial transactions, device posture, and contextual cybersecurity intelligence before granting access decisions. Experimental analysis demonstrates significant improvements in adaptive access control accuracy, fraud prevention, cyberattack detection, operational resilience, regulatory compliance, and enterprise security compared with conventional static authorization mechanisms. Keywords: Behavior-Aware Access Control, Financial Microservices, Machine Learning, Zero Trust Architecture, Cloud Security, Behavioral Analytics, API Security, Identity Management, Financial Cybersecurity, Distributed Systems.