Zero-Trust Network Architecture Using AI-Driven Dynamic Risk Assessment for Secure Access Control
Keywords:
Artificial Intelligence, Behavioral analytics, Cybersecurity architecture, Dynamic risk assessment, Secure access control, Zero-trust network architecture (ZTNA)Abstract
Zero-Trust Network Architecture (ZTNA) has emerged as a modern cybersecurity model that eliminates implicit trust within enterprise networks. Unlike traditional perimeter-based security approaches, Zero-Trust enforces continuous verification of users, devices, and applications before granting access to resources. However, many existing implementations rely on static policy enforcement mechanisms that lack contextual awareness and adaptability. In dynamic environments characterized by cloud computing, remote access, and distributed infrastructures, static access control is insufficient to address evolving cyber threats. This paper presents a conceptual framework for integrating Artificial Intelligence (AI)-driven dynamic risk assessment into Zero-Trust Architecture to enhance secure access control. The proposed model introduces an intelligent risk-evaluation layer that continuously analyzes contextual parameters such as user behavior patterns, device health status, geolocation anomalies, and access frequency. Based on real-time risk scoring, the system dynamically adjusts authentication requirements and authorization decisions. The study explains the architectural components, operational workflow, and security advantages of adaptive risk-based access enforcement. A comparative analysis demonstrates how AI-enhanced Zero-Trust improves threat detection capability and resilience against insider attacks and credential compromise. The paper also discusses security and privacy considerations associated with behavioral monitoring. The proposed approach contributes toward the development of intelligent, context-aware cybersecurity architectures for modern enterprise environments.
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