Zero-Trust Security Architecture for AI-Integrated Private 5G Networks

Abstract

Private 5G networks represent a transformative evolution in enterprise connectivity, blending reliability, ultra-low latency, and customizability for diverse applications. As organizations deploy these networks in manufacturing facilities and university campuses, integrating artificial intelligence creates both opportunities and security challenges. Traditional perimeter-based security models prove inadequate in these dynamic environments where devices move across network slices and workloads shift between edge and cloud. This article proposes an innovative Zero-Trust Security Architecture for AI-integrated private 5G networks, operating on the principle of "never trust, always verify" while leveraging AI for continuous authentication, behavioral analysis, and automated policy enforcement. The architecture's four-layer framework—Access, Transport & Segmentation, Policy & Intelligence, and Control & Orchestration—addresses the unique challenges of securing these environments. Key components include AI-powered identity management, micro-segmentation through network slicing, predictive threat detection, and a centralized governance layer. This comprehensive article enables organizations to maintain security integrity while fully leveraging the transformative potential of AI-integrated private 5G networks in critical infrastructure settings.