Optimizing Network Traffic Through AI-Enhanced Zero-Trust Architectures
S. Vishal, S Kiruthik Vishaal · FMDB Transactions on Sustainable Intelligent Networks · 2025
Zero-Trust Architectures (ZTA) migration involves transitioning from perimeter-based security to persistent authentication for every request to access resources. Although it introduces security, the migration comes with a high overhead cost, inducing network latency and increased complexity in policy administration. This paper presents an AI-based framework for ZTA that minimizes network traffic flow without compromising security tenets. Our solution involves a deep reinforcement learning (DRL) agent that dynamically varies network paths and access privileges in real-time depending on device posture, end-user behaviour, and app sensitivity. The study was proven in an emulated enterprise network deployment. The primary data used here is the 'ZTA-Traffic-Sim-2025', which contains simulated data of 10 million network flow records. Traffic for 5,000 users and 15,000 devices was simulated for a month, incorporating various simulated attack vectors. The model is trained using Python with TensorFlow for the DRL agent and the ns-3 simulator to simulate the network environment. The results confirm that the AI-based ZTA reduces average network latency by up to 35% and throughput by 25% compared to static ZTA, while improving the detection rate for anomaly activity by 18%.