AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management

Abstract

The increasing complexity of enterprise IT environments, coupled with the rise of cloud adoption, remote work, and sophisticated cyber threats, has exposed traditional perimeter-based security models as insufficient for protecting sensitive enterprise data. Zero Trust Security (ZTS) has emerged as a robust framework emphasizing continuous verification, least-privilege access, and granular security policies to safeguard organizational assets. When combined with Artificial Intelligence (AI), Zero Trust models can become more adaptive, predictive, and proactive in detecting and mitigating security threats.
This research proposes an AI-driven Zero Trust Security model designed to enhance enterprise data protection and intelligent infrastructure management. The framework integrates AI-powered analytics, machine learning-based anomaly detection, behavioral monitoring, and automated policy enforcement across enterprise networks, cloud platforms, and critical infrastructure components. By continuously analyzing user behavior, network traffic, and system interactions, the system identifies potential security breaches, enforces dynamic access controls, and reduces the attack surface.
The research methodology includes architectural modeling, simulation of enterprise scenarios, and performance evaluation against traditional security approaches. Results demonstrate that AI-enhanced Zero Trust architectures significantly improve threat detection accuracy, automate compliance management, and optimize security policies for dynamic enterprise environments. This model supports intelligent infrastructure management while ensuring robust data protection, regulatory compliance, and resilience against emerging cyber threats.