AI-Augmented Zero Trust Architectures: Enhancing Cybersecurity in Dynamic Enterprise Environments

Abstract

In today's increasingly interconnected enterprise environments, traditional cybersecurity measures struggle to keep pace with the dynamic nature of evolving threats. This paper presents an AI-augmented Zero Trust architecture designed to enhance cybersecurity in enterprise systems by employing continuous verification, least-privilege access, and predictive analytics. Leveraging machine learning algorithms, the proposed framework can dynamically adjust security policies in real-time, detect anomalous behaviors, and reduce the attack surface. By integrating AI-driven threat intelligence and automated incident response, the architecture ensures heightened protection across diverse and distributed systems. This study provides an in-depth analysis of the role of AI in fortifying Zero Trust principles, emphasizing its impact on enhancing agility, scalability, and resilience in cybersecurity operations within enterprise environments