Integrating Zero-Trust Model with AI Techniques to Enhance Proactive Monitoring of User Behavior in Enterprise Security

As cyber threats increase in complexity and sophistication, traditional security models are no longer sufficient to protect large organizations. We have recently witnessed a radical shift in the cybersecurity threat landscape, with attacks becoming more sophisticated and organized with a marked increase in insider attacks, leading to the urgent need to adopt proactive and adaptive security strategies. Therefore, by analyzing the effectiveness of the Zero Trust Security model as an advanced security strategy based on the principle of "never trust and always verify" and through a rigorous multi-method approach that significantly enhances organizational security postures, this study aimed to design and develop an integrated security framework that integrates the architectural principles of the Zero Trust (ZTA) model with machine learning and artificial intelligence (AI) capabilities. This framework aims to enable continuous and proactive monitoring of user and device behavior, thereby achieving more accurate and effective detection of internal and external threats in large enterprise environments. The research adopted an empirical methodology , where the proposed model was built and tested in a simulation environment designed to simulate the network infrastructure of a large organization. The developed framework included the following key components: Algorithms for real-time user risk assessment and an automated AI-managed incident response mechanism. The performance of the model was measured based on a comprehensive set of performance criteria. The results showed a significant improvement in all key security metrics, which in turn led to enhanced protection against internal and external threats by enabling security resilience and continuous verification to optimize compliance with security regulations. The proposed model also achieved: 92.3% detection accuracy against advanced persistent threats (APTs) and insider attacks, and a 58.7% reduction in average incident response time (MTTR). Providing both theoretical and practical advances in enterprise cybersecurity, offering empirically proven insights into security architectures, the study provides organizations with an evidence-based framework for moving to more resilient, AI-enabled environments in zero-trust environments while maintaining operational feasibility and addressing real-world implementation considerations. However, the implementation faced challenges such as high cost, technical complexity, and resistance to organizational change. The study concludes that Zero Trust's success requires phased planning, investment in supporting technologies and user education. It also recommends further research on the integration of Zero Trust with AI technologies to enhance proactive monitoring. This paper provides a practical framework for large organizations to assess the feasibility of adopting the Zero Trust model according to their security needs.