Zero trust network access driven by ai behavior analysis

Enterprise networks are growing fast, and cyber-attacks are becoming more advanced. Because of this, traditional perimeter security is no longer enough. In many cases, once a user enters the internal network, the system trusts them automatically. This can allow insider attacks and help attackers move inside the network after a breach. Zero Trust Network Access (ZTNA) solves this problem by not trusting any user or device by default and by checking every access request.The aim of this project is to design and implement an AI driven ZTNA system for an enterprise like environment with Windows endpoints. The system controls access at the network gateway using a distributed design and a general-purpose hardware platform. Strong authentication is used, including certificates, device identity, and username password login. An AI module is implemented to study access behavior and help the system make better access decisions.The implementation shows that adding AI to ZTNA helps in detecting abnormal access patterns and improves access control. The results support using AI driven Zero Trust systems to increase security in modern enterprise networks.