ARTIFICIAL INTELLIGENCE AND CHANGE OF ZERO-TRUST ARCHITECTURE: AN OVERVIEW OF ACTIVE AND CUSTOM NETWORK PROTECTION

Abstract

Increasing sophistication of cyber threats in connection with traditional network circuit solution through cloud computing and mobile workforce has reduced the traditional, perimeter -based security models. Zero-Trust Architecture (ZTA) has proven to be a paramount paradigm, and the work on the principle of "ever trust, always confirmed". However, the practical implementation of ZTA, especially on a large scale, provides in the dynamic environment, important challenges in policy management, log analysis and real -time decision -making. This article proposes an overview to integrate and integrate artificial intelligence (AI) and machine learning (ML) to automate and improve ZTA. We believe that AI is not only a supplementary technique, but in fact is a basic environment for achieving dynamic and active zero-Trust models. The function involves a systematic review of ZTA core components and AI abilities, followed by an integrated AI-ZA frame design. This framework benefits from user and device dismsive analysis (UBA), ML for natural language treatment (NLP) for automated policy production, and deep education to detect real -time deviations in network traffic. Our analysis suggests that AI-operated ZTA detection (MTTD) can reduce medium time and adapt to hazards (MTTR) for hazards (MTTR) time, to automate policy enforcement and adapt real-time safety currencies based on calculation risk. Discussion addresses important implementation challenges, including data quality, model training, algorithm bias and computational overheads. We conclude that coordination between AI and zero-Trust is necessary to create flexible, adaptive and scalable safety infrastructure that is able to defend against modern cyber opponents.