Adaptive Zero Trust Architecture: Leveraging ...

Adaptive Zero Trust Architecture: Leveraging Random Forest for Dynamic Trust Assessment in Enterprise Security

Author Name : Praveen Kumar Thopalle

ABSTRACT The increasing prevalence of cyber threats and sophisticated attacks necessitates a shift towards more robust security models in enterprise environments. This research explores the design and development of Zero Trust Architecture (ZTA), emphasizing the incorporation of machine learning for adaptive trust assessment. Zero Trust principles, which dictate that no user or system should be implicitly trusted inside or outside the organization’s network, require dynamic and context-aware security decisions. The integration of machine learning into Zero Trust frameworks provides the ability to continuously assess trust based on behavior analysis, anomaly detection, and predictive risk evaluation. By leveraging machine learning models, this research aims to enhance the security posture of enterprise systems through real-time decision-making, thereby minimizing vulnerabilities and unauthorized access. This paper presents a detailed analysis of Zero Trust principles, discusses challenges in implementation, and introduces a novel adaptive trust mechanism supported by machine learning techniques.