AI-Driven Adaptive Zero-Trust Models for U.S. Defense Networks

Abstract

The dynamically changing cybersecurity landscape in the USA demands a much more robust Identity Access management approach. At the core of this evolution lies the trust mode of security relies on the never-trusted-always-verify principles, turning it into one of the leading ways to plan how access to crucial resources should be secured. This study investigates the use of Artificial Intelligence in improving Identity Access Management through User Behavioral Analytics and adaptive authentication at Zero Trust Architecture. AI-driven User Behavioral Analytics was singled out as one of the transformative tools in the continuous monitoring and analysis of user behavioral patterns. Through the use of ML algorithms, baseline activity metrics are set up: time of login, location, device attribute, and what resource he or she tried to access. It flags deviations from these baselines as potential anomalies, requiring further scrutiny and possible security actions. This proactive approach to anomaly detection greatly strengthens Identity Access Management within a Zero Trust context by allowing an organization in the USA to identify and address such threats in real time.