AI-Driven Zero Trust Security Architecture For Protecting U.S. Critical Infrastructure

The digitalization of critical infrastructure sectors of the U.S. economy such as energy, transportation, healthcare, and defense has expanded the cyber-attack surface at a rapid pace. The old models of perimeter-based security are no longer effective against complex attacks, like advanced persistent attacks (APTs), insider attacks and AI-assisted cyber-attacks. This paper will suggest AI-based Zero Trust Security Architecture (ZTSA) adapted to secure the critical infrastructure in the United States. The framework incorporates behavioral analytics, federated learning, and adaptive risk scoring, that allow one to continue verification and intelligent response to threats. The predictive and generative AI models are utilized to simulate the attack scenario, improve anomaly detection, and automate policy enforcement. Experimental assessment based on simulated critical infrastructure datasets is shown to have a higher detection rate of 95.8 and a 30% lower rate of false positives than traditional zero-trust systems. The outcomes show that AI-enhanced zero-trust models will be capable of mitigating critical infrastructure in the US to a considerably greater extent in terms of resilience, scalability, and mitigation of threats in real-time.