National Resilience Through Enterprise Security: The Role Of Zero Trust In Protecting Ai-Driven Critical Infrastructure In The United States

Abstract

The evolving threat landscape of the 21st century has witnessed critical infrastructure systems throughout the United States increasingly dependent on Artificial Intelligence (AI) and Machine Learning (ML) technologies. These technologies optimize performance across essential sectors, yet introduce fresh vulnerabilities to sophisticated cyberattacks and data extraction. This article examines the implementation of Zero Trust Security Architecture (ZTSA) for AI/ML workloads within critical infrastructure environments through semi-structured interviews with cybersecurity professionals and document analysis across energy, healthcare, and financial sectors. Results reveal significant reductions in threat detection time and unauthorized access attempts following ZTSA implementation. Organizations with comprehensive monitoring detected substantially more potential threats than those with partial coverage. Distinct security patterns emerge, with energy sectors favoring segmentation-based approaches and healthcare prioritizing identity-centric models. The synthesis of industry implementations, regulatory directions, and architectural approaches yields a framework for protecting critical AI systems, strengthening national infrastructure resilience against emerging threats. This comprehensive security framework not only addresses current vulnerabilities in AI-driven infrastructure but also establishes a sustainable foundation for ongoing security evolution, ensuring that critical systems remain protected as both AI capabilities and threat vectors continue to advance in sophistication and scope.