Enhancing U.S. National Security through AI-Driven Predictive Analytics for Cyber Threat Detection in Critical Infrastructure

Abstract

The heightened interconnectivity of the U.S. critical infrastructure, including energy, transportation, financial, and communication systems, has enhanced the nation’s vulnerability to advanced and dynamic cyber threats. The traditional cybersecurity controls, despite being useful, have been discovered to be insufficient due to the emerging elements of sophistication and power of adversary attacks on core systems. Artificial Intelligence (AI) presents a breakthrough in the sphere of national defense because it is likely to result in the development of predictive analytics that would identify, analyze, and react to any potential cyber-attacks in their initial stage. The paper discusses the potential of predictive analytics based on artificial intelligence to support the U.S. national security by making cyber threats in critical infrastructures more detectable and resilient. The study highlights the superiority of AI algorithms, particularly machine learning and deep learning models, over conventional systems in terms of accuracy, speed, and flexibility through three methods: an integrated literature review, model-based analysis, and comparative evaluation. According to the results, predictive analytics will be able to play the role of enhanced creation of situational awareness, reduction of false positives, and proactive risk mitigation. Another aspect that is highlighted by the paper is the policy and ethical imperatives of AI integration, and the subsequent fact of governance, data transparency, and interagency cooperation can be viewed as one of the success criteria. It concludes that AI in national security can be implemented in a sustainable manner by means of a middle-ground solution that balances technological innovation and regulation governing its application, ensuring resilience without eroding civil liberties.