Leveraging Generative AI And Zero Trust Countermeasures In Critical Infrastructure: From Vulnerability To Resilience

Abstract

Generative artificial intelligence (GenAI) has had a transformative effect on the automation and decision-making responsibilities in numerous industry sectors, including healthcare, industrial control systems, and finance. However, the rapid expansion of GenAI has inherently added complexity to the global cybersecurity threat landscape. The objective of this research is to investigate how adversaries are leveraging the capabilities of GenAI to conduct scalable fraud, manipulate healthcare data, and subject security-sensitive operational processes to exploit. In order to support the objective, a qualitative analysis is performed that also utilizes sector-based risk mapping, adversarial scenario modeling, and safety system framework validation. The research found that continuous identity verification, context-aware Zero Trust controls, Human-in-the-Loop (HITL) monitoring, and data provenance assurance significantly mitigate vulnerabilities associated with natural-language processing (GenAI) systems. These contributions are significant for lawmakers, cybersecurity architects, and organizational leaders who want to construct reliable artificial intelligence governance to support critical infrastructure systems.