Critical Infrastructure Protection: Generative AI, Challenges, and Opportunities

TL;DR

This paper addresses the cybersecurity vulnerabilities of Critical National Infrastructure and proposes a comprehensive CIP framework that leverages Generative AI and Large Language Models (LLMs). It details a CIP-focused LLM lifecycle and presents concrete applications across energy, water, and transport sectors, including predictive threat intelligence and automated incident response. The work also surveys reliability methods (Weibull, Markov, Monte Carlo) alongside cybersecurity challenges, privacy/trust considerations, and the compatibility of safety and security analyses. It concludes with forward-looking directions such as digital twins, quantum-resistant cryptography, and AR-supported operator interfaces to enhance resilience and secure critical services in practice.

Abstract

Critical National Infrastructure (CNI) encompasses a nation's essential assets that are fundamental to the operation of society and the economy, ensuring the provision of vital utilities such as energy, water, transportation, and communication. Nevertheless, growing cybersecurity threats targeting these infrastructures can potentially interfere with operations and seriously risk national security and public safety. In this paper, we examine the intricate issues raised by cybersecurity risks to vital infrastructure, highlighting these systems' vulnerability to different types of cyberattacks. We analyse the significance of trust, privacy, and resilience for Critical Infrastructure Protection (CIP), examining the diverse standards and regulations to manage these domains. We also scrutinise the co-analysis of safety and security, offering innovative approaches for their integration and emphasising the interdependence between these fields. Furthermore, we introduce a comprehensive method for CIP leveraging Generative AI and Large Language Models (LLMs), giving a tailored lifecycle and discussing specific applications across different critical infrastructure sectors. Lastly, we discuss potential future directions that promise to enhance the security and resilience of critical infrastructures. This paper proposes innovative strategies for CIP from evolving attacks and enhances comprehension of cybersecurity concerns related to critical infrastructure.

Figures (3)

• Figure 1: A Markov Chain of an MS/DR system
• Figure 2: The steps of Generative AI and LLM lifecycle for critical infrastructure protection.
• Figure 3: Instruction fine-tuning LLM models for Critical Infrastructure Protection.