Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and...

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Critical National Infrastructure Protection

Background:

• Critical National Infrastructures (CNIs) are vital for society but vulnerable to cyber threats.
• Existing cybersecurity measures require enhancement to address sophisticated threats.
• The reliability and security of CNIs are paramount for national stability.

Purpose of the Study:

• To comprehensively analyze AI-driven approaches for Critical Infrastructure Protection (CIP).
• To evaluate the role of Large Language Models (LLMs) and Generative AI in enhancing CIP.
• To provide a strategic roadmap for integrating advanced AI into national infrastructure security.

Main Methods:

• Review of current AI methodologies applied to cybersecurity.
• Examination of benchmarks for evaluating Large Language Models (LLMs) in cybersecurity.
• Analysis of core cybersecurity issues: trust, privacy, resilience, and securability in CNIs.

Main Results:

• Generative AI and LLMs show significant potential in bolstering CIP.
• Agentic AI offers promising avenues for proactive defense mechanisms.
• Established benchmarks are crucial for assessing AI tool reliability in cybersecurity.

Conclusions:

• Advanced AI methodologies are essential for fortifying national infrastructures against emerging cyber threats.
• A strategic integration of AI, including LLMs and Agentic AI, is recommended for future CIP.
• Further research is needed to guide the effective implementation of AI in protecting critical national infrastructures.