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

Analysis

Why This Paper Matters

Critical National Infrastructures (CNIs) such as energy grids, water systems, transportation networks, and communication frameworks are foundational to modern society, yet they face escalating cybersecurity threats. This review paper addresses a timely and critical gap by comprehensively analyzing AI-driven approaches for Critical Infrastructure Protection (CIP). As cyberattacks grow in sophistication, traditional security measures often fall short, and there is a pressing need to leverage advanced AI techniques, particularly Generative AI and Large Language Models (LLMs), to enhance defense mechanisms. The paper's focus on trust, privacy, resilience, and securability aligns with the core challenges in securing these vital systems.

Moreover, the paper introduces established benchmarks for evaluating LLMs within cybersecurity contexts, which is essential for rigorous assessment and comparison of AI models. By exploring the role of Agentic AI for proactive defense, it pushes the frontier of how autonomous AI agents can be deployed to anticipate and mitigate threats. The synthesis of current challenges, benchmarking strategies, and innovative AI applications provides a strategic roadmap for researchers and practitioners, making it a valuable reference for those committed to fortifying national infrastructures.

Technical Contributions

• Comprehensive Review: The paper surveys AI-driven approaches for CIP, covering reliability of CNIs, cybersecurity issues, and the role of Generative AI and LLMs.
• Benchmarking Framework: It examines established benchmarks for evaluating LLMs in cybersecurity, providing a foundation for standardized assessment.
• Trust and Privacy Analysis: It delves into core cybersecurity issues—trust, privacy, resilience, and securability—within the context of critical infrastructures.
• Agentic AI for Proactive Defense: The paper presents insights on applying Agentic AI, where autonomous agents can proactively detect and respond to threats.
• Future Directions: It outlines a roadmap for integrating advanced AI methodologies into CIP, highlighting challenges and opportunities.

Results

As a review paper, it does not present new experimental results or quantitative metrics. Instead, it synthesizes existing knowledge and provides qualitative insights. The paper identifies key challenges and opportunities, and proposes a strategic roadmap. It discusses the potential of Generative AI and LLMs but does not offer empirical validation of their effectiveness in CIP scenarios. The lack of concrete metrics is a limitation, but the paper's value lies in its comprehensive overview and forward-looking perspective.

Significance

The paper's significance lies in its timely synthesis of AI applications for critical infrastructure protection, an area of growing importance. By highlighting the role of LLMs and Agentic AI, it bridges the gap between advanced AI research and practical cybersecurity needs. The discussion of benchmarks and trust issues informs the development of robust and reliable AI systems for high-stakes environments. The strategic roadmap can guide future research and policy-making, ultimately contributing to the resilience of national infrastructures against cyber threats. This review is a valuable resource for researchers, practitioners, and policymakers seeking to understand and leverage AI for CIP.