Military power projection depends on resilient energy infrastructure, yet the grids supporting the United States and allied forces are increasingly vulnerable to cyberattacks. This article examines how intrusions against national power systems and on-base microgrids threaten operational continuity. Through illustrative case studies of Germany, Japan, and the United States, it identifies recurring weaknesses in both legacy and modern grid architectures and demonstrates the insufficiency of current defensive measures. As a solution, the paper evaluates the use of Large Language Models (LLMs) for a more adaptive cyber early warning system (CEWS). Drawing on experiments from the NATO Systems Analysis and Studies (SAS-183) project, it presents findings from tests using LLMs to analyze real-world energy-system data. The results confirm that Artificial Intelligence (AI) can significantly improve anomaly detection and threat contextualization. However, the article cautions that without secure, human-supervised architectures, these same systems introduce risks of high-consequence false positives and adversarial manipulation. Ultimately, this research concludes that AI enhances energy resilience only when its computational speed is balanced by disciplined human judgment.
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doi.org/10.55682/cdr/hgwr-bfg2
The Cyber Defense Review
Volume 11, Issue 1