Knowledge Graph Framework Secures Large Language Models in Smart Grids

A knowledge-grounded security assessment framework for large language models deployed in smart-grid energy informatics achieved a Macro-Average F1 score of 94.6 percent, reduced risk quantification error by 18.7 percent, and improved scoring consistency by 12.4 percent compared with rule-based scoring, TextCNN, RoBERTa, and general-purpose LLM discrimination. According to research published in Energy Informatics, large language models are increasingly integrated into smart-grid information services for dispatching, inspection and maintenance, marketing, and regulatory supervision. However, these deployments introduce security risks associated with data leakage, unauthorized instructions, factual inconsistency, content compliance violations, and business misdirection.

To address these vulnerabilities, researchers developed a security assessment framework that integrates hierarchical risk indicators, power knowledge-graph evidence, permission-aware semantic entailment reasoning, and confidence calibration. Within this architecture, cross-domain interactions are represented using access subjects, resource attributes, generated responses, business constraints, and traceable evidence paths. The system processes these representations to produce calibrated risk probabilities, impact levels, and verification scores, thereby supporting traceable, permission-aware risk assessment.

Experimental evaluations were conducted across four power-business scenarios using desensitized interaction records and adversarial test samples. Ablation results demonstrated the specific contributions of evidence-chain aggregation and semantic entailment reasoning to overall security performance. Additionally, concurrent evaluation within a research and development simulation environment demonstrated an average verification latency of 143 milliseconds at 400 requests per second and achieved a verification success rate of 98.4 percent at 800 requests per second, establishing an engineering basis for secure model integration.

Subject of Research: Smart-grid large language model security assessment

Article Title: Secure large language model deployment in smart-grid energy informatics: knowledge-graph-driven risk quantification and cross-domain access verification

Article References: Tong, S., Zhang, B., Sun, H., Liu, Y., & Qiu, B. (2026). Secure large language model deployment in smart-grid energy informatics: knowledge-graph-driven risk quantification and cross-domain access verification. Energy Informatics. https://doi.org/10.1186/s42162-026-00700-2

Image Credits: AI Generated

DOI: 10.1186/s42162-026-00700-2

Keywords: smart grids, large language models, energy informatics, knowledge graphs, risk quantification, access verification, semantic entailment, power systems, Secure, large, language, model