A Large Language Model-Assisted Protocol for Metadata Classification and Governance in Electric Power Information Systems

Electric power organizations must classify heterogeneous data assets before applying access, retention, and cybersecurity controls. This study presents a metadata-only protocol that combines a fixed large language model (LLM) prompt, structured JSON output, deterministic screening, and mandatory human adjudication across five governance classes. The protocol was evaluated on 240 metadata records from 10 business domains. Raw LLM predictions agreed with adjudicated human labels for 207 records (86.25%; macro-F1 = 0.827), outperforming an exploratory deterministic rule baseline (66.67%) and a majority-class baseline (41.25%). Eighty-two records (34.17%) were routed to manual review; after adjudication, all 82 matched the reference labels. Five unsafe downgrades involved Critical Operational records and were intercepted by the review pathway. The workflow is limited to metadata and does not process raw documents, full tables, telemetry, or logs. These findings support LLM use as a triage aid rather than an autonomous classifier for metadata in electric power data governance.