Article

Design and Evaluation of a Secure Air-Gapped LLM-Based Intelligence Analysis Framework

2026-03-04

Abstract excerpt

<title>Abstract</title> <p>The increasing reliance on heterogeneous and high-volume data sources in security-sensitive analytical en- vironments has amplified the cognitive burden placed on human analysts. While large language models (LLMs) demonstrate strong capabilities in semantic reasoning and multi-document synthesis, their deployment in regu- lated and air-gapped settings is constrained by data sovereignty...

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Literature Corpus work: bb5bf97a-7038-5bab-b718-fce084a483d6
DOI: 10.21203/rs.3.rs-9016380/v1

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