Paper Detail

An Approach for a Supporting Multi-LLM System for Automated Certification Based on the German IT-Grundschutz

Lea Roxanne Muth, Marian Margraf

arxiv Score 11.2

Published 2026-06-24 · First seen 2026-06-25

Research Track A · General AI

Abstract

This paper presents a novel approach to perform semi-automated BSI IT-Grundschutz certification using a MultiLarge Language Model system (MLS) with Hybrid RetrievalAugmented Generation (HybridRAG). Facing the challenges of the Network and Information Security Directive 2 (NIS2) directive, a shortage of specialists, and high implementation costs, our MLS architecture aims to increase efficiency, reduce costs, and support certifiers in maintaining the quality of security concepts while meeting the increased demand for certifications of newly affected companies. The system combines Large Language Models (LLMs) and Knowledge Graphs (KGs) to support different phases of the certification process, including protection needs assessment, modeling, IT-Grundschutz check, measure consolidation, and subsequent realization. Our architecture addresses the growing demand for security concepts and offers an approach to handle the digital security challenges introduced by NIS2.

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BibTeX

@article{muth2026approach,
  title = {An Approach for a Supporting Multi-LLM System for Automated Certification Based on the German IT-Grundschutz},
  author = {Lea Roxanne Muth and Marian Margraf},
  year = {2026},
  abstract = {This paper presents a novel approach to perform semi-automated BSI IT-Grundschutz certification using a MultiLarge Language Model system (MLS) with Hybrid RetrievalAugmented Generation (HybridRAG). Facing the challenges of the Network and Information Security Directive 2 (NIS2) directive, a shortage of specialists, and high implementation costs, our MLS architecture aims to increase efficiency, reduce costs, and support certifiers in maintaining the quality of security concepts while meeting the},
  url = {https://arxiv.org/abs/2606.25608},
  keywords = {cs.CR, cs.AI},
  eprint = {2606.25608},
  archiveprefix = {arXiv},
}

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