用多大模型+知识图谱辅助德国网络安全认证，提效降本。
An Approach for a Supporting Multi-LLM System for Automated Certification Based on the German IT-Grundschutz
- 构建多大模型系统融合知识图谱，分阶段支持认证流程。
- 可应对NIS2新规下认证需求激增与专业人才短缺问题。
- 适合需合规认证的中小企业及安全咨询机构使用。
本文提出一种基于德国IT-Grundschutz的半自动化认证新方法，采用多大语言模型系统（MLS）结合混合检索增强生成（HybridRAG）。面对网络与信息安全指令2（NIS2）带来的挑战、专业人才短缺及高昂实施成本，该系统旨在提升效率、降低成本，并在满足新增企业认证需求的同时保障安全方案质量。架构整合大语言模型（LLMs）与知识图谱（KGs），支持认证全过程，包括保护需求评估、建模、IT-Grundschutz检查、措施整合及后续实施。该方法有效应对日益增长的安全方案需求，为应对NIS2引入的数字安全挑战提供可行路径。
原文摘要 · Abstract (English)
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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