🤖 AI Summary
This study addresses the high cost and limited scalability of manual compliance audits under Germany’s IT-Grundschutz framework, which pose a significant burden on small and medium-sized enterprises. To partially automate the certification process—encompassing structural analysis, protection requirements assessment, modeling, and compliance verification—the authors propose a multi-agent system (MAS) integrated with a hybrid retrieval-augmented generation (HybridRAG) approach. The method innovatively incorporates a hypothesis-validation loop to mitigate agent hallucinations and employs a decoupled reasoning pipeline that separates semantic extraction from deterministic inheritance of protection requirements, thereby enhancing compliance rigor. Experimental results demonstrate a substantial reduction in human effort for semantic tasks; however, stages relying on deterministic logic remain constrained by the inherent probabilistic nature of large language models.
📝 Abstract
The NIS-2 Directive mandates robust Risk Management from thousands of small and medium enterprises. To ensure compliance, companies rely on established standards such as the German IT-Grundschutz (IT-GS) of the Federal Office for Information Security. However, IT-GS certification is resource-intensive and requires a high level of manual effort for documentation, validation, and revision, making scalable implementation difficult and expensive.
Building upon our previous conceptual framework, this paper presents the technical implementation and empirical evaluation of a Multi-Agent System (MAS) architecture combined with Hybrid Retrieval Augmented Generation (HybridRAG) for the partial automation of IT-GS certification. We introduce two novel technical contributions to the MAS architecture to enforce the compliance rigor. The Hypothesis-Verification Loop in the Structural Analysis (SA) phase that cross-references agent-inferred dependencies against the Knowledge Graph to reduce hallucinations, and a Decoupled Reasoning Pipeline that separates agent-driven semantic extraction from the deterministic protection need inheritance. We utilize the BSI's "RecPlast GmbH" case study as a human expert-generated reference data set for end-to-end evaluation of the architecture and to quantify Precision, Recall, and F1-scores. The performance of the system is investigated across the phases of SA, Protection Needs Assessment (PNA), Modeling, and IT-GS Check.
The empirical results reveal noticeable differences throughout the different steps of IT-GS. While the MAS demonstrates high efficacy in semantic tasks (SA and Modeling), significantly reducing manual effort through automated information extraction, quantitative results reveal limitations in logical reasoning phases (PNA and IT-GS Check) as the probabilistic nature of current LLMs struggles to meet the deterministic rigor required by IT-GS.