Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts
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Abstract (arXiv)
The NIS-2 Directive increases the need for continuous, auditable compliance evidence and motivates a shift from document-based compliance toward machine-readable compliance artifacts. The Open Security Controls Assessment Language (OSCAL) is a standard for this purpose, which the German Federal Office for Information Security (BSI) is adapting with Grundschutz++. However, companies are still managing extensive legacy IT security concepts (IT-SCs), and migrating them without verification could transfer outdated assets into the new format. While existing research primarily addresses the generation of new concepts, there is a lack of a verification framework that extracts legacy IT-SCs into an auditable intermediate representation, deterministically compares the extracted graph with an independently constructed reference state, and exports schema-valid OSCAL artifacts. This paper introduces the Automated Security Concept Structure Extraction and Reverse Topology-checking (ASSERT) Framework, which addresses this gap by using ontology-based extraction of legacy documents into formal document graphs, a five-class graph difference against a verified reference graph, and the export into schema-valid OSCAL outputs for system description and assessment evidence. Using the BSI's RecPlast dataset, we compare a local open-weight model and a commercial model across three configurations with different levels of reference-ontology exposure. The evaluation shows that ASSERT makes document-infrastructure inconsistencies measurable, but reveals a trade-off between discovering undocumented entities and enforcing a schema.
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References · 13
- 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
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- 2025GraphCompliance: Structure-Aware Regulatory Compliance through Policy-Context Graph Alignmentcited by 9
- 2024From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Modelscited by 7
- 2025Bench-2-CoP: Can We Trust Benchmarking for EU AI Compliance?cited by 6
- 2025RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QAcited by 6
- 2023A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graphcited by 4
- 2026Neuro-Symbolic Compliance: Integrating LLMs and SMT Solvers for Automated Financial Legal Analysiscited by 3
- 2024ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysiscited by 2
- 2026ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detectioncited by 2
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