Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes
arXiv SecurityArchived Aug 04, 2026✓ Full text saved
arXiv:2608.00698v1 Announce Type: new Abstract: We propose the SECUMAN ontology and shapes for representing and analysing cybersecurity risk-management documentation for medical devices. Cybersecurity risks are increasingly relevant for connected medical devices and may have direct consequences for patient safety. Current risk-management files are often maintained as semi-structured natural language text, which makes consistency checking, certification review, and reuse difficult. SECUMAN provid
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 1 Aug 2026]
Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes
Martin Diller, Anne Esslinger, Piotr Gorczyca, Evi Hartig, Lia Kacholdt, Hannes Strass
We propose the SECUMAN ontology and shapes for representing and analysing cybersecurity risk-management documentation for medical devices. Cybersecurity risks are increasingly relevant for connected medical devices and may have direct consequences for patient safety. Current risk-management files are often maintained as semi-structured natural language text, which makes consistency checking, certification review, and reuse difficult. SECUMAN provides a formal OWL-based vocabulary for modelling security-risk context, assessment, control measures, and residual-risk evaluation, and uses SHACL constraints to check structural completeness and conformity with the intended documentation model. The ontology is aligned with VDE Spec 90025 and the related RISKMAN ontology and shapes, while extending their safety-oriented approach to concepts relevant to cybersecurity risk documentation such as threat scenarios, protection goals, attacker profiles, exposure levels, assets, and secure design arguments. SECUMAN is intended to support automated first-pass validation, traceability, and integration of cybersecurity and safety risk-management documentation.
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.00698 [cs.CR]
(or arXiv:2608.00698v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2608.00698
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From: Martin Diller [view email]
[v1] Sat, 1 Aug 2026 14:55:48 UTC (543 KB)
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