CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Aug 04, 2026

Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes

arXiv Security Archived 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

Full text archived locally
✦ 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 Focus to learn more Submission history From: Martin Diller [view email] [v1] Sat, 1 Aug 2026 14:55:48 UTC (543 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 04, 2026
    Archived
    Aug 04, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗