D2.2 Common Certfication Model and Language Definiton
Bibliographic record
Abstract
This deliverable presents the finalized specification for the Common Certification Model (CCM) and the Com-mon Certification Language (CCL) developed within the COBALT project, which aims to establish a unifiedcybersecurity certification approach for diverse industrial and technological domains. As part of Task T2.2 un-der Work Package 2, the document builds upon the interim version by refining the CCM and CCL definitions,enhancing their integration into the COBALT framework.The core objective is to create a standardized, interoperable, and scalable cybersecurity certification methodol-ogy that transcends sectoral boundaries, addressing challenges in Industry 4.0 (I4.0), Quantum Computing,Cloud environments, and other emerging ICT infrastructures. To this end, the deliverable consolidates: A review of current certification models and standards such as ISO/IEC 27001, NIST RMF, andEUCS. The COBALT certification strategy, based on dynamic, digital-twin-supported assessment. A shared cybersecurity information schema derived from open standards like OSCAL, BOMs(SBOMs, HBOMs), and MUD profiles. Key developments in this deliverable include: A detailed design of the CCM Manager, covering its architecture, API endpoints, user interfaces, andits role in automating certification workflows. A description of COBALT ontologies, such as the Target of Evaluation (ToE) and Certification De-scriptors, which provide machine-readable, semantically enriched representations of cybersecurity re-quirements and assessment results. Integration pathways for real-time conformity assessment and evidence handling, including dynamicupdates via Digital Twins and decentralized data sharing models. Mapping of CCM Manager API and flows with COBALT enablers like the Security Digital Twin Man-ager, Certificate Manager, and Clouditor, forming an operational toolkit to enforce certification pro-cesses. This deliverable positions CCM and CCL as foundational to achieving cross-sector interoperability, continuouscertification, and trusted automation in cybersecurity compliance processes. It provides the technical and con-ceptual groundwork to support future integration across COBALT’s full stack, offering a sustainable, extensiblemodel for EU-wide cybersecurity certification
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".