New Validation of a Cybersecurity Model to Audit the Cybersecurity Program in a Canadian Higher Education Institution
Bibliographic record
Abstract
This article presents the results of one empirical study that evaluated the validation of the CyberSecurity Audit Model (CSAM) for the second time in a different Canadian higher education institution. CSAM is utilized for conducting cybersecurity audits in medium or large organizations or a Nation State to evaluate and measure cybersecurity assurance, maturity, and cyber readiness. The authors review best practices and methodologies of global leaders in the cybersecurity assurance and audit arena, that puts in evidence the lack of universal guidelines to conduct extensive cybersecurity audits and the detection of existing weaknesses in general programs to deliver cybersecurity awareness training. The architecture of CSAM is described in central sections. CSAM has been tested, implemented, and validated in three research scenarios (1) a single cybersecurity domain audit (Awareness Education), (2) Cybersecurity audit of several domains (Governance and Strategy, Legal and compliance, Cyber Risks, Frameworks and Regulations, Incident Management, Cyber Insurance and Evolving Technologies) and (3) Cybersecurity audit of all model domains The study concludes by showing how the validation of the model allows to report significant information for future decision making that the target organization may correct cybersecurity weaknesses or to improve cybersecurity domains and controls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".