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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".