Bibliographic record
Abstract
This article presents an empirical study evaluating the effectiveness of the CyberSecurity Audit Model (CSAM 2.0) at a Canadian higher education institution. CSAM 2.0 is a comprehensive model used to assess cybersecurity assurance, maturity, and readiness in medium to large organizations and at the national level. It allows for the effective evaluation of security controls across various cybersecurity domains. The study highlights global best practices in cybersecurity audits, highlighting the lack of standardized guidelines and weaknesses in cybersecurity training programs. The paper details CSAM 2.0's structure and architecture, sharing results from three research scenarios: (1) a single audit focusing on awareness education, (2) audits in multiple domains such as governance, legal compliance, and incident management, and (3) a full audit covering all model domains. The study concludes that CSAM 2.0 offers valuable insights for improving cybersecurity practices and addressing vulnerabilities. Keywords: Cybersecurity, Cybersecurity Audits, Cybersecurity Audit Model, Cybersecurity Assurance, Cybersecurity Maturity, Control Evaluation, Risk Management, Incident Response, Cybersecurity Domains, Cybersecurity Training.
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 | 0.089 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".