The CyberSecurity Audit Model (CSAM)
Bibliographic record
Abstract
This chapter presents the outcome of two empirical research studies that assess the implementation and validation of the cybersecurity audit model (CSAM), designed as a multiple-case study in two different Canadian higher education institution. CSAM can be applied for undertaking cybersecurity audits in any organization or nation state in order to evaluate and measure the cybersecurity assurance, maturity, and cyber readiness. The architecture of CSAM is explained in central sections. CSAM has been examined, implemented, and established under three research scenarios: (1) cybersecurity audit of all model domains, (2) cybersecurity audit of numerous domains, and (3) a single cybersecurity domain audit. The chapter concludes by showing how the implementation of the model permits one to report relevant information for future decision making in order to correct cybersecurity weaknesses or to improve cybersecurity domains and controls; thus, the model can be implemented and sufficiently tested at any organization.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".