Weathering the storm: examining how organisations navigate the sea of cybersecurity regulations
Bibliographic record
Abstract
Governments around the world routinely regulate the activities of private enterprises to guide the behaviour of individuals and organisations towards acceptable norms. This holds true in a cybersecurity context. However, practitioners report that cybersecurity regulations are often out of date and compliance is confusing, expensive, and time consuming. As a result, organisational leaders are often uncertain about the practicalities of adopting and implementing the various rules, which can lead to trickle-down effects on the robustness of lower-level cybersecurity controls and compliance activities. In this research, we aim to clarify how cybersecurity regulations are operationalised in organisations, as well as reveal the compliance and performance consequences of cybersecurity regulations. To do so, we interviewed 22 senior leaders with expertise in cybersecurity regulations. Our analysis reveals 7 distinct themes (i.e., concept groupings) that are ordered within four phases (i.e., temporal stages), which we use to create the Institutional Cybersecurity Regulations Model (ICRM). The results provide a holistic view of the cybersecurity regulations process in organisations that can serve to clarify current theory relationships and inform future research. As well, the ICRM can provide a practical roadmap for managers to navigate regulatory cybersecurity challenges in their own companies.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".