Knowledge Management for Cybersecurity in Business Organizations: A Case Study
Bibliographic record
Abstract
Knowledge management (KM) plays important roles in cybersecurity. This study collects five real-life cases of good practices of KM for the domain of cybersecurity in business organizations. Through an iterative process of team-based qualitative data analysis of the five cases, the study develops a model of KM for cybersecurity that conceptualizes three common aspects of KM practices for cybersecurity in business organizations. First, KM for cybersecurity in business organizations has clear specialized organizational structures that involve three inter-organizational tiers across the organization boundaries. Second, the knowledge flows of KM for cybersecurity in business organizations emphasize on explicit, declarative, and specific knowledge. Third, in comparison with KM for other domains, KM for cybersecurity has well-defined objective measures to assess the effectiveness of KM. The domain-specific KM model based on the good practice cases provides a road-map for KM practices in the domain of cybersecurity in business organizations.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".