Guidelines for designing IT security management tools
Bibliographic record
Abstract
An important factor that impacts the effectiveness of security systems within an organization is the usability of security management tools. In this paper, we present a survey of design guidelines for such tools. We gathered guidelines and recommendations related to IT security management tools from the literature as well as from our own prior studies of IT security management. We categorized and combined these into a set of high level guidelines and identified the relationships between the guidelines and challenges in IT security management. We also illustrated the need for the guidelines, where possible, with quotes from additional interviews with five security practitioners. Our framework of guidelines can be used by those developing IT security tools, as well as by practitioners and managers evaluating tools.
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 | 0.065 | 0.127 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".