A Risk-Based Framework to Inform Prioritisation of Security Investment for Insider Threats
Bibliographic record
Abstract
Threats to information security from inside an organisation are difficult to manage as insiders, by definition, have legitimate access to the organisation's information, consistent with their roles.Impacts of insider threats range from minor information compromise perhaps through carelessness, to catastrophic financial and reputational damage.Security managers are required to continually upgrade security measures to reduce the risk posed by insider threats, however with so many security controls to choose from, finding optimal security solutions based on benefit-cost is challenging.We have developed a risk-based framework called Security-in-Depth (SiD) where residual risk is the metric that assists the security manager to make informed decisions on which security packages contribute more to the organisation's security objectives.We present a case study to illustrate the way our framework is applied, customised to manage a range of insider threats.Uncertainties about the future threat spectrum and the future effectiveness of controls are included in the framework to inform the decisionmaking process.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".