Common Methods for Security Risk Analysis
Bibliographic record
Abstract
This document is the result of a study conducted to document the state of Canadian risk management. The study provides a history of Canada's initiatives with respect to risk management and investigates how Canada can augment the Working Group with its experiences and its future initiatives and opportunities. In addition, the study presents a comparison between the prevalent Canadian threat and risk assessment methodology (ITSG 04) and the recommendations of the National Institute of Standards and Technology Risk Management Guide for Information Technology Systems (NIST 800-30). Substantial evolution of risk management has occurred in the past few years, but the tools and documentation have been a significant impediment on further development. There is a definite need to standardize the TRA process and provide system owners with a useful and consistent tool to evaluate the risks to information and IT systems. The approach to a common framework is emphasized by the need for a common language. The provision of a shared set of concepts and vocabulary can only help unify the disparate terminologies that variant TRA approaches and methodologies have engendered. Equally valuable is the prospective TRA automation or partial automation. Automated tools were premature in the early days when risk management was first introduced. Practitioners have gained expertise and experience in the conduct of TRA. It is recognized that human intervention will most likely be required in any automated TRA, however partial automation may be an initial step toward a common framework.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".