Security excellence from a total quality management approach
Bibliographic record
Abstract
This paper focuses on the synergy of business and security requirements to create a holistic methodology or approach. The integration revolves around the concept of total quality management to measure the security posture and is based on the premise that security requirements must be aligned and fused with the business' objectives. The postulated security methodology has extended the total quality management and business excellence philosophies to create a new security excellence approach. The American National Institute of Standards and Technology's metrics are used as benchmarks to determine the security areas that should be addressed while the European Framework for Quality Management is used to reflect the integration with the National Institute of Standards and Technology's metrics and to represent the domains in a business excellence approach. The fusion is then extended to the Control Objectives for Information and Related Technology and, finally, to the international Standard ISO/IEC 17799 (Information technology – security techniques – Code of practice for information security management) to depict the merger between security and business domains along a TQM approach and to be transferable to any standard or regulation by being able to incorporate acceptable security requirements into the underlying framework.
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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".