MART 23: A Tool to Audit Information Technology Risk Management Maturity
Bibliographic record
Abstract
In recent years and due to different crises (financial crises, epidemic crises, politic crise), organizations have turned their attention to searching best practices in order to better manage inherent risks.Actually, every organization is now obliged to take risks so as to grow and even to survive.Under these conditions, it is vital to correctly manage potential risks to the business, otherwise, if these risks occur, organizations may not be able to reach their objectives.From another side, all businesses rely on information technology so its related risks should be well managed.Consequently, and to audit the maturity of information technology risk management (ITRM), we developed a system named MART 23, built on using best practices of COBIT 5.In fact, COBIT 5 like other standards presents some guidelines for risk management / information technology risk management, but none of them offer an operational approach and tool for auditing, assessing and improving ITRM maturity in organizations.In the following article, the MART 23 system is presented to audit ITRM maturity, through UML design and some layouts.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".