Using Fault Tree Analysis with Cobit 5 Risk Scenarios
Bibliographic record
Abstract
Information System Audit and Control Association (ISACA) proposed a preliminary idea on applying fault tree analysis to look at the root reasons for the IT risks outlined in COBIT 5 Risk Scenarios. So far, there was no prescriptive procedure/methodology, which could be used to build the fault tree. This research looked into various methodologies for building the fault tree and proposed a new methodology, which could be used for analysis of risks outlined in COBIT 5 Risk Scenarios document. The methodology has been developed specific to COBIT 5 processes to build the fault tree, which, in turn, can help to outline the common factors that lead to failure of the processes subsequently leading to a risk. Fault tree analysis, could help to improve processes and suggest potential mitigation strategy to improve management/governance of IT. The paper also includes a sample of using the proposed methodology on one of the risk scenarios in order to calculate minimal cut set of IT management practices that organization needs to focus on to address specific risks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".