Technology-based Practical Blockchain System Audit Maturity Model
Bibliographic record
Abstract
Information system auditing can reveal the quality of such systems, and standard audit items are crucial elements of system and audit quality. Blockchain technology is currently being applied to various areas including the financial, manufacturing, healthcare, distribution, and public sectors, and an increasing number of systems that apply such technologies are also being developed.The current audit model is insufficient for application in the field, and the auditing of systems applying new technologies, such as blockchain, has not been given sufficient attention. Furthermore, it is difficult to evaluate the relative levels of audited systems using audit results. Existing studies have only examined the auditing of systems that apply blockchain. Although the Korea Association of Information Systems Audit has suggested a checklist for systems applying blockchain, it has yet to be adopted. To address this problem, 50 existing audit result reports and technical data were collected, from which sixteen factors of four audit quality properties consisting of blockchain system, technology compliance, software quality, and document were derived. Furthermore, an audit maturity model was presented after evaluating the priorities of the 16 derived factors. The results of the evaluation of the priorities of audit items indicated that auditors give a higher importance to technology-based than document-based audits of information systems. This study contributes to the literature by deriving field-oriented audit items including blockchain technology, thus enabling practical audits to be conducted in a short time. Further, this study enables the maturity of systems to be compared based on audit results by presenting audit maturity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".