Evaluation of cloud computing risks using an integrated fuzzy-ANP and FMEA approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the considerable benefits of cloud-based services and their effect on the reduction of total investments in information technology (IT) infrastructures, there still exist a plethora of concerns regarding the potential risk of this relatively new method of resource outsourcing. Due to the diversity of activities in the risk management process, it is essential to develop an innovative framework for controlling and streamlining relevant processes. Such processes include the identification, ranking, and determination of relevant exposure strategies and risk responsiveness strategies. The proposed framework developed in this study was based on the risk management process phase of the PMBOK model to analyse the collected data via fuzzy analytical network processing and failure mode effective analysis methods. A survey was then drafted with participating IT experts. Results demonstrate that the three most important risks are data confidentiality, data integrity and reliability. Furthermore, 117 risk-responsiveness solutions such as auditing the scope of access to information, using relevant techniques to control data integrity, and implementing appropriate training programs for the support team within the organisation were recognised and ranked to suggest the most appropriate remedial strategies that extensively mitigate against identified 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.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it