A Hierarchical Security-Auditing Methodology for Cloud Computing
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
Security concerns are frequently mentioned among the reasons why organizations hesitate to adopt cloud computing. Given the numerous choices of cloud-resource providers, clients often find it difficult to assess their relative advantages and shortcomings with respect to security, which may prevent them from making any choice. In this paper, we describe our methodology for a hierarchical security-audit method for cloud-computing services. Our method examines the overall security of the cloud offering, based on the examination of a comprehensive set of security concerns at the IaaS, PaaS, and SaaS layers. For each layer, relevant evidence regarding its security is collected and subsequently synthesized into an overall security score. We illustrate our method through a case study, examining the relative security merits of the Google Cloud and the Microsoft Azure Cloud.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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