Integrating Security in Cloud Application Development Cycle
Bibliographic record
Abstract
Nowadays, more and more business and individuals tune to Software-as-a-Service (SaaS) applications to rapidly access various software capabilities through the Internet. The more SaaS adoption evolves, the more software service providers compete for fast development to cope with the market pace. This trend pushes security after functionality-needs in the priority list. This, in turn, results in delivering applications with potential security risk. The risk is further elevated due to the lack of visibility, control, and regulatory enforcements over consumers' data associated with such applications. Motivated by the raised necessity to consider security-needs at the same priority as functionality-needs, this paper proposes a comprehensive platform to interweave security activities and services from inception through deployment and beyond. Such activities and services are based on information flow control. The platform specifically envisions these activities to devote security into every phase of the development lifecycle of SaaS applications and offer different style of defenses as security services. It promotes for shared security responsibility to gain twofold benefits: a) it helps service providers to protect their SaaS applications from prevalent security threats; b) it enables SaaS consumers to choose a protected application to process their sensitive data with a trust in its security.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".