Edge-to-Cloud DevSecOps Workflows: End-to-End Compliance Automation for Enterprise Data Engineering
Bibliographic record
Abstract
Contemporary businesses are shifting more towards distributed edge-to-cloud architectures to satisfy the high scalability, real-time intelligence and secure operational continuity needs. But when data pipelines cut across infrastructures of a heterogeneous nature, the process of ensuring continuous security and regulatory compliance becomes very complex. The paper introduces a new Edge-to-Cloud DevSecOps model, which brings automated compliance enforcement as a part of enterprise data engineering processes. The proposed architecture integrates security-as-code, security-policy-driven orchestration, and continuous monitoring between stages of data ingestion, transformation, storage, and deployment. With the help of zero-trust and digital policy twins, as well as AI-based vulnerability analytics, the system imposes end-to-end controls, which meet the requirements of GDPR, HIPAA, PCI-DSS, and ISO 27001. Other features included in the framework are federated logging, secure CI/CD pipelines, and real-time threat response based on adaptive risk scoring. Multi-cloud edge experimental analysis shows a 42 percent decrease in compliance validation time, 36 percent enhancement of policy enforcement accuracy, and a big decrease in Mean Time to Remediation (MTTR). The solution under proposal enhances the governance, lowers the overheads of operations and quickens the provision of secure data-driven service in the enterprise settings. In general, this paper brings DevSecOps automation and resilient, compliant, and scalable edge-to-cloud data engineering.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".