Operationalizing AI in Cloud-Native Systems: A DevSecOps Framework for Secure, Scalable, and Cost-Efficient Engineering
Bibliographic record
Abstract
Cloud-native architectures and artificial intelligence (AI) have reshaped software delivery, but their convergence creates challenges in security, scalability, and cost control. Current practices in MLOps, container orchestration, and DevSecOps address these concerns independently, leaving gaps when AI workloads must meet production requirements simultaneously. We present an AI-augmented DevSecOps framework integrating four pillars: AI-driven security in Continuous Integration/Continuous Deployment (CI/CD) pipelines, reinforcement learning–based autoscaling, cost-aware orchestration, and explainable compliance. Evaluation on Kubernetes with NVIDIA A100 GPUs demonstrates 23% latency reduction, 35% faster vulnerability detection, and 22% cost savings versus rule-based baselines. Contributions include: (i) joint optimization of security, performance, and cost; (ii) explainable decision mechanisms; (iii) minimal adoption pathway for resource-constrained teams. The framework addresses operationalization—moving AI from prototypes to governed production deployments—by bridging MLOps reproducibility, DevSecOps threat mitigation, and cloud-native elasticity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".