DevSecOps Sentinel: GenAI-Driven Agentic Workflows for Comprehensive Supply Chain Security
Bibliographic record
Abstract
A growing number of security challenges are born out of the complexity of modern software supply chains that span microservices, containerization, and cloud-native architectures. The increasing rate of new cyber-threats, and the need to quickly deploy software updates after a security incident, typically outpaces traditional DevSecOps security practices. In this paper, we propose a novel DevSecOps Sentinel system, which employs Generative AI (GenAI) driven agentic workflows to improve software supply chain security holistically. In this paper, we elaborate on the architecture of DevSecOps Sentinel: by integrating cutting-edge GenAI models, and by deploying intelligent agentic workflows. Then we dive into how the system impacts our software development life cycle from code writing to production and beyond. Our results indicate that agentic workflows powered by GenAI are a viable method to tackle the intricate security issues of modern software supply chains. Integrating the analysis capability of AI and marrying this with the strengths that come from agentic systems, DevSecOps Sentinel reveals a way forward for organizations seeking to strengthen their security profile in an ever more hostile digital world - to build better software — faster, safer, and reliable.
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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 | 0.005 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".