Conceptualizing the Secure Machine Learning Operations (SecMLOps) Paradigm
Bibliographic record
Abstract
Due to the proliferation of machine learning in various domains and applications, Machine Learning Operations (MLOps) was created to improve efficiency and adaptability by automating and operationalizing ML products. Because many machine learning application domains demand high levels of assurance, security has become a top priority and necessity to be involved at the beginning of ML system design. To provide theoretical guidance, we first introduce the Secure Machine Learning Operations (SecMLOps) paradigm, which extends MLOps with security considerations. We use the People, Processes, Technology, Governance and Compliance (PPTGC) framework to conceptualize SecMLOps, and to discuss challenges in adopting SecMLOps in practice. Since ML systems are often multi-concerned, analysis on how the adoption of SecMLOps impacts other system qualities, such as fairness, explainability, reliability, safety, and sustainability are provided. This paper aims to provide guidance and a research roadmap for ML researchers and organizational-level practitioners towards secure, reliable, and trustworthy MLOps.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".