SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle
- DOI
- 10.1007/s10664-025-10795-y
- Published
- 2026-02-11
- Container
- Empirical Software Engineering
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10664-025-10795-y,
title = {SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle},
author = {Xinrui Zhang and Pincan Zhao and Jason Jaskolka and Heng Li and Rongxing Lu},
year = {2026},
journal = {Empirical Software Engineering},
doi = {10.1007/s10664-025-10795-y},
url = {https://doi.org/10.1007/s10664-025-10795-y}
}RIS
TY - JOUR TI - SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle AU - Xinrui Zhang AU - Pincan Zhao AU - Jason Jaskolka AU - Heng Li AU - Rongxing Lu PY - 2026 JO - Empirical Software Engineering DO - 10.1007/s10664-025-10795-y UR - https://doi.org/10.1007/s10664-025-10795-y ER -
APA
Zhang, X., Zhao, P., Jaskolka, J., Li, H., & Lu, R. (2026). SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle. Empirical Software Engineering. https://doi.org/10.1007/s10664-025-10795-y
Source records
- crossref · retrieved 2026-09-26T07:28:22.372Z