[论文解读] SecMLOps: A Comprehensive Framework for Integrating Security Throughout the MLOps Lifecycle

Xinrui Zhang, Pincan Zhao+3 more|arXiv (Cornell University)|Jan 15, 2026

Adversarial Robustness in Machine Learning被引用 0

一句话总结

SecMLOps 提供一个将安全性贯穿整个 MLOps 生命周期的综合框架，采用 PTPGC 模型，并通过一个行人检测系统案例研究和经验评估进行证明。

ABSTRACT

Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive framework designed to integrate robust security measures throughout the entire ML operations (MLOps) lifecycle. SecMLOps builds on the principles of MLOps by embedding security considerations from the initial design phase through to deployment and continuous monitoring. This framework is particularly focused on safeguarding against sophisticated attacks that target various stages of the MLOps lifecycle, thereby enhancing the resilience and trustworthiness of ML applications. A detailed advanced pedestrian detection system (PDS) use case demonstrates the practical application of SecMLOps in securing critical MLOps. Through extensive empirical evaluations, we highlight the trade-offs between security measures and system performance, providing critical insights into optimizing security without unduly impacting operational efficiency. Our findings underscore the importance of a balanced approach, offering valuable guidance for practitioners on how to achieve an optimal balance between security and performance in ML deployments across various domains.

研究动机与目标

• 定义 SecMLOps 并论证在整个 ML 生命周期中整合安全性的必要性。
• 给出一个基于 PTPGC 的框架，包含八个专门的安全角色。
• 将 ML 特定的安全威胁映射到 MLOps 阶段，并提出具体的控制措施。
• 通过一个详细的行人检测系统案例研究和经验验证，展示该框架。

提出的方法

• 将框架 grounding 于 DevOps、MLOps 以及 PTPGC 模型。
• 定义八个安全角色并在 SecMLOps 中分配职责。
• 识别 ML 特定威胁（如数据污染、对抗样本、漂移）并使用 STRIDE 分析将其映射到 MLOps 阶段。
• 提出覆盖数据、模型训练/部署、测试、监控和治理的技术栈及安全能力。
• 描述具有可重复性、溯源性、自动化安全检查和漂移感知的持续监控的过程。
• 说明 SecMLOps 如何集成到 CI/CD 流水线以及治理/合规性的考虑。

实验结果

研究问题

• RQ1 在 MLOps 生命周期中，ML 独有的安全需求和威胁是什么，如何系统地加以应对？
• RQ2 SecMLOps 如何在 People、Technology、Processes、Governance、Compliance 五个组件上实现落地？
• RQ3 在实际用例中应用 SecMLOps 对安全性和性能的影响如何体现？

主要发现

• 提出一个综合的 SecMLOps 范式，将安全贯穿于 MLOps 生命周期，基于 PTPGC 模型。
• 将 ML 的特定威胁映射到不同的 MLOps 阶段，并基于 STRIDE 的控制措施。
• 一个详细的行人检测系统案例研究演示了 SecMLOps 各组件在实践中的整合。
• 经验评估突出安全措施与系统性能之间的权衡，为优化提供指引。
• 该框架强调可重复性、溯源性、自动化安全测试以及漂移感知监控，以提升韧性。