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.

翻译：机器学习（ML）已成为大型复杂系统运行中的一项关键技术，推动了自动驾驶汽车、医疗诊断和金融欺诈检测等领域的进步。尽管其益处显著，但ML模型的部署也带来了重大的安全挑战，例如对抗性攻击，这些攻击可能损害这些系统的完整性和可靠性。为应对这些挑战，本文基于安全机器学习运维（SecMLOps）的概念，提出了一个全面的框架，旨在将稳健的安全措施集成到整个ML运维（MLOps）生命周期中。SecMLOps建立在MLOps原则之上，将安全考量从初始设计阶段嵌入到部署和持续监控的全过程。该框架特别关注防范针对MLOps生命周期各个阶段的复杂攻击，从而增强ML应用的韧性和可信度。一个详细的高级行人检测系统（PDS）用例展示了SecMLOps在保护关键MLOps中的实际应用。通过广泛的实证评估，我们强调了安全措施与系统性能之间的权衡，为在不过度影响运行效率的情况下优化安全性提供了关键见解。我们的研究结果强调了平衡方法的重要性，为从业者提供了关于如何在跨领域ML部署中实现安全与性能最佳平衡的宝贵指导。