Abstract: Machine Learning Operations (MLOps) has become essential for managing the lifecycle of machine learning models, from development to deployment and monitoring in production environments. As organizations increasingly rely on machine learning for critical applications, security concerns within MLOps pipelines have become paramount. This paper presents a comprehensive framework for integrating security into MLOps workflows, addressing risks such as data breaches, adversarial attacks, and model theft. We explore key architecture patterns, identify security challenges in MLOps platforms, and propose techniques for securing build and deployment processes. By embedding security into each phase of the MLOps lifecycle, organizations can mitigate risks and safeguard their machine learning investments.

Keywords: MLOps Security, Machine Learning, Adversarial Attacks, Secure Model Deployment, Data Security, Model Integrity.

| DOI: 10.17148/IARJSET.2024.111025