The need for security is pervading all modern day systems. But given the growth in cloud machine-learning computing, which deals with extremely valuable data, and companies need to be paying particular attention to handling that data securely. The operation and maintenance of large scale production machine learning systems has uncovered new challenges which have required fundamentally different approaches to that of traditional software. The area of security in MLOps has seen a rise in attention as machine learning infrastructure expands to further critical use cases across industry. In this talk, we will discuss the key security challenges that arise in production machine learning systems, best practices and frameworks that can be adopted to help mitigate security risks in ML models, ML pipelines and ML services reinforcing SecOps into MLOps.