Managing the security and compliance of ML environments

Data science teams generally spend a big portion of their time processing the data, training the ML model, and deploying the model to an inference endpoint. Due to the amount of work and research required to succeed in their primary objectives, these teams often deprioritize any “additional work” concerning security and compliance. After a few months of running production-level ML workloads in the cloud, these teams may end up experiencing a variety of security-related issues due to the following reasons:

• A lack of understanding and awareness of the importance of security, governance, and compliance
• Poor awareness of the relevant compliance regulations and policies
• The absence of solid security processes and standards
• Poor internal tracking and reporting mechanisms

To have a better idea of how to properly manage and handle these issues, we will dive deeper into the following topics in...