Visualizing Secure MLOps MLSecOps Practical Guide

This document is a practical guide focused on Visualizing Secure MLOps (MLSecOps) for building robust AI/ML pipeline security. It aims to extend open source tools from secure DevOps to secure MLOps, providing a layered visual learning approach supported by explanatory text. The guide outlines the transition from DevOps to DevSecOps, emphasizing the need for integrating security within the MLOps framework to address unique security challenges associated with AI/ML applications. It discusses the importance of proactive security measures throughout the AI/ML lifecycle, which includes identifying vulnerabilities early in the development process. The document also highlights specific security risks and challenges that arise in the MLOps stages and provides recommendations for mitigating these risks. Additionally, it identifies gaps in current tooling and suggests future development opportunities to enhance MLSecOps capabilities. The target audience includes AI/ML practitioners, software developers, security practitioners, and open source communities involved in AI/ML security.