Machine learning systems are now business-critical infrastructure, yet many organizations deploy models without the operational security discipline applied to traditional software. Secure MLOps Fundamentals provides a practical, end-to-end security framework for building, deploying, and operating machine learning systems safely at scale.

This book translates security principles into concrete MLOps controls that engineering and platform teams can apply immediately, without academic theory or vendor bias.

Inside, readers will learn how to:

• Secure training data, features, and labels against tampering and leakage
• Harden model development pipelines and CI/CD workflows
• Manage secrets, credentials, and access across ML environments
• Apply model signing, versioning, and artifact integrity controls
• Detect drift, abuse, and anomalous behavior in production models
• Integrate security reviews into MLOps without slowing delivery

Written for real-world environments, this guide aligns security, DevOps, and ML practices into a single operational model. It is designed as both a reference and an implementation checklist for teams responsible for production AI.