Protectors of the modern world: defending against Shadow ML and Agentic AI

Machine learning operations (MLOps) are now critical infrastructure for many companies. The rise of "Shadow ML," where employees deploy AI tools without IT approval, poses security and compliance risks. To mitigate these, organizations must implement MLOps security best practices throughout the model lifecycle. This includes using trusted sources for dependencies, scanning code and containers, signing artifacts, and blocking releases with security issues. As AI grows more prevalent, robust MLOps security will be essential for maintaining trust in digital systems.