Securing AI models and ML pipelines: Best practices and pitfalls to avoid

AI security is no longer optional; it is foundational to trustworthy and scalable AI systems. This session breaks down the AI lifecycle through a security lens, highlighting where most enterprises fail and how to address those gaps pragmatically. Topics include:
• Securing training data
• Protecting feature stores
• Hardening model artifacts
• Access control for inference APIs
• Monitoring model behavior in production
• Aligning AI security with governance and compliance frameworks

Real enterprise examples will illustrate what works, what does not, and how to design resilient ML platforms without slowing innovation.