Wendy Albert

As artificial intelligence increasingly integrates into cloud-native applications, the associated security risks surrounding AI workflows are evolving swiftly and becoming more complex. Each phase of the AI lifecycle—data ingestion, model training, deployment, and ongoing maintenance—presents vulnerabilities, especially within public or hybrid cloud environments where resources are shared and less controlled. Curious how AI systems in the cloud really work—and how to protect them? This session pulls back the curtain on a modern cloud-hosted AI workflow, breaking down each phase from data ingestion to model deployment.