Running AI workloads on Kubernetes presents unique networking and security challenges that require careful attention to protect sensitive data and maintain operational integrity. By implementing well-known security best practices, like securing API endpoints, controlling traffic with network policies, and enhancing observability, developers can mitigate risks and establish a robust security posture for their AI projects.

Observability is evolving into a crucial component for AI transformation, transitioning from reactive monitoring to a strategic intelligence layer that enhances AI's safety, explainability, and accountability. With significant budget increases and a strong focus on security, organizations are prioritizing AI capabilities in their observability platforms, yet a gap remains in aligning observability data with business outcomes.