AI services are multiplying faster than privacy controls can keep up. This talk covers a Kubernetes-native approach to make privacy "just work": an open-source framework that treats data protection as infrastructure, not application code. It introduces the concept of a Privacy Operator that discovers AI and ML workloads, applies declarative privacy policies, and enforces anonymization at deployment and runtime. Instead of developers wiring in libraries or filters, the platform ensures that sensitive data never leaves a workload unprotected. We will demonstrate the architecture, policy model, and enforcement patterns, from webhook-based mutation to service-level mediation, with key trade-offs for latency, reliability, and observability. This session will show privacy automation in action as policies update dynamically across running AI workloads.