Guarding GenAI Integrity on Kubernetes: Identity Down, Policy Up
AI workloads amplify Kubernetes’ flexibility—and its failure modes. Integrity requires controls that understand models, data paths, and runtime drift, not just pods and namespaces. Push identity down with workload-bound credentials; push policy up with context from model criticality and data sensitivity. Watch the gray zones: GPU device plugins, sidecar sprawl, and egress to model registries. Least privilege, immutable images, and runtime enforcement are table stakes; without AI-aware guardrails, the blast radius grows silently. If latency budgets reject CPU hooks, pivot to eBPF plus network policy—not “trust me” exemptions.