AI is moving into the operating core of communication networks: control loops, spectrum allocation, network slicing with QoS, and customer-issue automation, under regulatory watch on life-critical systems. Opaque models and unreproducible outputs do not meet regulatory legal bar. This session presents a deterministic governance pattern for AI-RAN, drawn from work with the FCC. We separate AI-for-Networks (internal optimization) from Networks-for-AI (multi-tenant hosting), and show how one hybrid-cloud platform carries both. The core is a five-stage pipeline: intent, retrieval, rule-bounded reasoning, synthesis, citation-validated output. Every output is reproducible and audit-traceable.
We map it onto a six-layer governance model using OPA or Kyverno for policy, OpenTelemetry and Kepler for energy telemetry, and GitOps for model, rule, and corpus lifecycle. We close with a maturity model and a measurement checklist that separates real AI-RAN energy savings from selective accounting.