AI analytics demos are easy. Production is where things break: leaked sensitive fields, wrong joins, “confident” hallucinated metrics, and silent policy bypasses. This talk is a practical playbook for turning a BI copilot/natural-language analytics assistant into something you can safely ship. We’ll cover the minimum guardrails that actually work in real teams: semantic-layer bound retrieval, policy-aware query construction, answer verification (SQL re-checks + metric validation), and lightweight evaluation harnesses that catch regressions before users do. You’ll see common failure modes, how attackers and normal users trigger them, and the simplest architecture patterns to prevent both data leaks and confidence leaks—without slowing down iteration.