| Risk | Impact | Mitigation | |------|--------|------------| | (incorrect insights) | Trust loss | Implement confidence scoring ; fallback to “insufficient data” message; human‑in‑the‑loop for beta. | | Latency spikes under heavy load | SLA breach | Autoscale compute pool; aggressive Redis caching; rate‑limit per user (max 5 insights/min). | | Data‑privacy breach (insight generation on restricted data) | Legal/compliance | Enforce row‑level security at DB level; run model in isolated VNet ; audit logs for every request. | | Premium‑only perception alienating non‑premium users | Churn risk | Offer “preview insights” (1 per month) for free tier to drive upgrades. | | Model drift over time | Degraded relevance | Continuous re‑training pipeline using collected feedback (rating + edited summaries). |
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