Private AI Deployment Checklist.

Fourteen operational checks that decide whether your private LLM lands in production or stalls in the pilot graveyard. Used on real deployments in regulated MENA enterprises.

• Data residency and regulatory scope (PDPL, DIFC, GDPR, HIPAA)
• Model selection: open weights vs API vs hybrid
• GPU sizing for your concurrency profile
• RAG architecture and citation requirements
• Embedding model and vector store choice
• Identity, access, and per-role redaction
• Eval suite design before launch
• Cost guardrails per tenant or per role
• Audit logging retention and SIEM hand-off
• Failure modes: hallucination, jailbreak, leakage
• Disaster recovery and model rollback
• Inference latency budget and caching layer
• Update cadence for weights and prompts
• Off-ramp plan if the vendor disappears