## Introduction
Most AI projects fail not because the model doesn't work, but because the system around it isn't production-ready. After shipping dozens of enterprise AI systems, we've developed a checklist that separates demos from deployments.
## The Checklist
### 1. Security & Access Control
Before any AI system goes live, you need:
- **Authentication integration** with your corporate identity provider
- **Role-based access controls** that mirror your organizational structure
- **Data encryption** at rest and in transit
- **API authentication** for all external connections
### 2. Audit & Compliance
Every action the AI takes should be:
- **Logged** with timestamp, user, and context
- **Traceable** back to source data
- **Exportable** for compliance reviews
- **Immutable** once recorded
### 3. Monitoring & Alerting
You need visibility into:
- **Model performance** metrics over time
- **Error rates** and failure modes
- **Latency** and throughput
- **Cost** per inference
### 4. Maintenance & Updates
Plan for:
- **Model versioning** and rollback capabilities
- **Data pipeline** monitoring
- **Dependency updates** and security patches
- **Documentation** that stays current
## Conclusion
Production AI isn't just about the model—it's about the entire system. Use this checklist to ensure your next AI project actually ships.