A model working in a demo is not the same as a production-ready AI system.
Production deployments need controls around the model, not just a prompt and an API key. A practical baseline includes:
• Least-privilege access for models, tools, databases, and external APIs.
• Secret management instead of keys stored in code or prompts.
• Input validation and output handling for prompt injection, unsafe tool calls, and sensitive data.
• Retrieval permissions that are enforced before documents reach the model.
• Structured logs for prompts, model versions, latency, cost, tool calls, and failures—without leaking protected data.
• Evaluation tests and rollback paths for every prompt or model change.
• Rate limits, budgets, timeouts, and fallbacks so one failure cannot become an outage or surprise bill.
Human approval should remain part of high-impact actions such as payments, access changes, account recovery, and destructive operations.
The goal is not perfect safety. The goal is a system whose risks are understood, monitored, and contained.
HackerAI helps teams manually configure secure AI deployments, integrations, and handoff documentation. See the available setup services in our Shop or visit HackerAI.sh.