The pilot-to-production gap is the point where an AI proof of concept looks successful in a controlled trial but fails to become a reliable live service. In a pilot, teams usually test a narrow workflow, use clean data, and tolerate manual oversight. Production changes the requirements: the system must be monitored, secured, logged, integrated with other tools, and resilient to failures, abuse, and scale.
This matters in cyber security because many AI risks only appear after deployment. A chatbot or agent can be exposed to prompt injection, sensitive data leakage, unsafe tool use, or runaway actions once it is connected to real users and internal systems. Defenders close the gap with access controls, rate limits, evaluation, approval steps, audit logs, and human fallback paths. Reducing the gap is not just an engineering task; it is a control problem that determines whether AI can be trusted in live operations.