Azure AI
Governed generative AI on Azure
The controls and evaluation loop teams need before an Azure AI prototype becomes a production workload.
Moving a generative AI prototype into production requires more than a capable model. The system needs clear data boundaries, identity controls, evaluation criteria, telemetry, and predictable cost limits.
Treat prompts as application inputs
Validate inputs, separate system instructions from user content, and assume retrieved data may contain hostile instructions. Apply least privilege to every tool the model can reach.
Evaluate the whole system
Measure groundedness, relevance, safety, latency, and cost on examples that represent real use. Repeat those evaluations when prompts, models, retrieval logic, or source data change.
Put explicit limits in place
Token caps, request throttles, budgets, and a kill switch are production controls. They should be observable and adjustable without a deployment.