Start with a specific task and an observable success criterion. A knowledge assistant, for example, needs evaluation for source relevance, unsupported answers, access boundaries and escalation when evidence is insufficient.
Review the data path. Know which information reaches the model provider, how retrieval permissions are enforced and what is retained in logs. Sensitive data should not be shared simply because a prototype accepts it.
Treat untrusted content as untrusted. Retrieved documents and user messages can carry instructions that attempt to change behavior. Tool permissions, output validation and human approval should constrain the consequences of a mistaken response.
Define operational ownership. Decide who reviews failures, monitors changes and can disable an integration. Evaluate cost, latency and quality under realistic conditions before expanding access.
NIST AI Risk Management Framework offers a useful reference for organizing governance, mapping context, measuring risk and managing improvements. Framework alignment must be supported by evidence and does not certify that a system is safe.
Published 13 October 2026 · Creative Ground Tech editorial team · Educational guidance
Reference: NIST AI Risk Management Framework.