AI Model Provenance: What Organizations Should Record

Provenance records which models, configurations, inputs, and human actions contributed to an outcome.

Observability focuses on runtime health and performance. Lineage often emphasizes data origin. Audit logging may capture system events without preserving decision-oriented authorization history. Organizations frequently need more than one of these views.

• Model provider, family, identifier, and version
• Release or deployment date where available
• Configuration, system instructions, and prompt version
• Tool availability and retrieved sources
• Input source, user identity, and organizational context
• Safety settings and relevant generation parameters
• Output, reviewer action, subsequent edits, and authorization
• Timestamp and geographic or hosting context when material
• Retention, privacy, and change-management references

Model outputs may not be perfectly reproducible across time, providers, or stochastic settings. That makes source records, reviewer notes, and authorization history more important — not less.