Trust Lifecycle State Machine
A clear model for how AI trust is established, upgraded, maintained and reduced over time. States are explicit. Changes require evidence.
Trust should behave like a system, not a marketing slogan.
Standard progression path
Observed → Declared → Verified → Anchored → Monitored
What each state means
System existence is recorded publicly. No endorsement is implied.
Identity, operator or scope information is formally asserted.
Defined artifacts or claims pass integrity review.
Records receive timestamp permanence and public referenceability.
Continuity controls remain active as systems evolve.
Status is explicitly reduced when trust conditions fail.
Upgrades require explicit actions
No state change should happen silently. Each transition should be explainable and supported by evidence.
Trust can decay
If oversight ends, trust may revert to the last valid state.
Broken or outdated artifacts can reduce verified confidence.
Major model or workflow changes may require re-verification.
AI systems change faster than static approvals
Models are updated, prompts evolve, vendors change, integrations grow, risks shift. Lifecycle governance reflects operational reality.
Model, not verdict
This page explains the lifecycle framework. It does not certify any individual system or create new trust facts.
Put your AI on a governed lifecycle
Start with assessment, move into verification, then maintain trust through monitoring.