# AI Model Lifecycle & Governance Platforms: evaluation worksheet

Source: https://dutygraph.com/directory/ai-governance/categories/model-governance/
Editorial date: 2026-09-07

## Scope

- Organization / team:
- Task and expected output:
- Human owner:
- Product and version:
- Evaluation date / environment:
- Reviewer:

## Questions

### 1. Can a model version be traced to its data references, evaluation and approval?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

### 2. Are approved uses and deployment environments explicitly scoped?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

### 3. Which changes or monitoring thresholds trigger revalidation?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

### 4. Can operators identify every affected deployment and recover a prior approved version?

- Observation (demonstrated / described / unknown):
- Evidence reference:
- Limitation or follow-up:

## Evidence checklist

- [ ] A model registry entry with lineage
- [ ] An approval tied to a version and intended use
- [ ] A revalidation or rollback exercise

## Boundary to check

A governed model can still be placed inside a poorly specified or overprivileged agent. Model controls address a component; agent behavior also depends on tools, orchestration, retrieval and the task being performed.

## Decision

- Fit for the scoped task:
- Unresolved gaps:
- Next action, owner and date:

This is a planning worksheet, not an endorsement, access approval or compliance certification. Keep confidential evaluation notes in your organization's approved storage.