WisdomPrompt AI Governance Resource

AI Model Approval Workflow

Define an AI model approval workflow with intake, risk review, evidence, owner approval, deployment status, and ongoing monitoring.

Direct answer

An AI model approval workflow keeps model and provider decisions traceable from intake through risk review, evidence collection, approval, deployment, and recurring monitoring.

How the workflow works

  1. Submit a model, provider, agent, or AI workflow for review.
  2. Capture intended use, data sensitivity, risks, controls, and vendor evidence.
  3. Route approval to accountable owners and reviewers.
  4. Monitor status, exceptions, and review cadence after approval.

Evidence WisdomPrompt keeps visible

  • Approval history and owner records
  • Risk and evidence context
  • Deployment and review status

FAQ

What should be approved before AI deployment?

Intended use, data handling, vendor/provider risk, controls, human oversight, monitoring, and rollback plans should be reviewed.

Can approvals expire?

Yes. High-risk AI systems should have recurring review dates and clear reassessment triggers.

Can this apply to agents, not only models?

Yes. The same workflow can govern models, agents, MCP servers, tools, and AI-assisted internal apps.