AI Governance Approval
Approval provides a formal governance decision point for AI systems and significant lifecycle decisions.Purpose
Approval establishes that the appropriate governance stakeholders have reviewed the available information and authorized a defined action, subject to any conditions or limitations. Approval may consider:- system classification
- risk assessment
- implemented controls
- residual risk
- supporting evidence
- applicable requirements
- assurance findings
- outstanding actions
- exceptions
- monitoring requirements
Decision traceability
A governance approval should be traceable to the relevant:- AI system
- risks
- controls
- assessments
- evidence
- assurance activities
- accountable decision authority
Conditional approval
An organization may use conditional approval when a system can proceed subject to defined restrictions, remediation actions, monitoring requirements, or review conditions. Conditions should be sufficiently specific to support subsequent monitoring and review.Approval and evaluation
Approval is one component of the broader AIGO governance model. Where governance evaluation is performed, approval information can form part of the evidence or governance context used to determine whether defined conditions have been satisfied. The evaluation architecture separates: governance records → evaluation rules → evaluation results This separation allows approval records to remain governance records while evaluation rules define the conditions that are checked.Machine-readable approval records
Approval decisions can be represented using the AIGO Approval Schema. Canonical schema:schemas/05-approval/05-AIGO-Approval-Schema-v0.1.json
Schema documentation:
schemas/05-approval/05-AIGO-Approval-Schema-Documentation-v0.1.md
Source
The canonical procedure is maintained in:guidance/02-procedures/06-AIGO-AI-Approval-Procedure-v0.1.md
The implementation template is maintained in:
guidance/03-templates/07-AIGO-AI-Approval-Template-v0.1.md