Permission Architecture
Thesis
AI governance is not a policy document. It is a permission architecture that defines who can deploy, modify, and override AI systems.
Analysis
Most governance frameworks describe what should happen. Few define the actual permission structure — who has authority to approve, who has authority to override, and who is accountable when the system fails.
A governance document that says 'AI should be used responsibly' is not governance. It is aspiration. Governance is the architecture that makes responsibility enforceable: the permission levels, the approval chains, the escalation paths, the accountability assignments.
Without this architecture, governance lives in a PDF that no one reads. With it, governance lives in the system itself — embedded in who can do what, when, and with whose approval.
Framework
Permission Architecture DEPLOY → Who can introduce a new AI system MODIFY → Who can change its scope or authority OVERRIDE → Who can reverse its decisions ESCALATE → Who is notified when it fails ACCOUNT → Who is responsible for its outcomes Each permission requires: • Named individual or role • Documented approval chain • Escalation threshold • Review cadence • Audit trail
Board-Level Questions
Who in our organization can approve a new AI deployment?
Who can override an AI decision, and under what conditions?
Who is accountable when the system fails?
Is our governance enforceable through system design, or only through policy?
Operating Implication
Design governance as an architecture, not a document. Define the permission structure, escalation paths, and accountability chains for every AI system. Make the architecture enforceable through system design, not policy aspiration.
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