Responsible governance becomes more workable when controls scale with autonomy, sensitivity, reach and the cost of error.
Category Archives: AI Governance
A practical permission model for AI agents based on least privilege, bounded actions, sensitive data and recoverability.
Prepare escalation and containment before an AI system produces harmful, unauthorized or materially unexpected behavior.
A monitoring model for higher-autonomy AI systems focused on permissions, behavior, exceptions, drift and escalation rather than fear-based language.
Human-in-the-loop only matters when the reviewer has information, authority, time and a clear reason to intervene.
A simple register can connect AI use cases with owners, affected people, data, controls and the jurisdictions or standards that may apply.
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