A policy becomes operational when ownership, evidence, thresholds, escalation and review cadence are explicit.
Category Archives: AI Governance
A proportional approach to controls based on impact, autonomy, reversibility and exposure.
Design AI evaluation around real workflows, failure modes and operational thresholds instead of impressive examples.
A lightweight post-deployment monitoring model covering performance, drift, incidents, permissions and user behavior.
Human review is only a control when people have information, time, authority and a realistic ability to intervene.
A lightweight change-control model for prompts, models, permissions, tools and policy-sensitive AI workflows.
Questions for evaluating AI vendors across data, security, model dependency, controls, portability and operating fit.
A practical inventory structure for understanding where AI is used, who owns it, what it touches and how much autonomy it has.
A concise reporting structure for AI adoption, value, exposure, incidents, controls and decisions requiring executive attention.
Agent oversight becomes more credible when organizations decide in advance how an agent can be paused, contained and reviewed.
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