Traceability is more useful when designed around real operating questions: what happened, why, under whose authority, and what can be reconstructed later.
Category Archives: Risk & Compliance
A proportional approach to controls based on impact, autonomy, reversibility and exposure.
A lightweight post-deployment monitoring model covering performance, drift, incidents, permissions and user behavior.
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.
Agent oversight becomes more credible when organizations decide in advance how an agent can be paused, contained and reviewed.
Responsible governance becomes more workable when controls scale with autonomy, sensitivity, reach and the cost of error.
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.
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