AI governance needs meaningful decisions on ownership, sensitive data, model behavior and ongoing monitoring.
What are we deploying?
Maintain an inventory of AI use cases, owners, intended outputs, users and material dependencies. Not every use case carries equal risk.
What data is involved?
Assess data classification, permission boundaries, retention, cross-border processing and the risk of sensitive content being exposed through prompts or outputs.
Who is accountable?
Clarify approval authority, model and vendor due diligence, human oversight, escalation and incident handling.
How will controls be tested?
Consider prompt injection, unauthorized information disclosure, output reliability, logging and change management. Tailor tests to the use case.
How is risk monitored?
Review incidents, model updates, data-flow changes, user behavior and emerging guidance. Treat AI governance as an operating process.
Putting the perspective into practice
Start with a well-defined scope, select a small number of high-value controls or decisions, assign accountable owners and review the outcome. Actual requirements vary by organization and applicable rules.
This article is general information, not a claim of original field research or a substitute for tailored professional or legal advice.
← All perspectives