Three AI Governance Questions Executives Should Be Able to Answer

Summary

The article proposes three questions for AI governance: how to detect agents acting against policy, identify every agent and its accountable owner, and preserve trustworthy records. It advocates correlating kernel-level activity with agent-level context for monitoring,|

Key points

• Executives should ask how the organization detects AI agents acting against policy, what AI is in use and who owns it, and what evidence can be produced about agent actions and approvals.
• The article recommends maintaining a live inventory of AI agents tied to accountable people or teams, rather than relying on surveys.
• It argues that agent-level activity data should be paired with kernel-level evidence, such as processes, file access, and network connections.
• Agent-provided context can show prompts or invoked tools, but the article cautions that agents control this data, so it is not proof on its own.
• The article recommends preserving an unalterable forensic record of agent actions, permissions, and approvals for audits and investigations.
• It advocates using real-time, correlated data to support rapid responses when an agent behaves anomalously or maliciously.

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