How do we show the board our AI is actually governed?
The EU AI Act requires documented decision-making and named human accountability, but current frameworks omit execution-time control logic for agentic actions. Unmonitored AI agents can change behavior without tripping traditional alerts, creating compliance and liability risks.
The question
Our board is asking whether the AI we have deployed is safe, compliant, and under control. What do we actually need to show them — an AI inventory, risk assessments, oversight controls, an incident process — to demonstrate governance rather than just discuss it, and how much is enough for now?
Counsel's position
Implement a tiered AI inventory linked to risk assessments, assign clear oversight accountability, and establish a tested incident response process.
Verdict
The verdict: Implement a tiered AI inventory linked to risk assessments, assign clear oversight accountability, and establish a tested incident response process.
EU AI Act requires documented decision-making and named human accountability
Given your board's request for compliance proof, you must demonstrate meaningful human accountability rather than just presenting a risk registry.
Unmonitored AI agents can change behavior without tripping traditional observability alerts
To prove your deployed AI is under control, your monitoring must capture why agents act, not just uptime and latency.
AI governance requires an inventory tracking models, data inputs, and infrastructure
Given your need to show the board what you actually control, treating AI as an ecosystem rather than isolated tools is essential.
Current AI governance frameworks omit execution-time control logic for agentic actions
While you assemble oversight controls for the board, recognize that standard risk frameworks leave a gap between policy and runtime enforcement.
Formalizing AI oversight before scaling enables rapid response to regulatory scrutiny
To demonstrate governance to your board, you must establish accountable owners and escalation protocols as strategic infrastructure, not just compliance checkboxes.
Read another verdict
- Which process should we point AI at first?
- Put one person in charge of AI — or is a Head of AI premature for us?
- Buy a tool for this process, or build around our own knowledge?
- Centralize AI strategy under CEO or distribute ownership?
- Adopt new AI ROI tools or refine existing methods?
- Invest in pre-build costing or post-deployment ROI tracking?
- Our documents are a mess. Clean them up before AI, or after?
- How do we measure the return on an AI workflow — and what baseline is honest?