Glossary
AI governance: what it covers for a business
AI governance is the set of policies, controls and accountability that decide how an organisation builds, deploys and monitors AI systems, and who answers for their decisions. Good governance makes AI faster to deploy, because teams know the rules in advance. Common reference points are the NIST AI Risk Management Framework and, in Europe, the EU AI Act.
Featuring insights fromShaun Devan · Founder & CEOUpdated · 3 min read
Governance at two levels
| Company level | Agent level | |
|---|---|---|
| Sets | Policy, risk appetite, approved uses | What one agent may decide alone |
| Owned by | Leadership, risk, legal | The business owner of the workflow |
| Evidence | Inventory of AI systems, risk assessments | Eval results, action logs, review records |
| Reviewed | On a set schedule | Before every change ships |
Controls that matter in production
- An inventory of every AI system in use, with a named owner.
- A written authority line for each agent.
- Evals as a release gate, and records of each run.
- A log of every agent action, kept where audit can reach it.
- Data handling rules: what may be sent to which model, and where data is stored.
Governance that keeps pace
Governance fails when it is a committee that reviews every idea. It works when the rules are written once and built into how agents are made, so each new agent ships already inside them. Native builds those controls into every agent. See how Native handles security.
Sources
Frequently asked questions
What is an AI governance framework?
A structured set of policies and practices for managing AI risk. The NIST AI Risk Management Framework is a widely used voluntary one; the EU AI Act sets legal obligations by risk level in Europe.
Who is responsible for AI governance in a company?
Leadership sets policy and risk appetite; each AI system should have a named business owner responsible for what it may do and how it is monitored.
Does AI governance slow down adoption?
Poorly designed governance does. Clear rules set in advance and built into each agent let teams deploy faster with less debate.
