Not sure where your AI use stands?
Run the free AI compliance checkup to get a practical readiness score, likely risk bucket, missing controls and next actions.
The practical purpose
An AI risk management framework helps teams decide which AI risks are acceptable, which need controls, which need escalation, and which should stop a project. It should be repeatable enough for daily operations and flexible enough to handle different use cases.
A simple operating model
- Govern: define roles, policy, accountability, risk appetite and approval paths.
- Map: understand context, users, data, purpose, affected groups and legal setting.
- Measure: test performance, bias, robustness, explainability, privacy and security risk.
- Manage: decide controls, monitor residual risk, report incidents and improve the system.
Evidence to maintain
Keep an evidence pack for important AI systems: business purpose, model/vendor description, data description, risk classification, test results, user notices, human oversight design, monitoring plan, incident records and approval decisions. This pack is useful for audits, customer questions, procurement, legal review and internal governance.
How this supports AI Act readiness
For companies preparing for the AI Act, a framework helps structure the work before detailed legal obligations are confirmed for a specific use case. It also prevents scattered AI adoption where each team writes its own rules and evidence disappears in chat messages.
FAQ
Is NIST AI RMF mandatory?
NIST AI RMF is a voluntary framework, not a law. It is useful as a structure for trustworthy AI risk management.
Can this work for non-technical teams?
Yes. Business owners can map use cases, impacts and controls while technical teams provide model, data and testing evidence.
What is the first deliverable?
An AI inventory combined with a risk triage worksheet.
Sources and review method
This page is written as general business guidance, not legal advice. It is maintained from official AI Act materials, European Commission / AI Office updates, the NIST AI Risk Management Framework and practical AI governance controls.