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Control categories
- Preventive controls: approval gates, restricted data, approved vendors, policy and access controls.
- Detective controls: logging, monitoring, testing, complaints, drift checks and human review.
- Corrective controls: incident response, rollback plans, model changes, user notification and remediation.
Common mitigations
- Require human review for high-impact outputs.
- Limit who can use sensitive AI features.
- Remove or mask personal and confidential data where possible.
- Test for bias, robustness, harmful outputs and false claims.
- Add clear user notices and escalation paths.
- Monitor performance and incidents after deployment.
Residual risk
Risk mitigation does not remove all risk. Document residual risk, who accepted it, why it is acceptable, and when it will be reviewed. This is especially important for customer-facing, employee-facing and regulated use cases.
FAQ
What is the best AI risk mitigation?
Human oversight, data restrictions, testing, monitoring and clear escalation are often the most practical starting controls.
Can risk be reduced to zero?
No. The goal is to reduce risk to an acceptable level and document the decision.
Who owns mitigation?
The business owner should own the use case; technical, legal, privacy and security teams support controls.
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.