Operationalizing AI safety evaluations before deployment

Introduction

Operationalizing AI safety evaluations before deployment makes model risk reviews repeatable and auditable.

The goal is to combine technical testing with governance gates so teams can ship responsibly.

Key Points

• Risk classification defines evaluation depth
• Red-teaming uncovers misuse and abuse paths
• Metrics must track reliability, bias, and robustness
• Governance checkpoints create clear go/no-go decisions
• Post-launch monitoring keeps safety current

How To

1. Define the use case, user impact, and highest-risk failure modes.
2. Select evaluation benchmarks, red-team scenarios, and acceptance thresholds.
3. Run tests, document outcomes, and remediate the highest-severity issues.
4. Hold a governance review with product, legal, and risk owners.
5. Monitor drift, incidents, and updates with a recurring review cadence.

Conclusion

Consistent evaluations turn AI safety into a scalable operational practice.