Deployment Corrections: An incident response framework for frontier AI models

Understand

A comprehensive approach to addressing catastrophic risks from AI models should cover the full model lifecycle.

• This paper explores contingency plans for cases where pre-deployment risk management falls short: where either very dangerous models are deployed, or deployed models become very dangerous.
• Informed by incident response practices from industries including cybersecurity, we describe a toolkit of deployment corrections that AI developers can use to respond to dangerous capabilities, behaviors, or use cases of AI models that develop or are detected after deployment.
• We also provide a framework for AI developers to prepare and implement this toolkit.