Adapting Cybersecurity Frameworks to Manage Frontier AI Risks: a Defense-in-Depth Approach
The complex and evolving threat landscape of frontier AI development requires a multi-layered approach to risk management (“defense-in-depth”). By reviewing cybersecurity and AI frameworks, we outline three approaches that can help identify gaps in the management of AI-related risks.
AI Chip Smuggling into China: Potential Paths, Quantities, and Countermeasures
This report examines the prospect of large-scale smuggling of AI chips into China and proposes six interventions for mitigating that.
Open-Sourcing Highly Capable Foundation Models
This paper, led by the Centre for the Governance of AI, evaluates the risks and benefits of open-sourcing, as well as alternative methods for pursuing open-source objectives.
Deployment Corrections: An Incident Response Framework for Frontier AI Models
This report describes a toolkit that frontier AI developers can use to respond to risks discovered after deployment of a model. We also provide a framework for AI developers to prepare and implement this toolkit.