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Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
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Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
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As AI-enabled cyber capabilities become more advanced, we propose "differential access" as a strategy to tilt the cybersecurity balance toward defense by shaping access to these capabilities. We introduce three possible approaches that form a continuum, becoming progressively more restrictive for higher-risk capabilities: Promote Access, Manage Access, and Deny by Default. However, a key principle across all approaches is the need to prioritize defender access, even in the most restrictive scenarios, so that defenders can prepare for adversaries gaining access to similar capabilities. This report provides a process to help frontier AI developers choose and implement one of the three differential access approaches, including considerations based on a model's cyber capabilities, a defender's maturity and role, and strategic and technical implementation details. We also present four example schemes for defenders to reference, demonstrating how differential access provides value across various capability and defender levels, and suggest directions for further research.
Forward citations
Cited by 2 Pith papers
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Strategic commitments shape collective cybersecurity under AI inequality
Subsidized commitment by a small group of defenders in an evolutionary game model significantly increases strong defense adoption, suppresses attacks, and improves system resilience under AI access inequality.
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Strategic commitments shape collective cybersecurity under AI inequality
Targeted subsidies for committed defenders in an evolutionary game model of AI-unequal cybersecurity significantly increase strong defense adoption, suppress attacks, and enhance overall resilience.
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