Translating AI Cyber Evaluation Findings into Defense Prioritization

Determine a structured methodology to translate the findings of frontier AI safety evaluations of cyber capabilities into decision-relevant insights that indicate where cybersecurity defenders should prioritize the deployment of mitigation strategies in real-world scenarios.

Background

The paper reviews common frontier AI safety evaluations in the cyber domain—such as CTF-style exercises, knowledge benchmarks, uplift studies, and cyber range simulations—and observes that while these reveal model capabilities, they do not directly guide defenders on how to allocate resources or prioritize mitigations across the attack chain.

The authors identify a translation gap between capability measurement and actionable defense planning, motivating their proposed framework to map evaluation outputs to cost changes and bottlenecks in the Cyberattack Chain to inform targeted mitigations.