Does restricting frontier AI in the name of safety actually make us less secure? Joshua Saxe joins me to make the case that it does, and that AI cybersecurity will be won through defender adoption, not restriction.
Josh has spent 15 years at the intersection of AI and security. He built and ran the machine learning program at Sophos, then led security for Llama at Meta, covering security post training, evals, agent guardrails, and prompt injection prevention. He recently left to co-found a startup reimagining vulnerability and exposure management agentically. He also writes one of the most cited blogs on AI and cyber policy.
In this episode: - Why restricting frontier model access harms defenders more than attackers - How monitored closed models put threat actors at a structural disadvantage - The jagged frontier, and why attackers don't need frontier models for most of their tradecraft - The national security and supply chain risks of pushing the world onto Chinese open weights models - Why exploits don't cause cyberattacks, and which attacker constituencies AI actually unblocks - The dual use ceiling on guardrails and classifiers - Where defenders should be adopting AI right now, from access management to SOC automation - Using agents to burn down the mountain of security technical debt
Chapters: 0:00 Intro 0:42 Josh's background, from blackhat teen to Llama security lead 3:07 The case for diffusion over restriction 6:14 Why restriction hurts defenders more than attackers 10:19 The jagged frontier and what attackers actually use models for 12:49 National security and the supply chain risk of Chinese open weights 16:08 Exploits don't cause cyberattacks 20:20 Where defenders should adopt AI right now 24:20 Guardrails, classifiers, and the dual use problem 27:34 Reimagining vulnerability management with agents 32:17 The structural advantage defenders hold 35:15 Policy wishes and the attacker's Claude Code moment
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