AI Safety Compendium

Nova DasSarma

2 min read

Nova DasSarma

AI information security researcher. DasSarma appeared on the 80,000 Hours Podcast to make the case that information-security is a foundational, load-bearing component of AI safety — not a peripheral concern.

Core Argument

DasSarma’s central thesis: even if alignment research succeeds and responsible labs develop safe AI, all of that is defeated if adversarial actors can steal model weights and deploy them without safety measures. Information security therefore undergirds every other safety strategy — alignment, responsible scaling, ai-control — because all of them assume the developing lab retains control of the model.

Threat Model

DasSarma focuses on state-level adversaries as the appropriate threat model for frontier AI labs. Nation-state intelligence agencies have resources, sophistication, and persistence that far exceed typical cybercriminals. Their tactics include persistent access to compromised systems, supply chain attacks on hardware and software dependencies, and human intelligence (insider recruitment). She argues most AI labs are defending against the wrong adversary class.

Career Guidance

DasSarma identifies AI-related information security as a high-impact, talent-starved field. Security expertise from government, finance, and critical infrastructure transfers well to AI lab security. The work has outsized impact because incremental improvements — better access controls, improved monitoring, operational security training for researchers — directly protect all other safety work.

Connections

Her arguments are directly referenced by holden-karnofsky (model weight theft as key risk) and nick-joseph (infosec as part of anthropic’s responsible scaling policy). buck-shlegeris’s ai-control work assumes model weights remain with the developing lab — model theft bypasses all control mechanisms.

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