Google DeepMind and Isomorphic Labs outlined a bioresilience program to curb AI misuse in biology while aiding outbreak response.
The two organisations published an update on a joint initiative that began quietly and has now built out more than 15 partnerships with government bodies, biosecurity organisations, and research groups over the past 12 months.
The disclosure arrives with a specific framing problem attached. Frontier models such as Gemini carry an increasingly detailed grasp of biology, and DeepMind acknowledges that pairing these systems with specialised biology models, agents like its Antigravity platform, and third-party databases will only sharpen that capability further.
However, the same knowledge that helps a researcher map a vaccine target could, in principle, help a threat actor close gaps in their own understanding. DeepMind and Isomorphic describe this as a dual mandate: enable the scientific advances frontier AI makes possible, while keeping those same tools out of the hands of people who’d misuse them.
The program sits on three pillars, according to the companies: preventing misuse, detecting outbreaks faster, and responding once an outbreak or attack is underway.
The 15-plus partnerships built over the last year touch all three, though the update gives limited detail on which organisations are involved beyond a handful of named collaborators, including Lawrence Livermore National Laboratory, the UK AI Security Institute, CEPI, and the Francis Crick Institute.
DeepMind says it intends to widen these relationships over the next six to twelve months, with attention turning to threat intelligence, evaluation methods for AI agents, and jailbreak mitigations. It’s also coordinating with the Frontier Model Forum on questions such as how to handle riskier categories of training data, virology datasets being the example given.
Locking down Gemini without blocking legitimate science
The prevention work rests on threat modelling designed to identify which actors are most likely to attempt misuse and what bottlenecks currently stop them. DeepMind says it uses a mix of expert red-teaming and randomised controlled trials to judge whether Gemini could help someone clear those bottlenecks.Â
Post-training methods are meant to teach the model to refuse harmful queries while avoiding what the company calls over-refusal of legitimate science questions, a balance that’s proven difficult across the industry generally, not just for DeepMind. Classifiers and probes are deployed to flag risky activity in real time, and the company says it runs targeted log analysis to catch more subtle misuse patterns that automated filters might miss.
None of these mitigations is described as solved. DeepMind frames them as an ongoing process rather than a finished system, which matters for any enterprise or government body evaluating whether to rely on the safeguards as currently configured. A classifier tuned against known jailbreak patterns in a controlled…
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