Security and clinical ML share a hard problem
A research note from the Adaptive AI Lab — cybersecurity and clinical machine learning both face distribution shift, rare high-cost cases, and punished confident mistakes, so they share design requirements.
Research note · July 2026 · Trustworthy AI
Cybersecurity and clinical machine learning look unrelated, but they fail in the same ways. Both domains change after deployment — new attack patterns, new patient populations, new devices. Both contain rare, high-cost cases that standard training underweights. And both punish confident mistakes: a missed intrusion and a missed diagnosis are not rounding errors.
That makes robustness, out-of-distribution detection, interpretable evidence, and careful validation shared design requirements across our cyber and health projects. Methods we build for one domain — retrieval-based adaptation, uncertainty-aware prediction, prospective validation — transfer directly to the other.