Cody Nash is a PhD scientist and ASCP-certified laboratory professional who builds AI-enabled systems for detecting and analysing malicious code, from multi-stage agentic pipelines down to logistic regressions tuned to catch one-in-a-million events.

Before joining Manifold, Cody spent nearly seven years at Sonatype building industry-leading malicious software detection across open-source ecosystems including PyPI and npm, cross-validating agentic AI, classical ML, and anomaly-detection approaches at scale. His earlier career spans clinical genetic diagnostics, research at Caltech, and a decade of applied ML work ranging from generative modelling with Los Alamos National Laboratory to an issued patent in ML-driven colour prediction. It's a background that built one consistent discipline: look at the data itself, not just the metrics describing it.

At Manifold, Cody applies that discipline to the agentic threat surface, building the detection models and analysis tooling behind the research team's work on malicious agent behaviour, separating legitimate agent activity from compromised action at scale.