SecJev: Bringing Security Expertise to System One Decision Models
Security workflows need models that turn complex observations and explicit policies into decisions. System One models introduced by Jev return typed predictions and probabilities; security specialization supplies the domain expertise behind those predictions. We introduce SecJev, to our knowledge the first family of Jev-style decision models specialized for security, spanning 0.8B to 9B parameters. Built on Kev's single-pass candidate scorer, SecJev learns Boolean, choice, and ordered decisions from text, telemetry, and observation histories. We develop SecJev-Corpus to unify source-label prediction and explicit-policy evaluation across 14 tasks and eight sources. It covers tool outputs, ...
| Authors | Zheng Chen, Fei Yu, Haohao Huang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 October 2026 |
| Last updated at source | 14 October 2026 |
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