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, traffic, federated updates, consensus, authentication, and vehicle messages.
Scene-weighted training adapts the models across these domains while preserving a shared typed decision interface.
Security specialization improves every model in the family; SecJev-0.8B outperforms general Kev-9B by 20.51 percentage points in task-macro accuracy.
At 0.8B and 4B, SecJev achieves accuracy and latency close to answer-only generative fine-tuning with lower peak allocated GPU memory.
SecJev-4B improves on Kev-4B and security-trained generation in prompt-injection and traffic transfer tests.
We release adapters, decision heads, SecJev-Corpus, and training and inference code.