Developer uses AI judgment layer to triage 24 security findings in one pass

A developer running a personal security panel with 52 tools across seven categories found that chaining them into a pipeline generated 24 findings in roughly 35 seconds, creating a triage bottleneck. Rather than adding more tools, they integrated Jev, a typed-evaluation model released by TypeSafe on September 15, to act as a judgment layer over the aggregated findings. Jev assessed all 24 findings in a single pass, splitting them evenly into 12 true positives and 12 noise entries, each with an associated confidence score. The same layer was applied to a Windows PowerShell audit that flagged two high-severity threats — an unsigned driver and a fileless process — which Jev scored at 0.76 and 0.79 confidence respectively, signalling probable but not certain risk. The developer argues the approach demonstrates that an AI triage layer is cost-effective even for small security operations centres, as it reorders the alert queue by severity weighted against the model's verdict.