How does Gemini generate YARA-L queries from natural language?

When an analyst types a plain-language question into Google SecOps search, Gemini interprets the intent behind that question and translates it into the corresponding structured YARA-L or UDM search query, which is then presented to the analyst as fully mapped, runnable syntax rather than being executed as an opaque black box.

Because the generated query is shown explicitly rather than hidden, an analyst can review exactly which UDM fields and operators Gemini chose before running it, catch a misinterpretation of their intent, and either directly edit the generated syntax or refine their original natural-language prompt to iterate toward the intended query — a design that keeps the AI assistance transparent and correctable rather than a fully opaque process.

This is especially valuable for analysts less fluent in YARA-L syntax specifically, letting them express an investigative question in plain language while still building familiarity with the underlying query language over time by seeing how their intent consistently maps to specific fields and structures.