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Synapse uses two-model AI consensus to flag false positives without deleting real bugs

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Security platform Synapse has introduced an AI-assisted false-positive triage system designed to reduce alert fatigue in CI pipelines without compromising vulnerability reporting. The system allows a first AI model to propose that a finding is a false positive, but only a second, independently prompted model can confirm it — and only if both return a confidence score of 75 or above. Critically, no finding is ever deleted; flagged items are marked as exempt from the gate's exit code but remain visible in compliance reports. Before any model runs, a deterministic classifier filters out noise from test files and fixtures, ensuring AI calls are reserved for harder, production-scope decisions. The feature is opt-in and works with any OpenAI-compatible endpoint, including locally hosted models via Ollama or vLLM.

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