Agentic-based approach for reducing false positives in machine learning model output classifications

Abstract

Techniques are provided for adjudicating suspected-malicious samples using in-context gating and an appeals stage to reduce false positives. A first-stage classifier generates a benign-or-malicious verdict. When malicious is indicated, a context-gating model determines whether the sample is in-context for a target application domain. If out-of-context, the initial verdict is finalized. If in-context, contextual information is assembled by retrieving and condensing items from a knowledge base via similarity search. The sample and the contextual information are then supplied to an appeals model, distinct from the first-stage classifier and the context-gating model, which issues a context-sensitive verdict. The appeals verdict is emitted as the final decision. This architecture separates fast screening from domain-aware reasoning, enabling dynamic incorporation of deployment-specific knowledge without retraining the first-stage classifier, improving accuracy, and providing a principled fallback when the sample is out-of-scope for the application domain.