Real-Time Threat Analysis

Known threat signatures, blocklists, and rule-based patterns are matched instantly using optimized data structures. The vast majority of threats never reach the AI layer, minimizing latency and computational cost.

Anomalous patterns that pass pre-filtering are analyzed against learned behavioral baselines. Statistical deviation models identify threats that dont match known signatures but exhibit suspicious characteristics.

The most complex and novel threats are escalated to a multi-model AI chain. Multiple specialized models analyze the threat from different angles, producing a consensus classification with confidence scoring.

Threats that the AI chain cannot classify with sufficient confidence are immediately escalated with a complete analysis package, enabling rapid human decision-making.