The cybersecurity market is flooded with vendors claiming self‑learning AI, yet many solutions merely replay past incidents without true adaptation.
Torq’s SOC Brain challenges this norm by delivering a system that evolves with each investigation, drawing from an organization’s unique history and analyst judgments.
At its core are three interlocking capabilities—Torq Recall, Torq Reflex, and Torq Retrospect—each capturing a different facet of organizational knowledge.
Torq Recall transforms historical cases into reasoned precedents by matching observables, weighing evidence, and converting tacit analyst knowledge into structured intelligence.
This reduces investigative overhead, as analysts no longer need to manually curate lookup tables; the AI extracts pertinent patterns automatically, accelerating triage and ensuring consistency across shifts.
Torq Reflex continuously observes SOC verdicts and analyst corrections, internalizing the organization’s risk appetite and decision thresholds through supervised feedback.
Early deployments show Reflex matching analyst‑corrected verdicts about 85 % of the time, with accuracy improving as more feedback accumulates.
Such alignment enables high‑confidence alerts to be safely automated, lowering false positives and alleviating analyst fatigue.
Torq Retrospect solves the cold‑start problem by ingesting resolved incidents from SIEM, EDR, and other sources before the first live alert.
This gives the AI immediate institutional memory, shortening the time required to achieve reliable performance from day one.
Data privacy is ensured by dedicating a separate SOC Brain to each customer, with no pooling of data or model parameters across clients.
The platform also emphasizes explainability, confidence‑based automation, and full auditability to meet governance and transparency expectations.