Torq's AI SOC: Empowering Self-Learning Investigations

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Torq has launched Torq SOC Brain, an advanced component of its AI SOC Platform designed to evolve through ongoing analysis of historical investigations, analyst decisions, and organizational security operations.

Introducing Torq SOC Brain

Torq SOC Brain creates a unified, self-learning AI SOC that adapts to the unique risk evaluation methods of each security operations center (SOC). Unlike conventional autonomous systems, it employs reasoning based on precedent, adjusting to how SOCs assess risk and refining its judgment with every investigation.

Core Capabilities

Torq Recall

Torq Recall leverages historical data to inform current decisions by analyzing security observables such as IP addresses, file hashes, URLs, and hostnames. It ranks relevant past cases, evaluates how prior analyst judgments influenced outcomes, and adjusts confidence levels based on evidence strength.

Torq Reflex

Torq Reflex refines AI models through direct input from SOC teams. It continuously trains dedicated algorithms using confirmed analyst verdicts and corrections, aligning with the organization’s unique risk assessment frameworks. This capability achieves 85% accuracy in matching analyst-corrected decisions immediately.

Torq Retrospect

Torq Retrospect incorporates historical security data predating Torq’s implementation. By importing resolved incidents from existing security tools, it provides Torq SOC Brain with years of organizational knowledge, accelerating accurate decision-making from the first alert.

Data Privacy and Governance

Each Torq customer receives a private SOC Brain tailored exclusively to their organization. This system learns exclusively from internal analyst decisions, incident data, policies, and historical records, ensuring that no customer data is shared or pooled across users. The architecture prioritizes data privacy, with all learning processes occurring within the customer’s environment.

“Torq SOC Brain does not merely automate SOC functions but evolves to reflect the organization’s operational norms,” said Ofer Smadari, CEO of Torq. “By integrating analyst decisions, historical investigations, and risk frameworks, it reduces repetitive reviews, standardizes outcomes, and ensures AI-driven decisions mirror those of human teams.”

Transparency and Compliance

The platform emphasizes transparency and governance, supporting explainable decisions, confidence-based automation, and auditability. Its design aligns with regulatory requirements, including the EU AI Act, while maintaining human oversight in critical operations.