Torq AI SOC Platform

Customizable playbooks that automate and orchestrate repeatable response tasks and multi-step workflows across security and IT tools.

Centralized case management to plan, track, and coordinate the response to security incidents, storing investigation data and evidence in one workspace.

Automatic enrichment and triage of incoming alerts to reduce manual analyst effort and prioritize genuine incidents.

Out-of-the-box connectors and APIs to security and IT systems that let playbooks read context and push enforcement actions.

Aggregation, scoring, and operationalization of threat intelligence feeds to enrich alerts and drive automated response decisions.

Dashboards and reporting on response metrics such as mean time to respond, playbook performance, and analyst workload.

Generates investigation summaries and incident reports for analysts and leadership from completed investigation activity.

Ends each autonomous investigation with recommended next response actions drawn from the agent's own findings and verdict, for an analyst or a downstream tool to act on. The recommendation is advisory; executing containment is a separate capability.

Performs initial triage of incoming alerts automatically, classifying and prioritizing them to cut tier-1 workload before a human touches the queue.

Identifies and dismisses false-positive alerts with documented rationale, reducing noise reaching human analysts.

Investigates alerts end-to-end from trigger to verdict and closes them out autonomously, so the full volume of raw alerts gets analyzed without resource-constraint concessions.

Lets analysts drive investigations and threat hunts through natural-language questions instead of query languages.

Automatically gathers and attaches context (threat intelligence, asset and identity data) to alerts during triage and investigation.

Reconstructs attack timelines and maps alert activity onto attack paths so scope and impact of an incident are clear.

Suggests the next investigative or containment steps for an alert or incident, with the supporting reasoning, so analysts can confirm and act rather than deciding from raw telemetry alone.

Inserts AI-generated analysis, triage decisions, and enrichment into existing SIEM and SOAR case management workflows rather than requiring analysts to use a separate interface.

Applies ML classification to incoming alerts to filter false positives, group related events, and route high-confidence detections to analysts, reducing L1 analyst workload.

Assembles chronological attack timelines from raw events across multiple data sources automatically, reducing the time to build an initial incident narrative.

Accepts natural language queries over security telemetry and translates them to structured queries, enabling investigation without requiring analyst proficiency in SPL, KQL, or SQL.

Carries out containment and remediation steps through the organization's existing security tools, such as disabling an account, revoking sessions, quarantining a host or blocking an indicator, within a scope the team approved in advance. Higher-impact actions wait for a human approval, and every action is logged with the reasoning behind it. Recommending next steps is a separate, advisory capability.

Maps the detections running in the SIEM or EDR to a framework such as MITRE ATT&CK to show where coverage is missing, then drafts, tests and tunes detection content from investigation outcomes, for example by proposing a scoped change to a rule with a high false-positive rate. Output ranges from recommendations an engineer reviews to deployable detection-as-code.

Reads threat intelligence reports and feeds, summarizes what matters for the organization's own environment and turns the indicators and behaviors described into queries, hunts or detection candidates the team can run against its telemetry. Depth ranges from report summaries to automatic hunts triggered by each new report.