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Vega and Torq are integrating to close the gap every Cyber Defense Engineer lives with daily: the distance between a triaged incident and an automated response.
The detection-to-response gap is a data and speed problem
Detection engineering has gotten faster. AI-native platforms like Vega have compressed the time it takes to build, test, and ship detections across the full data estate. That part of the job is improving. What has not improved is what comes next: the gap between a triaged incident and an automated response. That gap still runs through tickets, Slack pings, and manual escalation paths built for a slower world.
The root cause is architectural. Security data lives everywhere: Legacy SIEMs, data lakes, cloud logs, EDR. Getting signal out of all of it requires ingestion pipelines that slow everything down before the detection even fires. Then when it does fire, the context needed to respond is spread across platforms that do not talk to each other. By the time a human assembles enough information to act, the window has closed.
AI-paced attackers compress the kill chain from weeks to hours. The detection-to-response loop has to run at the same speed. For most teams today, it runs shift-to-shift at best.
What changes with this integration
When Vega and Torq are both in the stack, the loop closes. Vega gives Cyber Defense Engineers (CDEs) federated access to all their security data without ingestion, detects at AI speed, and triages automatically. Torq takes the output and turns it into action: the right playbook runs, the response happens, the incident closes.
For the CDE, the experience is a continuous workflow instead of a relay race between platforms. You build a detection in the morning. It fires, gets triaged, and triggers a response by the afternoon. No re-ingestion. No pivoting between tools. No waiting for someone else to pick up the ticket.
- Build detections against your full data estate, in place, without moving data
- Vega’s agentic triage clusters signals, applies context, and surfaces a verdict
- The triaged incident flows to Torq and triggers an automated response playbook
- Torq can reach back into Vega’s data layer mid-playbook for additional context
From raw data to automated response, in the same session.
When the two platforms are in the stack together, the synergies are real
Vega brings federated detection and agentic triage. Torq brings AI-native hyperautomation for security response: not just playbook execution, but intelligent orchestration that can reason about what to do next, adapt mid-workflow, and handle the kind of non-deterministic response scenarios that rule-based SOAR was never built for. Each platform does its job well independently. But the detection workflow and the response workflow are parts of the same loop, and when they are connected, each makes the other more effective. Better detection quality feeds better response triggers. Richer response context feeds back into future detection tuning.
What this means for the Post-SIEM Era
Legacy SIEMs were designed for human-speed event streams, human-generated volumes, and human-in-the-loop response workflows. AI-paced threats break all three assumptions.
The CDE operating in this environment does not need a faster SIEM. They need a platform that federates across the full data estate without ingestion, detects at AI speed, triages automatically, and connects directly to response automation. That is the workflow this integration enables. The attacker is betting defenders cannot build it. Vega and Torq are building it.
Talk to us and see the synergies live
Vega will walk through your stack and show you exactly what the Security Analytics Mesh sees that your current tools do not.
Frequently asked questions
Is this a SIEM replacement or an augmentation?
Both. SAM works alongside existing Legacy SIEMs as a federated analytics layer, or it replaces them.
The integration with Torq works either way.
How is this different from connecting a SOAR to my existing SIEM?
Rule-based SOAR was built for deterministic playbooks triggered by known patterns. The scenarios that reach Torq from Vega include full federated context, an agentic triage verdict, and confidence scoring. Torq’s orchestration layer can reason about what to do next rather than just executing a fixed sequence.
What data does Vega federate into the triage context Torq receives?
SAM federates across whatever sources are in the stack: Legacy SIEM, EDR, NDR, cloud infrastructure, data lakes, SaaS logs. The triaged incident Torq receives carries the full correlated context, not just the alert that fired.
Does Vega move data for this to work?
No. SAM queries data where it already lives. Nothing is moved, re-ingested, or migrated. Torq’s reach-back queries into Vega’s data layer also execute in place.
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