AI AI SOC Integrations

Torq SOC Brain™: The AI SOC That Learns, Not Just Remembers

By Bob Boyle

July 28, 2026

11 Minute Read

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Back in June, I wrote a blog making the case that agentic triage alone isn’t an AI SOC. The way I see it, that’s like saying triage is the only responsibility of a SOC team. But as we know, the SOC’s responsibilities extend far beyond that, and these triage-only solutions don’t investigate threats, contain them, or close cases. That work is still left to the SOC team; the bottleneck is just shifting.

The argument is that the bar should be higher, and Torq is setting that bar. Full lifecycle coverage — across triage, investigation, threat hunting, containment, and remediation — enterprise scale within some of the largest Fortune 100 SOCs in the world, and transparent, defensible AI agents grounded in your organization’s business context all point to why Forbes, KuppingerCole Analysts, and Gartner have recognized Torq’s position at the top of the AI SOC market.

That argument still stands, but today… We’re raising the bar again.

Introducing: Torq SOC Brain™ — the memory layer that makes Torq the only AI SOC platform that genuinely learns.

The Self-Learning Torq SOC Brain

For most “AI SOC” platforms, learning simply means that when an alert fires, the system searches through past cases, finds one that looks similar, and feeds that example into an LLM to help it make a decision. This is more retrieval than actual learning. A system that retrieves past cases from weeks ago has no memory of what decisions were made yesterday, or understands that your senior analyst treats certain scenarios differently than your Tier 1 team does. It doesn’t get better or adapt because it’s pattern matching. And that approach is going to hit the same efficiency wall every single time.

“With Torq SOC Brain and its Torq Recall, Torq Reflex, and Torq Retrospect capabilities, the Torq AI SOC Platform truly learns how a SOC thinks, even from the years of history that predate a Torq deployment. That’s the difference between automation that treats every investigation as if it were on its own and the industry’s first SOC that gets smarter and more accurate with each completed investigation.”

– Ofer Smadari, Torq Co-Founder & CEO

Torq SOC Brain is the learning layer of the Torq AI SOC Platform… that actually learns. Every customer has it built directly into their private workspace, exclusively for their organization. It never pools customer data, never shares model parameters, and never trains one customer’s AI on another customer’s experience. It is a private intelligence layer that ensures your Torq AI SOC Platform becomes your judgment, your model, your intelligence.

Torq SOC Brain is the reason every Torq Auto Triage verdict, every Torq Socrates™ investigation, and every Torq HyperAgents™ response action gets more accurate as your team uses the platform. It’s built on three interconnected capabilities: Torq Recall, Torq Reflex, and Torq Retrospect. Together, they do something no other AI SOC platform can: turn your resolved cases, analyst decisions, and years of institutional history into a model that thinks like your team.

Torq Recall: Memory That Actually Works

The most common form of memory in AI SOC tools is a search interface, powered by an agentic chatbot that runs stateless queries to find semantic similarities. In security, just because something looks roughly similar doesn’t mean anything; to an AI model, two hash values might look semantically close, but point to two completely different files. In a high-stakes environment, “close enough” can be catastrophic.

Recall instead relies on structured retrieval, looking for exact, deterministic overlaps of specific security observables — IPs, file hashes, URLs, hostnames, or emails. If your team has seen an exact indicator before and documented their conclusion, Recall finds it.

If historical cases aren’t automatically fed back into agentic decisions, institutional knowledge stays buried in closed tickets, and verdicts quickly become outdated. A case closed last week tells a very different story than one closed last year. What makes Recall truly game-changing is the understanding of the signal strength, so the AI stays focused on entities that matter.

Recall analyzes analyst notes, weights precedents by recency, and understands that different conclusions hold different weights. And if it finds contradicting records, Recall knows when to say it’s sure and when it isn’t, rather than force an answer it cannot justify. That honesty is what builds trust in agentic decisions at scale. Triage verdicts and response actions are not based on last month’s playbook; they are grounded in how your SOC operates today and automatically applied to every alert.

Torq Reflex: Your Team’s Judgment Applied

Often, when AI decision-making misses the mark, it simply lacks the correct context. Recall solves that problem. But research conducted by Torq Labs found that, even with all the context, all the memory, and the same facts a human analyst has, an AI model can make an incorrect decision or even contradict an earlier decision. This isn’t a data gap; it is a judgment gap between humans and machines. While Recall allows agentic verdicts to be based on your most accurate case history (i.e., the data), Torq Reflex gives the AI model access to your team’s judgment.

Reflex is a per-tenant model that trains continuously on your team’s confirmed verdicts and corrections, engineered to operate under asymmetric risk where missing a malicious threat is considered far more costly than mislabeling a benign alert. When an alert fires, Reflex assigns a verdict and, most importantly, a calibrated confidence score — a real mathematical probability that builds trust in verdicts and makes the system safe to run.

When confidence is high, the verdict drives automated output (e.g., filtration, case creation, prioritization, or autonomous remediation). If it isn’t high, the full analysis still runs, the agentic reasoning is documented, and the case is escalated to a human analyst for confirmation. As a result, Reflex helps improve agentic decision making with each alert, because any alert that it’s uncertain about routes back through the human-on-the-loop process you already trust.

Whatever the analyst decides retrains the model, so every correction is worth a little more, and every verdict becomes more trustworthy. Reflex is the key to how the Torq SOC Brain actually learns and becomes more accurate over time. The longer you use Torq, the smarter the Torq SOC Brain gets.

“The longer Torq runs in our environment, the more it sounds like our best analysts. That’s the part no other AI SOC platform delivers.”

– Director of Security Operations, Fortune 500 Financial Services

Torq Retrospect: Informed Decisions From Day Zero

Recall and Reflex drive the Torq SOC Brain to truly learn over time, becoming your own SOC model based on your own SOC judgment. But given today’s AI-augmented threat landscape, time works against SOC teams in identifying and responding to attacks now more than ever. They need an AI SOC solution that drives value now, today, not 6 months down the road. Enter Retrospect.

Most AI SOC platforms arrive knowing nothing about the environment or how your SOC operates. Deployment is an uphill battle, early verdicts are incorrect, and analysts lose trust before the system has a chance to earn it. Retrospect makes sure you don’t start from scratch. Before Torq sees a single live alert, Retrospect normalizes all your external cases and observables, imports them from your existing case management tool, and makes that history immediately available to Recall and Reflex. Years of analyst decisions, closed cases, and documented outcomes become the foundation of the Torq SOC Brain.

The result is an AI SOC that doesn’t just arrive informed; it’s already smarter than most deployments will hope to achieve after 6 months of production. The learning curve that kills early adoption of agentic decision making collapses entirely. Your SOC’s institutional knowledge doesn’t start accumulating on Day One; it’s already there.

What The Torq SOC Brain Looks Like In Practice

In an enterprise SOC, accuracy is dynamic. Your environment changes. AI models are upgraded. Threat actors adapt. New attack patterns emerge. Your team’s risk posture evolves with every incident you close.

A static AI model calculates the same confidence against the same signal regardless of how many times your team has overridden it on that exact attack pattern. There is no “getting better”; it simply is what it is, never earning the trust of the human analysts responsible for defending and justifying every decision made in the SOC.

In the 2026 AI SOC Leadership Report, 97% of respondents reported confidence in AI’s ability to address alert triage, but only 37% applied AI to the use case. The gap between confidence and adoption, the report found, is largely driven by trust. SOC leaders reported they don’t have full transparency into agentic decision-making, and therefore, can’t defend a missed alert or a false verdict. Analysts find themselves re-checking the agent’s conclusions, doubling the work, and leaving them right back where they started: buried in alerts.

On the other hand, Torq Auto Triage has maintained 100% usage retention across all customers and a steady average MoM increase of 144% in alert volume fed into the system. Torq Auto Triage customers report a 97% reduction in alert noise, a 60x increase in triage velocity, and an improvement in accuracy score from 94% to 99% over an average three-month period.

“Torq Auto Triage was ready for an enterprise of our scale, where a competing solution was not.”

– Global FinTech Enterprise

The Torq SOC Brain drives accuracy in agentic verdicts, acting as the primary engine and memory layer behind Torq Auto Triage. The Torq SOC Brain justifies each Torq Auto Triage verdict with crystal clear evidence and reasoning, documented and cited in the historical truth of how your SOC operates. Those verdict decisions improve in accuracy with every alert and are treated as first-class data, stored in the Torq Context Graph. This, in turn, grounds every agentic decision across the entire Torq AI SOC Platform in your organization’s most accurate and up-to-date truth. Every triage verdict becomes more confident, every investigation becomes more precise, and every autonomous threat remediation becomes more trustworthy.

Most importantly, the Torq SOC Brain is what allows some of the largest enterprise SOCs in the world to achieve such rapid time-to-value with the Torq AI SOC Platform:

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts and has automated 41 different runbooks within just one month of deployment.” – Kevin Murrietta, Team Lead, Security Operations Center, Carvana

“Torq Auto Triage delivers much faster, more consistent, and more accurate results” – Global Biotech Enterprise

“We use Torq to automate triage across multiple attack surfaces. It performs better than other solutions. [Torq’s] AI capabilities provide immediate ROI by reducing manual effort and significantly improving operational efficiency.” – Senior Cybersecurity Engineer, Tech Vendor (10,001+ employees)

Most recently, the InfoSec Director of a Fortune 100 Global Retail Enterprise, who completed a full Torq AI SOC Platform migration in only two weeks, reported: “In just three months, we’ve already accomplished so much more than we ever did in five years with [previous solution].”

Your Judgment. Your Model. Your Intelligence.

Torq has been widely recognized as a leading innovator and a dominant force in the AI SOC landscape (see Forbes, KuppingerCole Analysts, and Gartner). We set the bar for end-to-end threat lifecycle coverage, while others simply triaged threats and shifted the SOC bottleneck further down the line.

Today, the bar is even higher. The question isn’t just whether your AI SOC completes the threat lifecycle; it’s whether it learns how your team wants it done.

Your AI SOC should handle every alert the way your team would, and get better at it over time. That’s the Torq SOC Brain. That’s why Torq Auto Triage is the only agentic triage engine that actually learns. And that’s why the Torq AI SOC platform isn’t just the most complete AI SOC on the market; it’s the only one that becomes your organization’s own AI model.

See it live at Black Hat at Booth #4935, or request a demo today.

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“Torq takes the vision that’s in your head and actually puts it on paper and into practice.”

Corey Kaemming, Senior Director of InfoSec

“Torq offers unprecedented protection and drives extraordinary efficiency for RSM and our customers.”

Todd Willoughby, Director

“Torq saves hundreds of hours a month on analysis. Alert fatigue is a thing of the past.”

Phillip Tarrant, SOC Technical Manager

“The only limit Torq has is people’s imaginations.”

Gai Hanochi, VP Business Technologies

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts.”

Dina Mathers, CISO

“Torq has transformed efficiency for all five of my security teams and enabled them to focus on much more high-value strategic work.”

Yossi Yeshua, CISO

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AI SOC

The 2026 AI SOC Roadmap: Where SOC Teams Are Headed and How to Get There

By Torq

July 21, 2026

8 Minute Read

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Every conversation our team has with security leaders begins the same way. Nobody is backing off on AI in the SOC. The direction of the lean is what’s shifting.

Torq’s 2026 AI SOC Leadership Report surveyed more than 450 CISOs and SOC leaders. The data confirms what those conversations were already telling me. We’ve left the adoption phase. The market is now in the architecture phase and the implications for how teams plan, buy, and build are significant.

The first wave of AI in the SOC came down to a few questions: Does it work? Can a large language model read an alert? Can an agent investigate a case? Can autonomous reasoning extend triage without breaking trust? All three are answered. Answered well enough that 94% of security leaders now use AI in at least one SOC function, and the report found that the average SOC runs more than seven AI-powered tools at once.

The architecture phase is about different questions. Which platform anchors the operation? What gets consolidated, and what gets replaced? Where does AI extend next, and what kind of trust framework lets it? The teams moving fastest right now aren’t running more AI experiments. They’re making architectural commitments — commitments that will shape the SOC through 2026 and beyond.

Where Teams Are Expanding AI in the Next 12 Months

Two expansion frontiers stand out in the data. Both are large, both are intentional, and both point in the same architectural direction.

Cloud security: 40% of security leaders plan to expand AI here in the next 12 months — the largest expansion category in the report, and it’s not close. Cloud security is the cleanest use case for AI in the SOC because it’s the area where manual coverage has fallen furthest behind operational reality.
Incident response automation: 28% of leaders plan to expand AI into response. Detection automation has been broadly accepted. Response automation is the next earned step — extending AI from “tell me what happened” to “act on what happened.” It’s a smaller percentage because it’s a higher-stakes commitment, but the direction is clear.

Underneath those headline numbers is a more telling pattern. Security leaders said they want 51% of daily SOC tasks automated on average — and the teams furthest along on AI deployment are also the teams setting the highest targets for the next 12 months. That correlation is the architectural payoff. Early adopters who built on the right foundation are now expanding from it.

Why Cloud Security Leads the Expansion

Cloud security generates orders of magnitude more telemetry than legacy infrastructure. The volume problem alone forces the issue: a team that can only review a small fraction of cloud signals manually needs AI to handle the rest. Hiring doesn’t close that gap, and CISOs know it.

The second reason cloud leads AI expansion plans is configuration drift. Cloud environments change continuously — new services spun up, identities rotated, workloads scaled across regions overnight. A static playbook for cloud detection is obsolete the moment the environment changes beneath it. AI that reasons across the current state of the environment adapts in ways static logic cannot.

The third reason is that in the cloud, finding a problem and fixing it are two different jobs owned by two different teams. Security can embed control points in the delivery pipeline and continuously scan cloud assets, but it can’t remediate unilaterally — the production environment belongs to DevOps and engineering. So every finding kicks off a lifecycle: detect the issue, identify the owner, get them to commit to a fix, and validate that the fix actually resolved it. That coordination loop, not the detection, is where the enormous operational load of cloud security comes from, and it’s exactly the kind of multi-step, multi-owner work AI orchestration is built to carry.

This is the structural reason cloud security is the strongest signal in the expansion data. The architecture that handles it has to orchestrate the full remediation lifecycle across teams, not just detect within a single team.

Why Incident Response Is the Trust Frontier

Detection automation was adopted broadly because it keeps a human in the loop: the AI surfaces risk, and a person still decides what to do about it. It’s the most common error, a false positive, that costs little more than analyst time. Response automation removes that buffer — the AI acts on the environment itself, so a wrong call doesn’t just waste time, it hits production. A contained endpoint that shouldn’t have been contained breaks a workflow, a revoked credential disrupts a legitimate session, and an auto-blocked IP can take down a production service. The stakes move outside the SOC, and the trust required to cross that threshold rises with them.

The data shows leaders are ready to cross it, but only on specific terms. The conversations behind the 28% expansion number make those terms clear.

Transparent reasoning: Every action an AI takes must be supported by a documented chain of reasoning. Black-box decisions don’t earn trust in response, and they shouldn’t.
Configurable autonomy: The team decides which actions AI can take autonomously, which require human approval, and which never run without analyst sign-off. The trust model is set by the operator, not by the platform vendor.
Continuous learning: Actions taken under AI direction are logged, auditable, and recoverable. When an agentic verdict is overturned, the decision becomes precedent, and the system improves.

This is the architecture we built at Torq for response automation. Agentic action earns the right to expand into incident response when the decisions behind it are grounded in organizational context, not just a siloed signal triggering a cookie-cutter response workflow.

The Torq Context Graph makes that trust possible, giving Torq AI Agents a single source of truth to reason over before any runbook planning or execution. What is true about the environment now? What exceptions have been made since this response plan was first crafted? Every response is executed exactly as an analyst would — with context, without improvisation.

Socrates, Torq’s AI SOC orchestrator, makes the trust boundary visible: when it encounters a step it can’t perform, it stops, names the limitation in plain language, and routes the work to a human. That’s the architecture that earns the right to expand AI into response.

What the Architecture Phase Requires

The teams that will win the next 12 months won’t be the teams with the most AI tools. They’ll be the teams with the best-architected AI platform. Three commitments separate the platforms that will define the AI SOC from the vendors that will get stuck.

1. A platform, not a stack. The report found that 80% of security leaders say their SOC is still fragmented across too many tools. The architecture phase rewards unification. The teams that pick a single platform to anchor the AI SOC and start unifying around it will move faster than the teams continuing to stitch together point solutions.

2. Trust by design. 90% of security leaders want explainable AI decisions before they’ll trust AI with more autonomy. The platforms that make AI reasoning visible — every step, every decision, every action documented — will earn the trust required to keep extending. The platforms that don’t, won’t.

3. Learning that compounds. The AI you deploy today should be measurably better six months from now, and it should be better because of your team’s specific corrections. Static AI plateaus, but adaptive AI compounds. The architectural commitment is to a platform whose returns scale with the team using it.

These are the foundations on which the architecture phase is built on. Platforms that ship them as design principles will define the AI SOC through the next few years. Platforms that bolt them on later won’t.

The 12-Month AI SOC Playbook

Below is some concrete guidance for the security leaders reading this with a budget cycle ahead of them.

Audit the gap. Map your current AI coverage against the end-to-end SecOps lifecycle that 89% of respondents identified as a key factor to increasing agentic trust, producing better-informed conclusions, and higher-confidence recommendations. Does your architecture cover triage, investigation, threat hunting, and response? Most AI point solutions are missing coverage, claiming to be something they are not.

Choose your unification anchor. If your stack runs more than seven different AI-powered tools (and the data says it probably does), pick one as the unification anchor and start measuring the others against it. The architecture phase rewards conviction. Indecision rewards no one.

Extend AI into response on your terms. Pick the two highest-volume response workflows your team handles manually today. Move them to AI-led execution, with analyst oversight for actions that require it. Measure the trust gap and the operational impact over 30 days. Expand or roll back based on the data. That’s how trust gets built — case by case, with the operator in control.

The AI SOC Roadmap Through 2026 and Beyond

The architecture phase isn’t a single year of work. It’s the foundation for what the AI SOC looks like by 2026 and beyond. The decisions made over the next 12 months compound — which platform anchors the SOC, which workflows get extended into AI, which trust framework earns the team’s confidence.

The teams that get this right won’t be the teams with the most AI tools. They’ll be the teams whose AI gets measurably better the longer it runs, on a platform built for that trajectory. The 2026 AI SOC Leadership Report has the full data behind where 450 security leaders are headed — what they’re prioritizing, what they’re walking away from, and what the architecture phase looks like across industries and team sizes.

Get the Report

Keep reading the 2026 AI SOC Leadership Report Series.

Ready to automate everything?

“Torq takes the vision that’s in your head and actually puts it on paper and into practice.”

Corey Kaemming, Senior Director of InfoSec

“Torq offers unprecedented protection and drives extraordinary efficiency for RSM and our customers.”

Todd Willoughby, Director

“Torq saves hundreds of hours a month on analysis. Alert fatigue is a thing of the past.”

Phillip Tarrant, SOC Technical Manager

“The only limit Torq has is people’s imaginations.”

Gai Hanochi, VP Business Technologies

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts.”

Dina Mathers, CISO

“Torq has transformed efficiency for all five of my security teams and enabled them to focus on much more high-value strategic work.”

Yossi Yeshua, CISO

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AI SOC

20 Questions Every Security Leader Should Ask Before Buying an AI SOC

By Torq

July 8, 2026

8 Minute Read

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Most “AI SOC” demos out there can look great. The polished dashboard, the confident verdict, the slide that says “autonomous.” A demo is built to show the platform at its best, on clean data, in a controlled environment, answering a question the vendor already knew was coming. The differences only show up after you’ve signed, when the platform meets your real stack, alert volume, and compliance requirements.

That’s why your AI SOC evaluation can’t end at the demo. The questions below are designed to surface what a polished walkthrough may hide. They’re grouped by what each cluster actually tests, with the kind of answer that should reassure you and the kind that should worry you.

If a vendor dodges, over-promises, or answers a different question than the one you asked, you’ve learned something the demo wouldn’t have told you.

Integration and Context

A SOC doesn’t run on one tool. It runs on a stack — SIEM, EDR, identity, cloud, email, ticketing — and an AI SOC is only as good as its ability to reason across the whole stack. A platform that can’t see your whole environment will make confident decisions on partial information, which is worse than no decision at all.

Context is what separates a verdict that’s right in general from one that’s right for you: the same login anomaly is benign for a contractor on a known VPN and critical for a finance director near the M&A data room. Ask:

Does the platform integrate with your existing stack as a unified orchestration and case management layer, instead of forcing a rip-and-replace?
Can it correlate signals, context, threat intelligence, and historical activity across SIEM, EDR, identity, cloud, email, and your other systems?
Does it maintain a continuously updated context model, a single source of truth for your environment?
Does it provide native case management, investigation timelines, and cross-tool evidence correlation?

What a strong answer sounds like: The platform layers on top of what you own, builds a living model of your environment, and continuously improves its understanding as more systems are connected.

Red flag: The vendor wants to replace tools you just bought, or treats “integration” as a one-way alert feed with no context flowing back.

Memory and Learning

When a senior analyst leaves, their judgment usually walks out with them. The same is true of most “AI SOC” tools: every alert is evaluated from scratch, and the hard-won verdict your team reached last week is buried in a closed ticket.

A real AI SOC turns past decisions into precedent, so the platform gets sharper the longer it runs, rather than repeating the same mistakes at machine speed. If a vendor can’t explain how their system learns from your analysts specifically, what you’re buying is static automation with a better logo. Ask:

Can the AI reference prior investigations, analyst decisions, and case outcomes when evaluating a new alert?
Does it maintain persistent organizational memory, so analyst decisions improve future verdicts?
Can it explain which prior cases, decisions, or patterns influenced a recommendation?
Does it learn from analyst feedback, verdict changes, and case resolutions to calibrate confidence over time?

What a strong answer sounds like: The platform shows you the past cases behind a verdict and measurably improves as your team corrects it.

Red flag: “It learns from feedback” with no explanation of how, or learning that lives in prompts rather than a model that actually remembers.

Action and Autonomy

This is the line most “AI SOCs” can’t cross, and it’s the one that matters most. Triage prioritizes risk; it tells you what’s real and what’s next. But if the platform then hands a to-do list to a human, it hasn’t reduced the workload. The investigation still happens by hand, the response still requires logging into another tool, and the case still closes on human time.

A true AI SOC carries the alert through to resolution and escalates to a person only when judgment is genuinely required. Ask:

Does the platform go beyond triage to autonomously investigate, contain, remediate, and close incidents?
Can it update cases in real time, adapting from autonomy to escalation the moment a threshold is met?
Does it support human-on-the-loop operations, where analysts step in only when needed instead of driving every workflow?
Can you set adjustable autonomy levels based on severity, confidence, business context, and risk tolerance?

What a strong answer sounds like: The platform closes a meaningful share of cases end-to-end, and you control where it acts versus escalates.

Red flag: “Autonomy” that stops at a recommendation, or an all-or-nothing switch with no dial for severity and confidence.

Customization and Control

No vendor’s out-of-the-box agents know your policies, your escalation paths, or your risk tolerance on day one. The platforms that work are the ones you can shape to your environment without a team of engineers babysitting them.

This is also where the trust conversation lives: security leaders are right to be cautious about handing authority to a system, and the answer isn’t less automation, it’s more control over what the automation is allowed to do. Ask:

Can analysts customize agent behavior, workflows, permissions, escalation boundaries, and objectives?
Can they do it in natural language, without weeks of engineering?
Can agents access only the systems, data, and tools an administrator explicitly authorizes?
Does the platform include enterprise-grade RBAC, governance controls, approval workflows, and policy guardrails?

What a strong answer sounds like: You define each agent’s role, scope, and authority, and you can adjust it as trust grows.

Red flag: Agents that can only be tuned by the vendor, or broad access with no granular permission model.

Transparency and Accountability

In the SOC, trust isn’t automatic… It’s earned. According to Torq’s 2026 AI SOC Leadership Report, 92% of security leaders cite at least one factor reducing their trust in AI, and black-box reasoning ranked among the top concerns, and was the number-one concern for SOC directors specifically.

An AI that hands down a verdict without showing its work doesn’t solve the trust problem. It defers it until the first false positive takes down a production system at 2am. Transparency is also what makes the platform defensible to auditors and boards. Ask:

Can the AI explain its reasoning for every verdict, recommendation, escalation, and action?
Are all actions, reasoning chains, overrides, and outputs captured in immutable audit logs?

What a strong answer sounds like: Every decision comes with its reasoning and evidence, and nothing happens that isn’t logged.

Red flag: “Trust the model” with no inspectable reasoning, or audit trails that capture outcomes but not the why behind them.

Scale and Proof

A demo runs in a sandbox. Your SOC runs in the complex real world at volume, under compliance requirements, with multiple business units and data residency rules. Plenty of new entrants look impressive on stage and fall apart on contact with that reality.

The only real proof is production: named customers, hard numbers, and metrics a CISO can take upstairs without a caveat. Ask:

Can the platform operate reliably at enterprise scale, with high alert volumes, real-time response, multi-tenancy, segmentation, and data residency support?
Can it prove operational impact — MTTR reduction, case closure rates, analyst time recovered, false positives eliminated — with the metrics a CISO can take to the board?

What a strong answer sounds like: Referenceable enterprise customers and specific, verifiable outcomes.

Red flag: Logos with no numbers, or numbers with no named customers behind them.

It All Comes Down to One Question

Read them together, and a single question emerges: Can the platform take action across your stack with reasoning you can see and controls you can govern, at the scale you actually operate? A triage-only tool answers “no” to half of these. A repackaged legacy product answers “no” to the rest.

If it can’t take action, it’s not an AI SOC. It’s one more thing to manage.

The Torq AI SOC Platform was built to answer yes to all 20. Auto Triage increases verdict velocity 60x, cutting MTTR and exposure time. Socrates builds, deploys, and orchestrates Torq HyperAgents™ across triage, investigation, response, and remediation, with transparent reasoning, adjustable autonomy, and enterprise-grade governance, all grounded in the Torq Context Graph. The platform runs more than one billion automated actions a week and is trusted in Fortune 500 production, with native metrics that a CISO can take to the board.

Ask any competitor to answer all 20. Torq does.

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Survive the AI SOC Apocalypse. Read the blog series.

Ready to automate everything?

“Torq takes the vision that’s in your head and actually puts it on paper and into practice.”

Corey Kaemming, Senior Director of InfoSec

“Torq offers unprecedented protection and drives extraordinary efficiency for RSM and our customers.”

Todd Willoughby, Director

“Torq saves hundreds of hours a month on analysis. Alert fatigue is a thing of the past.”

Phillip Tarrant, SOC Technical Manager

“The only limit Torq has is people’s imaginations.”

Gai Hanochi, VP Business Technologies

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts.”

Dina Mathers, CISO

“Torq has transformed efficiency for all five of my security teams and enabled them to focus on much more high-value strategic work.”

Yossi Yeshua, CISO

Get a Demo

AI SOC

The Four Biggest Gaps in Today’s AI SOC Vendor Market

By Torq

June 30, 2026

4 Minute Read

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A year ago, a handful of vendors called themselves an “AI SOC.” Today, more than 100 do. The label now means whatever the person selling it needs it to mean, leaving security teams to buy very different products under the same two words.

So let’s sort the market. Beneath the “agentic” branding, most AI SOC vendors fall into one of four categories, and none of them clears the bar. Each can look capable in a demo. Each shares the same flaw: it can tell you what’s happening, but it can’t actually do anything about it.

Here’s how to spot all four, and what you should be getting instead.

1. The Triage-Only Tool

This is the most common type. A lot of “AI SOC” startups focus almost entirely on triage, and to be fair, triage is a great fit for AI. It’s repetitive, high-volume, around-the-clock work that burns analyst time and attention.

But the job doesn’t end at triage. If your AI surfaces a verdict and then hands a to-do list to a human, it hasn’t reduced the workload. It’s just reorganized it. The investigation still happens by hand, the response still requires someone to log into another tool, and the case still closes on human time.

What you actually need: End-to-end lifecycle automation that carries an alert through investigation, remediation, and closure, with native case management and autonomous resolution for the cases that don’t need a human.

2. The Legacy Tool With Bolt-On AI

A legacy product wrapped in a chatbot and a few generative copilots is still a legacy product. Layering AI on top of an aging architecture can improve the experience at the surface, but it doesn’t remove the scalability and complexity limits that were there before. The same bottlenecks remain. They just have nicer branding.

What you actually need: A platform built for agentic SecOps from the ground up, designed not only to generate insights but to take action, coordinate workflows, and drive resolution autonomously, and built for enterprise scale from day one.

3. The Black Box

Too many AI SOC vendors expect teams to trust decisions they can’t inspect, tune, or control. In the SOC, trust isn’t automatic. It has to be earned. According to Torq’s 2026 AI SOC Leadership Report, black-box reasoning ranked among the top concerns for security leaders considering AI adoption, and for SOC directors specifically, it was the number-one concern.

If analysts can’t see why the AI reached a verdict or what data it touched, adoption stalls. Opaque reasoning breeds hesitation, double-checking, and operational risk, and out-of-the-box agents have limited value if teams can’t adapt them to their own workflows, policies, and risk tolerance.

What you actually need: Transparent agent reasoning, fully customizable agents, and user-defined logic and control, so every verdict is something your team can verify, tune, and govern.

4. The Shallow Newcomer

The newest vendors flooding the market can look slick in a demo, only to fall apart under the compliance requirements and complexity of a real enterprise environment. Most operate with shallow case memory, weak organizational context, and no ability to learn from analyst decisions. Every case starts from scratch, and the judgments your team has already made get buried in tickets and Slack threads instead of informing the next verdict.

What you actually need: Decisions grounded in your organizational context, memory that improves every verdict over time, native model context protocol (MCP) support, and an enterprise-grade platform proven in Fortune 500 production, not just a demo.

The One Question That Exposes All Four

The test is simple: Can it take action? A triage-only tool, a repackaged legacy product, a black box, and a shallow newcomer all stop at the same place. They analyze, they recommend, and then they hand the real work back to your team.

If it can’t take action, it’s not an AI SOC. It’s one more tool to manage.

A true AI SOC platform runs the complete threat lifecycle — triage, investigation, response, and case closure — with the transparency, context, control, and scale that enterprise operations demand. That’s the bar. Most AI SOC vendors don’t clear it.

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AI SOC Security Engineering

Torq Reflex: Teaching the AI SOC Judgment

By Noam Cohen, Head of AI

June 25, 2026

10 Minute Read

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Noam Cohen is a serial entrepreneur building seriously cool data and AI companies since 2018. Noam’s insights are informed by a unique combination of data, product, and AI expertise — with a background that includes winning the Israel Defense Prize for his work in leveraging data to predict terror attacks. As the Head of Artificial Intelligence at Torq, Noam is helping build truly next-gen AI capabilities into Torq’s autonomous SOC platform.

Your analyst tells the AI that the alert is just the red team running a scheduled pen test. They told it the same thing yesterday, and the day before. It works, then it doesn’t, then it does — and that flakiness is harder to live with than a tool that’s simply, predictably wrong.

This is happening in almost every SOC that runs AI triage, and the AI isn’t broken. It just has no memory of its own decisions. It looks at each alert individually, with no idea that your team has already settled this question a dozen times over. Every shift, it starts from scratch.

We tend to file the result under alert fatigue, but that undersells it. The higher cost is what happens to trust. Once analysts stop believing what the AI tells them, they treat it as one more box to click, and the money you spent on AI triage quietly buys you nothing. Earning that trust back is the problem the whole category has to solve next, and it’s the problem the learning layer of the Torq AI SOC Platform was built for.

Where First-Generation AI Triage Stops

The first wave of “AI SOC” tools proved that a large language model can read an alert and produce a credible analysis on day one, with no prior training on your environment. That breakthrough was great (at the time), and the day-one analysis it unlocked is part of the foundation Torq builds on. But it isn’t enough. A capable AI SOC has to keep learning, and the first generation treats day one as the ceiling.

That’s because the trouble starts after day one. The model judges every alert from scratch, and when an analyst corrects it, that correction is added to the prompt rather than the model itself. So the false positive your team carefully explained on Monday comes back looking brand new on Tuesday. Researchers call this self-inconsistency, an inherent failure mode of LLMs. Your analysts call it the reason they stopped trusting the tool.

We reviewed more than 1,000 real analyst corrections across four customer environments to understand why the AI and the analyst kept disagreeing. The reasons split cleanly into two groups.

About half the time, the AI was simply missing context. It flagged an admin who was authorized to do exactly what they did, or a piece of software the team already knew was safe, because nobody had told it who does what inside your walls. That kind of gap has a clear fix, described in the first two posts in our series: the Torq Context Graph and Torq Recall feed that organizational knowledge straight into the model. Together with Reflex, they form the memory and learning layer beneath every agent in the Torq AI SOC Platform.
The other half of disagreements are harder to reconcile, and they’re exactly what Torq Reflex was built for. In these cases, the AI wasn’t missing information. It had access to everything the analyst did, but still landed somewhere the team wouldn’t. Sometimes it contradicted its own earlier verdicts. Other times, it just read a borderline call differently than the team did, treating a contained threat as resolved when the team’s policy was to escalate it anyway, or accepting a thin set of signals as proof when the analysts wanted more. No amount of extra data closes a gap like that, because the gap is about judgment.

Why “Learning AI” From Other Vendors Often Isn’t

Everyone in this space says their AI “learns” now, and they rarely mean the same thing by it. Usually, it comes down to one of two moves: the tool rewrites its own prompt based on your feedback, or it saves your past corrections and pulls up similar ones when a new alert looks familiar. Both are useful. Both run into the same wall.

You can’t prompt your way to consistency. There’s no instruction that makes a model with no memory return the same verdict it gave yesterday; that takes a model that actually remembers what it decided.

Your team’s risk tolerance has the same problem. Something like “we treat blocked executions as malicious” isn’t really a rule. It’s a pattern that only becomes visible once you’ve watched the team rule the same way across a pile of messy edge cases. Calibration works like that, too. Analysts learn how much evidence is enough by being wrong and adjusting, not by being told to relax.

Prompts and lookups can describe your team’s judgment well enough. But they can’t absorb it; they’re limited and inconsistent, and they offer no real confidence in the answer. That’s the line Reflex crosses.

There’s a quieter problem, too: model swapping. As providers upgrade and retire the underlying LLMs, behavior shifts beneath you. Our research shows you have to tune both the prompt and the model, but when the model changes, customer instructions rarely get retuned along with it, which creates more inconsistency and less confidence at the worst possible time. Because Reflex is a stateful model trained on your team’s own calls, it stays resilient to those model changes.

How Torq Reflex Works

The easiest way to picture it is Spotify. Out of the box, Spotify plays music that appeals broadly to everyone: the hits and the editorial playlists. That’s the equivalent of the LLM baseline, and every AI SOC ships with a version of it. Discover Weekly is the part makes Spotify feel personal, built from what you play and what you skip.

Reflex plays the role of Discover Weekly. The general model is the editorial playlist that works for any customer, while Reflex is a model trained on how your team specifically makes decisions, learning more every time an analyst corrects it. It isn’t pulling the nearest past case off a shelf. It’s a classifier that has taken your patterns on board well enough to judge alerts it has never seen before.

Walk an alert through it, and the flow is simple. The alert arrives, and Reflex — a trained language model — assigns the most likely verdict along with a calibrated confidence score. If that confidence is high, its verdict shapes the output. If it isn’t, the full LLM analysis runs, and the case goes to an analyst, just as it would if Reflex weren’t there at all. Whatever the analyst decides flows back in and retrains the model, so each correction is worth a little more than the last.

And because Reflex lives inside the Torq AI SOC Platform, a confident verdict doesn’t just label an alert and stop. It feeds Auto Triage and case creation, and then orchestrates the response from Socrates across Torq HyperAgents™. So the judgment your team teaches Reflex makes every downstream action across the full threat lifecycle more trustworthy.

That confidence number is the advantage of training a model instead of running a search. A lookup can tell you an alert resembles something a human fixed once. Reflex gives you a real, calibrated probability, which is what makes the whole arrangement safe to run. It weighs in when it’s sure and holds its tongue when it isn’t, so it can only help: anything it’s uncertain about goes right back to the pipeline you already trust.

Reflex is a purpose-built language-model design, engineered to learn from a small number of samples and to operate under asymmetric risk, where missing a malicious verdict is far more dangerous than mislabeling a benign one. The same math lets you set your own tolerance for the two kinds of mistakes. Missing a real threat hurts more than chasing a false alarm, so by default, Reflex treats a missed malicious error as the costlier one, and you can dial that weighting to match how your organization actually thinks about risk. It beats typing “THIS IS VERY CRITICAL” into a prompt and hoping the model takes the hint.

What the Data Shows

We ran Reflex against the semantic-similarity approach most “learning” platforms lean on, across those same three environments.

The number we care about most is the performance on alerts where the AI was wrong and a person had to step in. Reflex matched the analyst’s corrected verdict 92% of the time and held within about three points of that across folds. The similarity approach managed 78%. On overall accuracy, the trained model came out roughly 11 points ahead, and on the call that carries the most weight — confirming a real threat — it gained around 22 points over the baseline.

A tight spread means the result isn’t a fluke, and Reflex gets there in roughly two weeks of normal analyst feedback. The early weeks are bumpier while the model finds its feet, then it sharpens with every retraining pass. Most AI triage systems perform exactly the same on day 100 as they did on day one. Reflex draws a curve you can put in front of your board.

What It Means for Your Team

Fewer repeat corrections: Your analysts stop re-teaching lessons that the system should already know. A correction made once carries forward to every alert like it.
Consistency across shifts: The same alert lands on the same verdict, whether it’s a senior analyst at 2pm or a newer one at 3am.
Real confidence scores: You get a calibrated probability instead of hedge language, so high-confidence cases move fast while the genuinely uncertain cases land in front of a person for judgment.
Error trade-offs you control: You decide how much worse a missed threat is than a false alarm, and the model enforces it.
A curve you can measure: The system improves in ways you can actually report on, quarter after quarter.
Built for oversight: The EU AI Act‘s high-risk rules take effect in August 2026. Reflex’s confidence-based routing with a human fallback already aligns with the Act’s oversight and transparency requirements.

Your Data, Your Model

Your model is yours and no one else’s. We don’t pool training data across customers, nor do we share parameters between them. Your red team’s patterns and your policy calls never leave your tenant or quietly shape a competitor’s experience. The model works for you because you’re the one who trained it.

That’s what this series has been building toward. That same data discipline is what makes a genuine human-on-the-loop model possible. Most SOCs run human-in-the-loop out of necessity. An analyst checks the AI on nearly every alert, because there’s no reliable way to know which verdicts to trust.

Reflex changes that calculation because it produces a calibrated confidence score grounded in your team’s own calls, it can act on the cases it’s sure about and send the uncertain ones to a person. Your analysts shift from reviewing everything to supervising the system and stepping in where their judgment actually counts. They stay on the loop, with an auditable record of what the model decided and why.

This is the final piece of our series, Context, Memory, and Learning in the AI SOC: context grounds the agents, memory gives them precedent, and learning teaches them your team’s judgment. Together, they’re the layer that makes an autonomous AI SOC something you can actually trust.

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AI SOC

Claude Mythos Broke the SOC. Agentic AI Fixes It.

The rules of offensive security just changed. Here's what every SecOps leader needs to do about it.

By Torq

June 19, 2026

9 Minute Read

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When Anthropic unveiled Claude Mythos Preview in April 2026, the security industry felt the ground shift. First, in a controlled research environment, Mythos autonomously discovered thousands of previously unknown vulnerabilities spanning every major operating system and web browser, including flaws that had survived decades of expert human review. And perhaps most impactfully, it then developed working exploits, without human guidance, at a rate no team of human researchers could match.

At first, Anthropic withheld Mythos from public release, channeling it instead through Project Glasswing to focus the model’s capabilities on defense. Since then, on June 9, 2026, Anthropic released a public version as Claude Fable 5. Even though the full Mythos 5 model remains restricted, the implications were already clear to security leaders. The economics of finding and exploiting software flaws have collapsed. This capability, in the hands of attackers, has fundamentally altered defenders’ collective calculus.

Manual SOC processes architected in an era that predates agentic AI now teeter on the precipice of obsolescence. The urgency shifts from detection to containment and remediation. Machine-speed threats demand machine-speed response. And SOC practices must be reborn and embrace agentic AI if they are to respond at the pace this new reality demands.

The Economics Collapsed. You Know This. So Do Attackers.

Claude Mythos is remarkable not only for its detection capability, but also, and especially, what that capability means in the wrong hands. We want to be clear: this is not fear-mongering. It is a call to action.

Historically, sophisticated cyberattacks required sophisticated attackers. Identifying a zero-day vulnerability in a major browser, chaining it with a privilege-escalation flaw, and building a working exploit required years of experience, deep technical knowledge, and significant time. That barrier kept the most dangerous attacks in the hands of nation-state actors and elite criminal groups.

Mythos obliterates that barrier. Detection is now commoditized.

Security analysts have characterized it plainly: tasks that once required specialist skills (ie, writing exploit code, understanding system architecture, using advanced attack tooling) can increasingly be automated using AI. What once separated a script kiddie from an elite threat actor was expertise. Mythos-class AI can supply that expertise on demand.

The AI-augmented low-skill attacker is born. Someone with minimal technical background can now point a capable AI at a target, receive a map of exploitable vulnerabilities, and get working attack code in return. What once took a nation-state months can now take an amateur hours. One security researcher put it starkly: handing a similarly capable model to bad actors would be “like giving script kiddies a nuclear weapon.”

We think that analogy appropriately frames the urgency. The democratization of exploitation capability is unfolding before our eyes in real time. We are asking you to break the glass and give this the attention it deserves. Now.

Manual SOC Practices Are Already Obsolete

The traditional Security Operations Center was built for a different threat landscape. Analysts triaged alerts manually, investigated incidents by hand, and escalated through human decision chains. Even as SIEM and SOAR tools added automation, the core model remained human-paced: see, think, act.

That model cannot survive in a world shaped by Mythos-class adversaries.

When attackers can generate targeted, sophisticated exploits at machine speed, the attack chain evolves faster than any human-centric operation can run. Vulnerabilities are identified, weaponized, and exploited in cycles measured in hours, perhaps even minutes, but certainly not weeks. The attacker’s tempo has permanently outpaced manual response.

The consequences are predictable: alert fatigue intensifies as detection volume spikes, critical signals get buried in noise, and analysts face an impossible triage workload. The attackers overwhelm the defenders. Triage becomes the bottleneck at exactly the moment that speed matters most.

Enterprise defenders have no choice but to adapt. The organizations that recognize this shift and act now to restructure their operations amidst this new normal will be the ones that contain Mythos-era attacks. Those who don’t will find themselves perpetually responding to breaches they could not prevent.

The Bottleneck Has Moved

Detection is no longer the hard part.

Modern threat detection has matured considerably. EDR, NDR, SIEM, and cloud security tools collectively generate enormous signal fidelity. Seeing a threat does not mean automatically neutralizing it. The bottleneck has shifted from detection to response action.

An alert fires. A SOC Analyst pulls context from five different tools, assesses scope and severity, and decides on containment. They document the recommended response action and initiate communication and coordination with the security control owner. All of this, to say nothing about whether they chose the alert that represents the greatest threat to the enterprise. Because let’s face it, the threat needle resides in an alert haystack. Maybe multiple haystacks. Meanwhile, the attacker has pivoted, moving laterally and establishing persistence. More alerts, more noise, more pressure.

Even for experienced analysts, response time falls off the adversary’s pace set by an automated, focused attack moving at machine speed. The detection sensor is not the SOC’s critical constraint; the gap between detection and action most certainly is.

Machine-Speed Threats Demand Machine-Speed Response

If the attacker is operating at machine speed, the defender must too. Easier said than done? Perhaps not as difficult as you imagine. Stay with me.

Machine-speed response does not mean removing humans from the loop entirely. Nor does it mean turning the keys entirely over to AI. It does mean restructuring operations so that humans provide critical oversight, define guardrails, and review high-stakes actions, while AI handles the high-volume, time-sensitive work that comprises a large share of security operations.

Agentic AI makes this new SOC architecture possible. Hence the term, “AI SOC.” Unlike traditional automation, which executes static playbooks, agentic AI reasons through novel situations, plans multi-step response actions, and executes across integrated systems without requiring human intervention at every step. It can triage an alert, enrich it with threat intelligence, correlate it against historical activity, determine the appropriate response, and execute containment — in seconds, at any hour, across unlimited concurrent incidents.

In a post-Mythos world, the most successful SecOps leaders will (1) structure their operations to use agentic AI to augment their human staff, (2) combine agentic AI and automation to slash MTTR, (3) balance agentic and deterministic automation for economic investment horizon optimization, and (4) build trust in agentically augmented systems through strategic scoping and small wins that build momentum.

How Torq Helps: The AI SOC Platform Built for This Moment

Torq built its AI SOC Platform for exactly the threat environment that Mythos crystallized. Recognized by Gartner® as the Company to Beat in AI SOC Agents for Threat Investigation (May 2026), Torq’s platform is purpose-engineered to close the gap between detection and response at the speed this era demands.

Multi-Agent Architecture

The Torq AI SOC Platform runs a multi-agent system (MAS), with Socrates serving as the agentic orchestrator, overseeing specialized AI agents that work in parallel across triage, investigation, containment, and case management. Each agent accesses only the data you specify and takes only the actions you authorize, within the scope you define. All agentic reasoning and actions are documented, transparent, and auditable.

Autonomous Triage

Torq Auto Triage is the agentic engine that stops noise before it floods your SOC. Auto Triage is fully integrated with the Torq AI SOC Platform and your security stack, to (1) ingest, normalize, and analyze telemetry at machine speed, (2) reveal your biggest risks, and (3) automatically open cases for true positives. Torq has some compelling tech under the hood that learns and adapts to how your SOC operates, so our model becomes your model. Where manual triage took 60 minutes, Auto Triage completes in seconds.

Investigation at Machine Speed.

Cases are automatically opened within Torq Case Management. Agentic insights, timelines, and evidentiary artifacts are automatically added to each case, which serves as the single source of truth for analysts and stakeholders. Socrates leads machine-speed investigation, tasking the specialized Torq HyperAgents™ to offload gruntwork from human analysts and get to the recommended course of action quickly, in minutes, not hours.

Autonomous Response with Guardrails

With Torq, autonomous response is a controlled dial, not a binary decision of human or agent. With recommended containment and remediation plans in hand, you decide what level of automation and under what conditions makes the most sense for your enterprise. Start small, build trust, and expand as you go. We have Fortune 500 enterprises using Torq to autonomously handle 100% of Tier 1 incidents. Mean time to respond (MTTR) is reduced by an average of 94% in our enterprise installed base.

Imagine what a 94% improvement in MTTR would mean for your SOC and your enterprise.

Agentic Builder for Continuous Adaptation

As the threat landscape rapidly evolves post-Mythos, Torq’s Agentic Builder enables security teams to translate human natural-language intent into production-grade AI agents and dynamic business logic. Simply, easily. What once took months is now done literally in minutes.

A Fortune 500 Retailer transferred four years of legacy SOAR to the Torq AI SOC Platform in days, freeing time to develop new use cases they never had bandwidth to get to before. Fully operational and deployed in under three weeks.
A Global Hospitality Enterprise ripped out SOAR, upgraded to the Torq AI SOC Platform, and was fully operational and deployed in six weeks.
A Commercial Real Estate Firm realized instantaneous value on Day 1, and now 60% of phishing cases are handled fully autonomously

New attack patterns demand new responses; Agentic Builder makes that adaptation achievable.

The Mythos moment dramatically reduced the barrier to entry to sophisticated attacks, compressed timelines, and raised the volume of what defenders must handle. The Torq AI SOC Platform was built to absorb that pressure, so your analysts can focus on what AI cannot replicate: human judgment, strategy, and context-aware oversight at the edge of the unknown.

The Clock Is Running

Claude Mythos is a preview of the threat environment that is coming. In some ways, this threat environment is already here. Attackers with AI assistance can already find, weaponize, and deploy exploits at a pace that manual SOC operations never anticipated and simply cannot match. The organizations that respond by building agentic defense capabilities that now work alongside their human experts will be significantly better positioned than those waiting for the next incident to prompt action.

The bottleneck has moved from detection to response. The economics of sophisticated attacks have collapsed. The SOC clock is running. The right time to build a machine-speed defense was before Mythos. The second-best time is now.

To learn more about how Torq’s AI SOC Platform can help your team meet the Mythos moment, get a demo.

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Todd Willoughby, Director

“Torq saves hundreds of hours a month on analysis. Alert fatigue is a thing of the past.”

Phillip Tarrant, SOC Technical Manager

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Gai Hanochi, VP Business Technologies

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AI SOC Security Engineering

Recall Brings Implicit Learning to the SOC

By Rony Fluk and Noam Cohen

June 15, 2026

9 Minute Read

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Key Takeaways

The manual grind is over: True autonomous security shouldn’t require you to manually tag alerts or write rigid correlation rules just to teach a system how your SOC operates.
Generic RAG fails in the SOC: Standard AI semantic searches hallucinate and match irrelevant data. High-stakes SecOps requires deterministic precision.
Automated institutional memory: Torq Recall transforms an analyst’s everyday workflow — resolving cases and writing notes — into actionable, automated triage precedent.
Honest AI builds trust: In messy, real-world environments, Torq Recall handles conflicting historical data by explicitly flagging discrepancies rather than blindly guessing.

AI SOC memory is the difference between an investigation that starts from your team’s history and one that starts from zero. In most security operations centers, there is none: context disappears the moment a case closes. When a new alert fires, the investigation effectively starts from zero. Analysts are forced to rely on search interfaces — however advanced — to find similar past cases and determine whether an IP, file hash, or domain has been seen before and what the team concluded during the last investigation. But in an agentic SOC, even great search is still manual work; relevant precedent should be retrieved automatically at triage time.

Legacy case management systems weren’t built to retain this operational memory; they were built for tickets. To compensate, these systems require analysts to spend significant time manually gathering and connecting evidence to turn a raw alert into an actionable case. In other words, they demand that the analyst work for the system.

Without an automated way to recall past relevant resolutions, analysts repeatedly reinvestigate the exact same benign patterns the organization has already dismissed, creating the SOC equivalent of déjà vu and wasting valuable time. This inflates false-positive noise, drives alert fatigue, and keeps institutional knowledge siloed — meaning that when a senior analyst leaves, their tribal knowledge walks out the door with them.

True autonomous security requires a completely different architecture. A modern AI-native SOC shouldn’t force teams to change their habits or do extra data entry. Instead, it relies on implicit learning. By turning normal daily workflows — resolving a case and leaving a note — into continuous institutional memory, Torq Recall uses deterministic retrieval to automatically triage future threats based on an organization’s actual history.

The Cost of the Blank Slate

When an analyst investigates an alert without historical context, the problem is not just trivial duplication. The harder problem is recognizing patterns that are tightly connected to the specific user, asset, IP, domain, or behavior involved in the alert. For example, a login anomaly from a suspicious IP may appear risky in isolation, but historical cases may show that the same user and asset repeatedly trigger this pattern after legitimate travel or VPN use, and that the SOC has already validated it as benign.

External threat intelligence — like reputation scores and known IOC databases — is critical for establishing a baseline. But the ultimate context for triaging a new alert is internal: How did your specific organization resolve this exact pattern in the past? Was it tied to a legitimate corporate CDN? Was it an expected admin script?

Without an automated memory of past resolutions, false-positive noise inflates, and analysts burn out from repetitive manual triage.

Why Generic RAG Can’t Survive the SOC

As the industry integrates AI into security operations, Retrieval-Augmented Generation (RAG) has emerged as a popular approach for surfacing historical data. But standard semantic RAG struggles to meet the precision requirements of a modern SOC.

Generic RAG is designed to find semantically similar information. That works well for documents, summaries, and natural language knowledge. It breaks down when the “meaning” resides within security entities such as IP addresses, file hashes, URLs, hostnames, users, assets, or email addresses.

In security, two values can look almost identical and still represent completely different evidence. For example, two hash-like values such as 5jshdh2kfw and 5jshdh2yfw may look semantically close to a model, but in SecOps they are different identifiers and may point to completely different files. A near match is not evidence. In a high-stakes environment, “close enough” can shatter analyst trust.

Torq Recall rejects fuzzy precedent matching in favor of Structured Retrieval. Instead of guessing, Recall relies on exact, deterministic overlaps of specific security observables — like IPs, file hashes, URLs, hostnames, or emails — to trigger a historical match. This ensures that every AI-driven triage recommendation is auditable, explainable, and grounded in verifiable evidence from your own environment.

But exact matching is only the first step. Recall also has to understand the signal strength. A match on a rare file hash, unique URL, or unusual email address can carry strong precedent. A match on a common binary like explorer.exe, or a widely shared corporate IP is much weaker. Torq Recall is designed to focus the AI on the security entities that actually matter — not just the ones that happen to overlap.

Deterministic Context Matching: Unlocking the Goldmine

Resolved cases are the raw material for AI SOC memory — the operational record of how your team actually works. Instead of leaving this context buried in closed tickets, Torq Recall seamlessly connects today’s raw alerts with yesterday’s finalized human reasoning.

When a new alert hits, Recall instantly searches your environment’s case history for relevant precedent. It does not treat every historical match equally: cases are prioritized based on exact observable overlap, signal strength, and recency. Because security environments change quickly, Recall applies a time-aware decay factor, allowing fresh resolutions to carry more weight while still preserving older institutional knowledge when the evidence is strong.

Crucially, Recall doesn’t just count past alerts; it analyzes the final resolution decision and the analyst notes.

AI in the Trenches: Handling SOC Chaos

In a real SOC, the data is rarely clean. Alerts arrive with partial context, telemetry is noisy, and human decisions are not consistently captured. Two similar cases may receive different labels, or the analyst’s notes may tell a more nuanced story than the final resolution field. If an AI system is designed in a sterile lab under the assumption of perfect data hygiene, it will inevitably fail when it encounters real-world ambiguity.

Torq Recall was designed not just to find historical matches, but to critically evaluate the quality and consistency of that precedent. Using rigorous LLM-in-the-loop stress testing, the AI was trained on curated case histories that mimic the messiest edge cases in a live SOC.

The guiding design principle was to build an “honest AI” that exhibits calibrated confidence. Instead of blindly forcing a recommendation when data is sparse or contradictory, Recall includes a subagent architecture that is instructed to act like a veteran analyst: it weighs the evidence, spots inconsistencies, and knows exactly when to ask questions.

The system gracefully manages real-world SOC chaos by identifying and adapting to complex scenarios like the ones below.

Real-World SOC Scenario	What was Encountered	How Torq Recall Handles It
Conflicting Precedent	Half of the historical alerts were closed as False Positives, while the other half were escalated as True Positives	Refuses to pick arbitrarily. Recall lowers its confidence score, explicitly flags the split precedent, and recommends the team “Investigate Further”
Misleading Labels (e.g., Red Team)	A past case is labeled “True Positive/Malicious,” but the analyst notes state: “Update: Part of a concluded red team exercise”	Prioritizes the narrative context in the notes over the static resolution label, recognizing that the threat is no longer active, and adjusts its recommendation accordingly
Severity Mismatches	Historical cases sharing an IP address were Critical-severity attacks, but the new alert is a Low-severity informational event	Highlights the severity mismatch as a key difference, ensuring it does not blindly inherit the escalation precedent of a completely different attack type
Weak Signal Overlap	The system finds a match, but it relies on a single, low-fidelity observable (such as a common corporate IP address)	Openly acknowledges the weak precedent, returns a “Low Confidence” assessment, and refuses to force a definitive recommendation
Inconsistent Data Entry	An alert is coded with a “False Positive” reason, but the detailed resolution notes describe actively blocking malicious activity	Flags the contradiction between the label and the details, lowering confidence to prevent the SOC from trusting bad data

To make this behavior reliable, we built a comprehensive testing and evaluation suite based on real-world data, including conflicting precedents, misleading labels, weak observable overlap, severity mismatches, and inconsistent analyst notes.

Recall is evaluated against these scenarios to ensure it surfaces discrepancies transparently instead of hiding them behind a black-box verdict. When the AI is honest about uncertainty and catches inconsistencies in past documentation, it builds the kind of trust analysts need before relying on automated triage.

From Tribal Knowledge to Institutional Memory

Letting the intelligence of your security team disappear the moment a ticket is closed is an architectural flaw that modern SOCs can no longer afford. Torq Recall fixes this by capturing analyst expertise implicitly, turning it into durable AI SOC memory without adding a single click to the workflow.

But this is just the foundational layer of institutional memory. We are currently expanding Recall’s contextual reach beyond closed cases. By integrating directly with the Torq data fabric, the system is evolving to capture deeper organizational context and fetch richer historical signals directly from your alerts. For an AI-native SOC, more context directly translates to better triage quality: the broader the historical data pool, the more precise and reliable the AI’s precedent matching becomes.

Ultimately, this continuous learning layer is being integrated across Torq’s wider product ecosystem, including Socrates — our AI SOC orchestrator. Building a resilient SOC doesn’t mean writing more rigid rules; it means deploying an AI teammate that seamlessly learns from every resolution your team makes.

Torq Recall is one piece of a larger body of work on how we build trustworthy, agentic AI for security operations. Our series on Context, Memory, and Learning in the AI SOC breaks down the architecture, testing methods, and design decisions behind the AI SOC.

Read About Torq Context Graph

Rony Fluk is a Senior AI Engineer at Torq, focused on building AI-native capabilities for modern security operations. He brings deep experience in LLM systems, automation, and agentic AI, designing deterministic, context-aware automation that helps SOC teams preserve institutional knowledge and make faster, more reliable decisions.

Noam Cohen is a serial entrepreneur building seriously cool data and AI companies since 2018. Noam’s insights are informed by a unique combination of data, product, and AI expertise — with a background that includes winning the Israel Defense Prize for his work in leveraging data to predict terror attacks. As the Head of Artificial Intelligence at Torq, Noam is helping build truly next-gen AI capabilities into Torq’s autonomous SOC platform.

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Corey Kaemming, Senior Director of InfoSec

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Todd Willoughby, Director

“Torq saves hundreds of hours a month on analysis. Alert fatigue is a thing of the past.”

Phillip Tarrant, SOC Technical Manager

“The only limit Torq has is people’s imaginations.”

Gai Hanochi, VP Business Technologies

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts.”

Dina Mathers, CISO

“Torq has transformed efficiency for all five of my security teams and enabled them to focus on much more high-value strategic work.”

Yossi Yeshua, CISO

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AI AI SOC Integrations

From Alert Triage to Remediation: The Actual AI SOC Lifecycle

By Bob Boyle

June 12, 2026

12 Minute Read

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The first half of 2026 is coming to a close. And unless you live under a rock, there is only one takeaway: The AI SOC revolution is here, and Torq is winning.

It started in January 2026: Torq secured our $140 million Series D, led by Merlin Ventures, with every single one of our existing investors doubling down, launching Torq into unicorn status at $1.2 billion and fueling the future of the agentic SOC.

Shortly after, Forbes said Torq is the “de facto leader of the AI SOC space,” noting that while the AI SOC boom is real, the work here at Torq started long before the buzz.

In March 2026, Torq became the Cursor of Security Operations, with agentic building capabilities that turn human intent into production-grade AI Agents in minutes, capable of handling triage, investigation, and response across every security solution in the SOC. This was a game-changer for CISOs and SOC leaders looking for a quick-start button for their AI SOC initiative.

In April 2026, Torq was named a Leader by KuppingerCole Analysts in all four categories for the AI SOC Category: Overall, Product, Innovation, and Market in the 2026 KuppingerCole Analysts Leadership Compass: The Emerging AI SOC (Document #81057)

Then, in May, in the Gartner® report AI Vendor Race: Torq Is the Company to Beat in AI SOC Agents for Threat Investigation (Document ID: G00855833), Gartner names Torq the Company to Beat.

It’s no surprise that the AI SOC space is moving fast, but it’s clear that Torq is leading the pack. So let’s talk about why and how.

The AI SOC Land Grab

The threat landscape is massively different than it was two years ago, heck, even six months ago. Attackers are using AI to develop and deploy the most convincing attacks ever seen, at a lower cost than ever before, and with a lower technical barrier to entry. According to CrowdStrike’s 2026 Global Threat Report, AI has enabled the bad guys to ramp attacks 89% year over year, with the fastest break-in they tracked taking only 27 seconds. What used to take months to build now takes hours.

Human-speed defense is no longer the answer to machine-speed attacks, and the market understood that. The problem is, the market overcorrected. Every vendor with an AI chatbot or a triage-only point solution wrapped their website in AI SOC messaging.

To start 2026, roughly 60 vendors claimed to be “AI SOC”; now there are over 100. We’ve seen the confusion firsthand, with different analyst firms defining the space in completely different ways. But the ones who spend the time to do the research — Gartner, KuppingerCole, and more — are reporting a consistent through line: end-to-end threat management, deep integrations across the entire security portfolio, and enterprise scalability.

Some influencers even claim the AI SOC market is already commoditized; we completely disagree. That’s a convenient argument if your product is triage-only, because that space is crowded and there’s no sign of growth slowing any time soon. The space is not commoditized; it’s fragmented. Broken up by hundreds of vendors calling different tools by the same name, leading to SOC teams making purchase decisions they will regret in six months when they realize agentic triage and an AI SOC platform are not one and the same, and defining it as such only works if you believe triage is all the SOC ever needs to do. But as we know, the work doesn’t end there.

Triage-Gate: The AI SOC Definition Rendition

Let’s be specific about what triage-only tools do and don’t do.

Triage does one thing: it prioritizes risk. It tells you which alerts are real and which aren’t, ranks them by severity, and recommends what should happen next. It doesn’t matter if you work in a Fortune 100 enterprise security operations center, or work in the front of an Emergency Room at the hospital. Triage, by definition, remains the same. Who is bleeding out and needs immediate emergency medical intervention? Who is more benign and can be handled with a simple automated email quarantine, followed by an IP address added to the company block list?

Here’s what triage doesn’t do: It doesn’t investigate a threat. It doesn’t contain it. It doesn’t remediate it. It doesn’t close the case. It simply analyzes the risk, reaches a conclusion, hands a to-do list to a human analyst, and then it’s done.

That means triage-only AI still ends with a human opening a ticket at the start of the actual security work. The investigation is still happening manually, and the evidence is gathered by hand, across disparate tools and queries. The response action requires an incident responder to log into a separate platform and make the necessary calls.

That is, by no means to say that AI alert triage isn’t valuable. In fact, alert volume has become so unmanageable in modern SecOps that, in Torq’s 2026 AI SOC Leadership Report, 97% of respondents reported confidence in AI’s ability to address the problem. However, from that same report:

37% are actually using AI for triage
90% reported challenges with AI Triage
80% rely on multiple point-specific tools
80% say this creates operational complexity

And therein lies the problem. The queue is prioritized, sure, but the SOC is still functioning at human speed. The bottleneck has moved. The enterprise remains exposed. The attackers still have the upper hand.

Torq Auto Triage: Going Beyond Analysis

This is not a knock on triage as a function; it’s a knock on calling it an AI SOC when the job isn’t finished. Torq Auto Triage is a key function of the Torq AI SOC Platform. Our customers leveraging Torq Auto Triage report 60x improvement in triage velocity, a mean-time-to-triage (MTTT) of 45 seconds, at a 97% reduction in EDR noise alone — with ruthless accuracy across hundreds of thousands of alerts per week in some of the largest Fortune 500 companies in the world.

Torq Auto Triage is the agentic engine that applies business context, threat intelligence, and historical case knowledge to deliver verdicts, suppress noise across your SOC, and prioritize threats that actually matter before they become incidents. Most importantly, however, Torq Auto Triage is fully integrated into the entire Torq AI SOC Platform, which then takes the baton through deeper investigation, containment, and remediation actions — all while continuously improving accuracy by grounding every decision in the Torq Context Graph.

A true AI SOC has to do everything a SOC does — not triage only, but manage the complete threat lifecycle from alert through resolution. That means triage, investigation, response, and case closure. And that is the difference.

Torq’s deep-rooted history in agentic SecOps and automation means we are not only capable of triaging the alerts firing from every corner of your SOC, but investigating and responding to threats across every security tool you integrate into the platform.

The complete security stack, the complete threat lifecycle, the complete AI SOC Platform.

The AI SOC Bar Should Be Higher

The AI SOC market is crowded, and it’s only getting louder with new vendors emerging from stealth mode what seems like every day. Most of them solve the narrow problem of triage, leveraging AI to ingest alerts, enrich them with threat intelligence, and surface a verdict to the awaiting human team. Others are beginning to add threat hunting capabilities to the mix, not because it’s the logical next step, but because it’s the only remaining SOC function that doesn’t require them to crack the code on taking action.

That should be table stakes. The bar needs to be higher. And Torq is setting it.

The questions SOC teams need to ask are:

Once you’ve triaged the alert and surfaced the risk, what happens next?
Who handles the investigation?
Does the AI agent contain the threat and stop the spread before wasting more valuable exposure time escalating the proposed response plan to the analyst?
Can the platform automatically remediate the Tier 1 and Tier 2 threats that don’t require human intervention?
Does it integrate with the security stack your team has already built to ensure complete visibility into every possible IOC?
Does it learn from, and remember, analyst exceptions, escalations and deviations from SOP so its verdicts aren’t based on last month’s playbooks, before the next VoidLink-level AI-generated threat changes everything about how your SOC operates?

If the answer stops at “we surface the risk,” then that is a useful tool, but it’s not an AI SOC.

Per our 2026 research, 92% of security leaders cite at least one factor actively reducing their trust in AI in the SOC today. Black-box reasoning was the number-one concern for SOC directors specifically. An AI that hands you a verdict without showing its work doesn’t solve the trust problem; it defers it until the first false positive blows up a production system at 2am. Deploying agentic AI without the right guardrails is its own category of risk.

What a Real AI SOC Platform Looks Like

The Torq AI SOC Platform runs the complete end-to-end threat lifecycle, and fast. At Carvana, Torq handles 100% of Tier 1 security alerts. A global biotech enterprise recently reported a 97% noise reduction in EDR alert triage and a 92% decrease in mean-time-to-resolve (MTTR) from the full Torq AI SOC Platform, in just their first quarter, leveraging the platform.

Torq Agentic AI handles
100% of Tier 1 security alerts.

Carvana

In the first quarter, Torq delivered a 97% noise reduction in EDR alert triage and a 92% decrease in MTTR.

Global Biotech Enterprise

These are real stories, across real enterprise customers, and they don’t stop there. Torq processes more than 1 billion automated actions across our customer base each week, including Chipotle, LEGO, Marriott, Procter & Gamble, Scotts, Siemens, Valvoline, and Virgin Atlantic.

When Gartner named Torq the Company to Beat in AI SOC Agents for Threat Investigation in May 2026, they mentioned Torq’s “combination of deterministic and agentic reasoning, multi-agent system, and model context protocol integration makes it the pacesetter in AI SOC agents for threat investigations.” ¹

Here’s how it works:

Torq Auto Triage handles ingestion and prioritization, normalizes alerts to the Open Cybersecurity Schema Framework (OCSF), enriches every alert with commercial and OSINT threat intelligence, plus your own organizational context, and applies agentic reasoning to separate real risk from noise. Verdicts come with MITRE ATT&CK® mapping, severity scoring, and full transparency into how the conclusion was reached. True positives don’t generate a recommendation; they automatically become cases in Torq Case Management, with context already assembled and next steps already queued.

From there, Torq Socrates^TM, the core orchestrator of the Torq AI SOC Platform, takes over. Socrates plans and coordinates specialized Torq HyperAgents™ that investigate cases, gather evidence, build timelines, and document their reasoning in real time. SecOps engineers simply describe what they need in plain language, and Socrates agentically builds these production-ready AI agents in minutes.

None of this works without the deep integration of Torq Hyperautomation^TM, and this is where the breadth matters. Torq has over 400+ pre-built integrations across EDR, IAM, SIEM, Network, Email, Cloud, and more. Creating a new integration point to feed alerts into the Torq AI SOC Platform is simple with Socrates’ agentic builder capabilities. Whether a use case is common or completely custom, the platform can handle it — and building new automated security actions no longer requires weeks of engineering work. Bottom line, your existing security stack stays.

With the platform configured and the triage verdicts rolling in, Socrates orchestrates the whole operation, managing case handoffs, executing response actions, and closing cases when the work is done. Over 90% of cases close completely autonomously, while the analysts focus on what actually needs human judgment.

Finally, the Torq Context Graph and memory layer powers every agentic decision in a live, continuously updated model of your environment, grounding each verdict in your environment’s truth and your analysts’ past judgments.

Two mechanisms power that memory: Recall, which surfaces the most relevant historical case precedents each time a new alert arrives, and Reflex, a per-tenant model, developed by Torq Labs, that trains continuously on your team’s confirmed verdicts and corrections. This means every Auto Triage verdict, every Torq HyperAgents investigation, and every autonomous Socrates response is based on the same organizational context, improving the decision-making with each alert prioritized and case closed. A single through line, grounding the AI SOC in real context, across the complete SecOps lifecycle.

The Verdict is the Start, Not the Finish

The AI-generated threat landscape that SecOps teams are operating in today is not going to simplify. Attacks will get faster, more convincing, and harder to keep up with. The velocity gap between machine-speed offense and human-speed defense cannot be closed by a tool that prioritizes risk and then hands it back to a human.

You need an AI SOC Platform that goes beyond analysis — triages, investigates, contains, and remediates at machine speed, with complete transparency into every decision. That’s the blueprint 450 security leaders described when asked what a real AI SOC should do, and that’s what the Torq AI SOC Platform delivers.

Triage gets you to the starting line, but what wins the race is everything that comes after it.

See the full threat lifecycle in action. Get a demo.

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¹Gartner, AI Vendor Race: Torq Is the Company to Beat in AI SOC Agents for Threat Investigation (Document ID: G00855833)

Gartner, AI Vendor Race: Torq Is the Company to Beat in AI SOC Agents for Threat Investigation, Tom Powledge, Matt Milone, 25 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Ready to automate everything?

“Torq takes the vision that’s in your head and actually puts it on paper and into practice.”

Corey Kaemming, Senior Director of InfoSec

“Torq offers unprecedented protection and drives extraordinary efficiency for RSM and our customers.”

Todd Willoughby, Director

“Torq saves hundreds of hours a month on analysis. Alert fatigue is a thing of the past.”

Phillip Tarrant, SOC Technical Manager

“The only limit Torq has is people’s imaginations.”

Gai Hanochi, VP Business Technologies

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts.”

Dina Mathers, CISO

“Torq has transformed efficiency for all five of my security teams and enabled them to focus on much more high-value strategic work.”

Yossi Yeshua, CISO

Get a Demo

Contents

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The Self-Learning Torq SOC Brain

Torq Recall: Memory That Actually Works

Torq Reflex: Your Team’s Judgment Applied

Torq Retrospect: Informed Decisions From Day Zero

What The Torq SOC Brain Looks Like In Practice

Your Judgment. Your Model. Your Intelligence.

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