AI SOC Hyperautomation MSSP Security Automation 101

SOC Automation for MSSPs: The 2026 Guide

By Torq

March 20, 2026

9 Minute Read

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TL;DR

Alert volumes have surged by more than 300% over the past 5 years. But MSSP pricing hasn’t kept pace. SOC automation is the only path to profitable scale.
Legacy SOAR and playbook-based automation can’t keep up. The shift is from scripted execution to agentic AI that reasons, adapts, and acts autonomously.
The biggest barrier to AI adoption in the SOC isn’t capability; it’s trust. Full auditability and explainability are non-negotiable, especially for MSSPs serving compliance-sensitive clients.
MSSPs evaluating SOC automation platforms should prioritize: Autonomous action, native multi-tenancy, deep integrations, and built-in ROI tracking.

Alert volumes are higher than ever. Client budgets are not. For managed security service providers, that math doesn’t work and no amount of hiring will fix it.

The MSSPs that are scaling profitably right now aren’t doing it with more analysts. They’re doing it with smarter automation. But SOC automation for MSSPs means something very different in 2026 than it did two years ago. This guide breaks down what it actually means, why legacy approaches are failing, and how to evaluate whether a platform can deliver real operational leverage for your business.

What Is SOC Automation?

SOC automation is the use of technology to execute security operations tasks — alert triage, enrichment, investigation, containment, and remediation — with minimal or no human intervention.

In practice, that means replacing the manual, repetitive work that consumes most of a Tier 1 analyst’s day: copy-pasting indicators between tools, running the same enrichment lookups on every alert, filling out tickets, and making low-stakes disposition decisions that follow the same pattern every time.

The goal is to stop wasting analysts’ time on work that doesn’t require human judgment.

For MSSPs specifically, SOC automation addresses the most painful structural realities of running a managed security practice:

Multi-tenant scale. You’re managing security for dozens or hundreds of clients simultaneously, each with different environments, tools, and risk tolerances.
24/7 coverage requirements. Threats don’t stop at 5pm, but staffing around the clock is expensive.
Margin pressure. Alert volume has grown dramatically; client pricing has not.
Talent shortage. Analyst burnout is endemic — 70% of SOC analysts with fewer than five years of experience leave within three years.

Without SOC automation, none of these pain points gets better.

Why Most SOC Automation Falls Short

Not all SOC automation is created equal, and a lot of what’s marketed as “automation” is really just slightly faster manual work.

First-generation SOC automation was built on SOAR platforms that let teams write playbooks. A phishing alert arrives, the playbook runs a series of steps, and if everything goes as expected, a ticket gets created. It was better than nothing. But it came with limitations.

Playbooks are brittle. They break when APIs change, when a new threat variant doesn’t fit the expected pattern, or when a client modifies their stack. Maintaining them at scale is a part-time job in itself.

The other problem: playbooks execute steps. They don’t think. They can’t adapt to a novel attack chain, correlate signals across multiple clients, or make a judgment call when something doesn’t fit the template. For a single-tenant enterprise SOC, that’s manageable. For an MSSP running hundreds of tenants, it becomes a ceiling on how much you can scale.

What the market is moving toward — and what leading MSSPs are already adopting — is SOC autonomy: AI-driven systems that don’t just follow scripts but reason through investigations, adapt to new threat patterns, and take goal-driven action. For a deeper look at how MSSP cybersecurity is evolving in 2026, this breakdown covers the key trends shaping the market right now.

The Real Benefits of SOC Automation for MSSPs

When AI-driven SOC automation for MSSPs is working the way it should, the operational impact is significant. Here’s where managed security providers see the most measurable gains.

Scale without adding headcount. The most direct benefit. With the right automation in place, a single analyst can effectively oversee what used to require a full Tier 1 team. Leading AI SOC platforms achieve 90%+ autonomous Tier 1 alert handling, meaning the vast majority of incoming alerts are triaged, investigated, and resolved without a human ever touching them.

That’s not a marginal improvement. That’s a fundamentally different operating model.

Faster MTTR across every client. Automated triage and enrichment happen in seconds, not minutes. When a phishing email hits a client’s inbox, an AI-driven workflow can analyze the message, pull threat intelligence, verify the user’s account status, quarantine the message, and close the ticket — all before an analyst would have even opened the alert. Mean time to response (MTTR) drops from 45 minutes or more to under five.

Margin protection. Every alert your platform handles autonomously is an alert your analysts don’t have to touch. That reduces cost-per-alert, cost-per-client, and the pressure to hire ahead of growth. It also frees senior analysts to focus on high-value services — threat hunting, client advisory, proactive risk assessments — that command better margins and differentiate your offering.

Analyst retention. Burnout is the talent crisis hiding inside the talent shortage. When analysts spend their days grinding through repetitive triage work, they leave. When automation absorbs that grind, they stay and do more interesting work. That’s good for your team and it’s good for your clients.

Multi-tenant operational consistency. Standardized, automated workflows mean every client gets the same quality of response, every time, regardless of which analyst is on shift. Centralized visibility with client-specific customization is how MSSPs turn consistency into a selling point. For a closer look at what this kind of AI-powered MSSP model looks like in practice, the Hyperautomation for MSSPs guide walks through the operational details.

Automation vs. Autonomy: Why the Difference Matters in 2026

The 2026 AI SOC Leadership Report surveyed 450 CISOs and security leaders and found that 94% of organizations are already using AI in the SOC in some capacity — but the average team is running seven different AI tools, most of them disconnected. 85% said they’d prefer a unified AI SOC platform to managing multiple point solutions. That fragmentation is both a symptom of the problem and a reason why basic automation continues to fall short.

The distinction that matters right now is between automation and autonomy.

Automation executes predefined steps. A playbook fires, checks a box, sends a notification. It’s deterministic. It does exactly what it was told to do, no more.

Autonomy means an AI system can reason with context, adapt when something unexpected happens, and take goal-directed action — not because it was scripted to do so, but because it understands the goal. When an alert fires, an autonomous system enriches across your SIEM, EDR, identity provider, and cloud environment, correlates related signals, makes a verdict, and either remediates or escalates with full context documented. No human touched it unless escalation was warranted.

The 2026 AI SOC Leadership Report also found that 97% of security leaders are confident AI can handle triage — but only 35% are actually using it there.

That gap isn’t a capability problem. It’s a trust problem. The number-one barrier cited was visibility: teams can’t see what the AI did, why it made the decision it made, or how to audit it after the fact. For MSSPs who have to demonstrate security outcomes to clients, that’s a critical gap. Establishing where human authority sits within AI governance is increasingly part of how mature SOC teams build that trust internally and with clients.

The platforms worth evaluating in 2026 close both gaps: autonomous action and full explainability.

5 Questions to Evaluate SOC Automation Platforms

Not every platform that calls itself “SOC Automation” delivers autonomous operations. Here’s a practical checklist for cutting through the noise.

1. Does it act or just advise? Can the platform autonomously execute containment and remediation, or does it surface recommendations for human approval? There’s a place for human-in-the-loop workflows, but if every action requires analyst sign-off, you haven’t actually automated anything.

2. Is it built for multi-tenancy? Can you manage hundreds of client environments from a single platform with client-specific customization at scale? This is non-negotiable for MSSPs. Generic enterprise platforms often bolt multi-tenancy on as an afterthought.

3. How does it handle integration complexity? Your clients don’t all run the same stack. Does the platform support your full range of SIEMs, XDR tools, EDR vendors, identity providers, cloud environments, and ticketing systems — with pre-built integrations that actually work? AI agents built for the SOC should be able to pull context from across the environment, not just one or two connected tools.

4. Is it explainable and auditable? Can you show clients exactly what the AI did, why it did it, and when it did it? This is where the trust barrier lives, according to the 2026 AI SOC Leadership Report. Both compliance requirements and client trust depend on transparency. If you can’t explain an AI decision, you can’t defend it.

5. Can you measure ROI? Does the platform track MTTR, automation rates, alert clearance volume, and analyst hours saved? Your clients want outcomes, not activity. You need the data to prove value and to price your services accordingly.

What This Looks Like in Practice

Use Case: Alert volume at Scale

An MSSP managing 50+ clients is drowning in alerts and missing SLAs. Tier 1 analysts spend their entire shift triaging, and escalations are backing up. With autonomous SOC automation, Tier 1 triage runs continuously across all tenants simultaneously — no shift changes, no queue backlogs. Analysts handle escalations only. Alert coverage goes from reactive and inconsistent to 90%+ autonomous.

Use Case: Phishing Response

A phishing campaign hits a client’s inbox. Each report historically required manual enrichment, user verification, and remediation steps. With an AI-driven workflow, the platform analyzes the email header and payload, cross-references threat intelligence, notifies the affected user via Slack, quarantines malicious messages, and closes the ticket. Phishing response time drops from 45 minutes to under five — across every affected client, simultaneously.

The AI SOC Platform Built for MSSPs

The Torq AI SOC Platform is purpose-built for the way modern SOCs actually operate and for the specific demands of multi-tenant managed security. Specialized AI agents handle triage, investigation, remediation, and case management autonomously, coordinated by Torq Socrates, an AI SOC analyst that reasons across the full alert context rather than executing a fixed script.

For MSSPs, that means:

90%+ autonomous Tier 1 alert handling across all client tenants
Full audit trails showing every action taken and the reasoning behind it
300+ pre-built integrations covering the breadth of tools your clients actually run
Multi-tenant architecture built in, not bolted on
Built-in support for MSSP-specific workflows, from onboarding to SLA reporting

The SOC org chart is already changing at the organizations leading this shift. The MSSPs that win in 2026 won’t have the most analysts. They’ll have the smartest automation.

Ready to see what 450 security leaders said they want from an AI SOC?

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FAQs

What is SOC automation for MSSPs?

Modern SOC automation for MSSPs is the use of AI-driven technology to handle security operations tasks — including alert triage, threat enrichment, investigation, containment, and remediation — across multiple client environments with minimal human intervention. Unlike single-tenant enterprise deployments, MSSP SOC automation must operate at scale across dozens or hundreds of clients simultaneously, making native multi-tenancy and consistent workflow standardization essential requirements.

How does SOC automation differ from SOAR?

SOAR (security orchestration, automation, and response) platforms use predefined playbooks to execute scripted steps when specific conditions are met. SOC automation in 2026 goes further, leveraging agentic AI that can reason through alert context, adapt to novel threats, and take autonomous action without a pre-written script for every scenario. SOAR executes. Agentic AI thinks.

What is the ROI of SOC automation for MSSPs?

The clearest ROI metrics include reduced cost-per-alert, lower analyst headcount requirements per client, faster mean time to response (MTTR), and improved SLA performance. MSSPs using advanced SOC automation platforms typically achieve 90%+ autonomous Tier-1 alert handling, which directly reduces service delivery labor costs and creates capacity to take on more clients without proportional headcount growth.

What should MSSPs look for when evaluating SOC automation platforms?

The most critical criteria are autonomous action (not just recommendations), native multi-tenant architecture, broad pre-built integrations across common security stacks, full auditability of AI decisions, and built-in ROI reporting. MSSPs should be skeptical of platforms that require significant playbook maintenance, lack multi-tenant support, or can’t demonstrate transparent decision-making — all of which undermine the scalability and client trust that automation is supposed to deliver.

How does AI change the MSSP analyst role?

AI doesn’t eliminate the analyst role; it elevates it. By automating Tier-1 triage and routine enrichment tasks, AI allows analysts to focus on higher-value work: complex incident investigation, threat hunting, client advisory, and strategic security improvements. According to the 2026 AI SOC Leadership Report, 9 in 10 security leaders view AI oversight as meaningful work, not overhead — a signal that the analyst role is evolving, not disappearing.

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 Hyperautomation Security Automation 101

SOAR is Dead. Here’s What Replaces It in 2026.

By Torq

March 18, 2026

11 Minute Read

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TL;DR

Legacy SOAR was built for a slower threat landscape. Static playbooks, custom scripting, and 12–18 month implementations can’t keep pace with threats that move at machine speed.
The right SOAR replacement isn’t a better playbook engine. It’s an AI-native platform built on agentic AI and Hyperautomation that investigates every alert, adapts to novel threats, and delivers ROI in days, not months.
Migration doesn’t mean starting over. Your tried-and-true workflows run faster on Hyperautomation, and the agentic AI layer adds everything legacy SOAR never could: autonomous investigation, adaptive triage, full case management, and remediation at scale.

When SOAR emerged around 2015, it was trying to solve a real problem: SOC analysts were drowning in manual, repetitive tasks across disconnected tools. SOAR promised to connect those tools, automate the workflows between them, and give analysts their time back. For a while, it mostly delivered.

That era is long dead.

Attackers now move at machine speed, leverage AI to scale their campaigns, and use techniques that evolve faster than any playbook library can track. Meanwhile, legacy SOAR platforms are still running on the same architectural premise they launched with a decade ago: build a playbook for every scenario, script every integration by hand, and hope your engineers never leave.

The evidence of the breakdown is everywhere. IDC found that 83% of SOC analysts struggle with alert volume. The SANS 2024 SOC Survey found that automation had become the top barrier to effective SOC operations, ranking higher than staffing shortages. That’s not a tooling gap. That’s a category failure.

In 2025, GigaOm renamed its SOAR Radar to the SecOps Automation Radar, acknowledging that the category had moved on. The question for security leaders in 2026 isn’t whether to replace legacy SOAR. It’s what the replacement actually needs to look like.

Why Legacy SOAR Can’t Be Fixed With More Playbooks

Before evaluating what comes next, it’s worth being clear-eyed about why legacy SOAR failed. The problems aren’t cosmetic. They’re architectural.

The playbook ceiling is real. Legacy SOAR can only automate what someone has already anticipated and coded. Every scenario requires a custom playbook built and maintained by a security engineer. New threat types, updated tool integrations, and evolving attacker techniques mean playbooks are perpetually incomplete or outdated.

Most organizations automate 30–40% of their alert volume at best, leaving the rest to queue up or go uninvestigated entirely. According to the SACR 2025 AI SOC Market Landscape, 40% of alerts are never investigated. Of those that are, 90% turn out to be false positives. That’s the real return on a legacy SOAR investment.

Integration sprawl compounds the problem. Legacy SOAR relies on custom scripting to connect your tools. Every new integration is a new maintenance commitment. At enterprise scale, this creates a fragile web of interdependencies that consumes engineering time without a corresponding increase in coverage. When one vendor updates their API, a cascade of playbooks can break simultaneously.

The talent dependency is unsustainable. The engineers who built your SOAR playbooks are the same engineers every company in your industry is trying to hire. When one leaves, they take the tribal knowledge encoded in your automation with them. Legacy SOAR’s reliance on custom scripting creates a dependency on scarce, expensive talent that compounds in cost every year. The economics of an agentic SOC make an increasingly compelling case for making the switch.

Alert fatigue isn’t a people problem. It’s a platform problem. When automation only covers a fraction of alert volume, the gap falls on human analysts. That sustained overload drives burnout, attrition, and the kind of alert fatigue that causes real threats to get missed. Adding more analysts to a broken process doesn’t fix the process.

More playbooks don’t solve these problems. Better playbook management doesn’t solve them either. The architecture itself is the constraint. If you want to understand just how broken the model has become, the SOAR is Dead Manifesto lays it out plainly.

What the Best SOAR Replacement Actually Looks Like

The strongest AI-driven SecOps automation platforms in 2026 don’t look like SOAR. They were built from scratch around a different set of assumptions: that not every threat can be anticipated in advance, that AI should reason through problems rather than match them to templates, and that automation should be accessible to every analyst, not just the engineers who can write Python.

Here’s what separates a genuine next-generation platform from a rebranded version of the same architecture:

It’s built on AI-native design, not AI as an afterthought. The platforms worth evaluating were built around agentic AI from the ground up. Agentic AI reasons through security scenarios dynamically, planning, investigating, and executing actions based on context rather than matching alerts against static rules. This distinction is critical: AI layered on top of playbook logic remains bounded by it. Agentic AI investigates threats for which no playbook exists. Understanding how AI should actually work in your SOC is the right starting point for any evaluation.

Hyperautomation is the foundation, not the feature. True security Hyperautomation means elastic, cloud-native workflow execution that scales with alert volume without degradation. Not a serial queue that backs up during volume spikes, exactly when you need your automation most. Look for platforms that can execute millions of automations daily and that let any analyst easily build and modify workflows, not just your most senior engineers.

Autonomous case management instead of a separate ticketing system. In most legacy SOC environments, case accountability is scattered across ticketing tools, chat threads, and analyst memory. Nobody has the full picture of an incident without manually assembling it from five different tools. The best SOAR replacements unify detection, investigation, and case lifecycle management in a single place, automatically creating cases from correlated alerts, enriching them with context from across the stack, and tracking every action from detection through resolution. When leadership asks what happened and how the team responded, the answer should live in the case record, not in someone’s head.

Any analyst can build automations, not just your engineers. If only two people on your team understand how your automation works, your platform is a single point of failure. Modern Hyperautomation platforms enable analysts to create, modify, and deploy workflows using natural language or a no-code visual builder. The best platforms reduce engineering dependency rather than requiring it as a baseline.

300+ native integrations with no custom scripting. Assess the native integration library depth, the quality of those integrations, and whether the platform can generate new connectors programmatically when needed. Custom scripting required per tool is a red flag. It’s the same maintenance trap that makes legacy SOAR expensive to scale.

Governance is built into the architecture. Automation and AI without governance accelerates risk. The best platforms build governance into the operating model: configurable approval gates for high-impact actions, scope limits on what AI agents can touch, and immutable audit trails for every AI decision and automated action. This isn’t a compliance checkbox. It’s the architecture that makes autonomous operations safe enough to trust at scale and defensible to auditors, insurers, and the board.

Time-to-value measured in days, not months. Ask every vendor for actual customer proof, not projected timelines. The best platforms get priority use cases live in days to weeks. If a vendor can’t point to customers who were live and generating measurable ROI within the first month, that tells you something.

Six Things the Right SOAR Replacement Delivers for Your SOC

Together, those capabilities define what an AI SOC platform actually is — not a rebrand, but a fundamentally different way of operating. The right SOAR replacement doesn’t just close the gaps left by legacy tools. It changes what your SOC can do entirely.

Here’s what that looks like for your team.

1. You go from automating tasks to automating outcomes. Legacy SOAR automates workflow steps. AI-native Hyperautomation automates entire outcomes — investigation, enrichment, triage decision, and response action — without a human orchestrating each stage. Instead of automating only the cases that have playbooks, you’re covering every case that hits your queue. The benefits of an AI SOC compound fast once the coverage gap closes.

2. Alert coverage goes from 30–40% to 100%. When agentic AI investigates every alert, including scenarios for which no playbook exists, nothing falls through the cracks. The best AI SOC platforms close over 90% of Tier 1 cases autonomously. The coverage gap that defined legacy SOAR simply stops existing.

3. Your engineers stop maintaining automation and start building strategy. When the platform handles playbook logic dynamically, your security engineers stop burning cycles on maintenance and start solving harder problems. That shift from automation janitor to strategic contributor is one of the most consistent things security leaders report after moving off legacy SOAR.

4. Response times compress from hours to minutes. Time-to-contain is the metric that matters most in a real incident. AI-native platforms don’t queue work serially; they execute at machine speed across every alert in parallel. The compounding effect of faster triage, faster enrichment, and faster response changes your MTTD and MTTR in ways that playbook tuning never could. This is especially critical in high-stakes scenarios, such as ransomware protection, where minutes matter.

5. The tribal knowledge problem disappears. When institutional automation knowledge lives in the platform rather than in a senior engineer’s head or a Python script nobody else understands, your team stops being one resignation away from a coverage collapse. Any analyst can build, understand, and modify workflows, so the system gets smarter over time instead of more fragile.

6. Every action is captured, every case tells the full story. Modern AI-native platforms build governance into the architecture: immutable audit trails for every AI decision, configurable approval gates for sensitive actions, and case records that hold up in a post-incident review. Real-time SOC dashboards give leadership full visibility into case status, SLA performance, and operational trends in one place. When your CISO, your compliance team, or your cyber insurer asks what happened and how you responded, the answer is already documented.

This is What Torq Was Built For

If the capabilities described above sound like they were written with a specific platform in mind, they were.

The Torq AI SOC Platform is purpose-built to replace legacy SOAR. It’s the only platform that combines Torq Hyperautomation™ — executing orchestration workflows at 10x the speed of legacy SOAR with 300+ native integrations and 4,000+ actions — with a Multi-Agent System that plans, investigates, and responds to threats autonomously.

At the center of the Torq AI SOC Platform is Socrates, Torq’s AI SOC Analyst. It coordinates Torq’s AI Agents to autonomously handle Tier 1 case triage, investigation, and remediation, escalating only what genuinely requires human judgment. This isn’t a chatbot layer over legacy automation. It’s an agentic system that reasons through security scenarios at machine speed, documents every decision, and learns from analyst feedback over time. Learn more about what an AI SOC platform should actually do before making your decision.

Autonomous case management means every alert is automatically correlated into a case, enriched with context from across your stack, prioritized by business impact, and tracked from detection through resolution. Kenvue — protecting household brands including Johnson’s, BAND-AID, and Neutrogena — launched end-to-end autonomous case management in six weeks on Torq.

The results from teams that have already made the switch are hard to argue with:

Carvana uses Torq agentic AI to handle 100% of Tier 1 security alerts and automated 41 runbooks within one month of deployment.
Valvoline replaced their legacy SOAR, went live in 48 hours, and saves six to seven analyst hours every single day.
RSM migrated 200+ managed MSSP customers to the Torq platform in three weeks and now automates 82% of global customer cases.
Lennar Corporation replaced their legacy SOAR deployment and cut phishing remediation from hours to minutes.
Deepwatch standardized its entire global security infrastructure on Torq. Their Sr. Director of Solutions Engineering noted the analyst environment they’ve built would never have been achievable with legacy SOAR.
Check Point uses the Torq platform to react automatically to problems before they become security incidents, eliminating alert fatigue despite a 30% manpower gap.

GigaOm named Torq a Leader and Outperformer in the SecOps Automation Radar for three consecutive years, specifically recognizing Hyperautomation capabilities that legacy SOAR platforms can’t replicate. And with a recent $140M Series D, Torq is accelerating the next phase of the agentic SOC era.

Your SOAR Had Its Run. See What Comes Next.

Legacy SOAR is dead. The teams still on it aren’t just dealing with a dated tool. They’re managing a coverage gap that widens every quarter, a maintenance burden that consumes engineering capacity, and an architecture that fundamentally cannot keep pace with how threats move in 2026.

The right replacement doesn’t automate more tasks. It automates outcomes: every alert investigated, every response executed at machine speed, every action auditable, and your analysts focused on work that actually requires human judgment.

Ready to make the move?

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FAQs

What should replace legacy SOAR in 2026?

The right SOAR replacement is an AI-native platform built on agentic AI and Hyperautomation, not a better version of the same playbook-driven architecture. The key capabilities to look for are full alert coverage, autonomous case management, low-code/no-code and AI workflow building accessibility for all analysts, 300+ native integrations without custom scripting, built-in governance, and time-to-value measured in days. The Torq AI SOC Platform was built specifically to deliver all of these and is named a GigaOm Leader and Outperformer for three consecutive years.

What's the difference between SOAR and AI-native Hyperautomation?

SOAR automates predefined workflows through static playbooks that engineers build and maintain. AI-native Hyperautomation uses agentic AI to reason through, investigate, and respond to alerts dynamically, including threat scenarios for which no playbook exists. SOAR covers a subset of known, repeatable processes (typically 30–40% of alert volume). The Torq AI SOC Platform investigates 100% of alerts at machine speed, with the Hyperautomation layer handling known workflows and the agentic layer handling everything else.

How long does it take to migrate from legacy SOAR to a modern platform?

With the right platform, migration happens in days to weeks, not months. Valvoline replaced their legacy SOAR and achieved ROI within 48 hours. RSM migrated 200+ managed customers in three weeks. The key is a platform with a structured migration path, native integrations that don’t require custom scripting, and an implementation program designed for fast time-to-value. See how to migrate →

What is the Torq AI SOC Platform?

The Torq AI SOC Platform combines Torq’s Hyperautomation engine with agentic system to triage, investigate, and autonomously remediate security cases at machine speed. At its core is Socrates, Torq’s AI SOC Analyst, which coordinates specialized AI Agents to handle the full Tier 1 case lifecycle from alert enrichment through remediation, escalating to human analysts only when genuinely required. The platform closes more than 90% of security cases autonomously and is trusted by enterprise security teams and MSSPs globally.

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 News & Events Security Engineering

From Intent to Outcome: How Agentic Coding is Transforming the SOC

By Ofer Smadari, CEO & Co-Founder

March 17, 2026

6 Minute Read

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Security teams are being asked to move faster and handle more complexity, while the threats they defend against are increasingly AI-assisted. When I wrote about VoidLink in January, my point was simple: you cannot fight machine-speed threats with human-speed defense. Attackers are using AI to code, adapt, and scale attacks while humans are still grinding away doing the heavy lifting in the SOC.

Earlier this year, Torq raised our $140M Series D to build the agentic SOC, where machines fight machines. This requires AI that goes far beyond just triaging alerts or summarizing threats. The agentic SOC must cover the complete SecOps lifecycle — from triage to fix, from Tier 1 to Tier 3, from builder to responder.

Simply better automation isn’t enough. Agentic automation is.

Today, we’re announcing Agentic Builder — a critical extension of the Torq AI SOC Platform, and the most significant step we’ve taken toward making the agentic SOC a practical reality for every security team.

The Problem Hasn’t Changed

The SOC’s struggle isn’t a people problem. The security teams I speak to every day are sharp, dedicated, and deeply skilled. The problem is legacy security models that expect human beings to act like machines, doing repetitive work at a pace and scale that human beings will never be able to sustain.

We’ve spent the last few years solving the first half of that problem, deploying agentic AI to handle the triage, investigation, and response that was drowning analysts. That’s working. Our customers are closing over 90% of security cases autonomously. Carvana is handling 100% of their Tier 1 alerts with Torq AI Agents. The average tenure of a security analyst using Torq is increasing, and teams are handling more work without adding headcount.

After successfully delivering AI capabilities that have freed SOC analysts from overwhelming alerts, false positives, and fatigue, Torq now liberates SecOps engineers and architects from the manual tedium that delays value realization. Torq is ensuring defenders move faster than attackers — autonomously, intelligently, and without limits.”

– Ofer Smadari, CEO and Co-Founder, Torq

But there’s a second major constraint to address: the engineering bottleneck. Building and maintaining the agents that do this work still requires human effort. It requires skilled engineers to create and maintain workflows as new threat categories emerge, format security cases, and write the logic for custom AI agents.

Hyperautomation’s no-code automation and drag-and-drop building solved a lot of the pain surrounding security engineering caused by legacy SOAR, but there is still a baseline of work hours that need to be dedicated to the maintenance overtime.

And if VoidLink taught us anything it is that “agentic coding” is accelerating threat engineering. Malware that once took months to create can now be produced in less than a 2-week agile sprint. It is not fair to expect humans to fight back against that level of machine-speed engineering. The agentic SOC must address every source of SecOps fatigue across the full threat lifecycle, not just a single piece of the larger puzzle.

That’s the problem Torq’s Agentic Builder solves.

What is Agentic Coding, and Why Does It Matter?

If you work in software development, you’ve watched what Cursor has done to engineering productivity. It didn’t just autocomplete code or create a chatbot that would discuss what code might look like. It moved to autonomous, multi-file execution — reading the full codebase, understanding dependencies, writing orchestration logic, and producing working output.

The shift wasn’t incremental. It was categorical.

Agentic coding is when an AI autonomously plans, writes, executes, and iterates on code to complete multi-step development tasks. The same categorical shift is now possible in security operations, which is exactly what we built here at Torq.

Within SecOps, agentic coding means ingesting a high-level security objective, planning, building across available security tools, running validation tests, and iterating until operationally correct in a production SOC environment. The AI operates with full system context, breaks down complex intent-based goals, executes independently, iterates against real feedback, and produces production-ready outputs.

This shift the cognitive load of engineering security automation from humans to machines, taking SecOps from “here’s a workflow template for you to start with” to “here’s a fully working security agent that is already integrated across your stack”

From Intent to Working Agent

Torq Agentic Builder builds production-grade AI agents from natural language prompts through contextual analysis, planning, and testing — effectively turning human intent into agentic outcomes in minutes.

Here’s what Agentic Builder actually does:

A SOC engineer or security architect describes what they need. Something like: “Correlate EDR alerts with suspicious login attempts and known malicious IPs, map to MITRE ATT&CK, and escalate based on severity.”
From that intent, Agentic Builder — part of Torq Socrates, the core orchestrator of the Torq AI SOC Platform — takes over to:
- Read your integrations, available APIs, existing workflows, runbooks, and case schemas
- Plan the assignment, selects the right tools, and defines guardrails
- Write the orchestration logic
- Build a deployable Torq HyperAgents™
- Test it against real scenarios before anything goes live — showing you every step, tool call, and output so you can refine behavior until it matches how your SOC actually runs

Nothing deploys without your explicit approval so humans remain the on-the-loop reviewers while the machine handles the execution, and heavy lifting, at machine speed. The output isn’t a template or a suggestion — it’s a working security agent, already integrated across your stack, ready to manage alerts 24/7.

What Agentic Coding Means for Security Teams

The historic tradeoff in security automation has been speed versus control. You could move fast and accept the risk or move carefully and fall behind the threat, but neither option was good enough. Agentic Builder eliminates that tradeoff.

With agentic coding, security engineers and architects can now design and operationalize sophisticated, agentic security workflows in minutes — without sacrificing governance, transparency, or control. Each agent is tested against real data before deployment, surfacing every decision for review, and continuously monitoring and auto-calibrating the SecOps workflow in production to eliminate the risk of drift.

That frees your best people to do what they do best: threat hunting, strategic risk decisions, and high-stakes incident response.

Where We’re Headed: Security Engineering at Machine Speed

Torq raised our Series D because we believe that the future of security operations is agentic, and we are uniquely positioned to deliver that reality. Not AI as a feature bolted on or another point solution, but full threat lifecycle management — from alert through remediation — with humans in control and machines doing the work.

Agentic Builder is the next chapter in that story. It means the Torq AI SOC Platform doesn’t just run your SOC, it helps you build it, scale it, and continuously improve it while keeping pace with an adversary that never slows down.

Torq is providing exclusive demos of Agentic Builder for qualified RSAC attendees, March 23-26, at Booth #527, South Expo Hall, Moscone Center in San Francisco.

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AI AI SOC Security Automation 101

AI or Die: Where Human Authority Must Ultimately Sit

By John White

March 16, 2026

10 Minute Read

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John White is the Field CISO for EMEA at Torq. A respected security executive with more than 20 years of leadership experience, John previously served as CISO at Virgin Atlantic, where he led a multi-year transformation deploying the Torq AI SOC Platform to modernize cyber operations. Prior to that, he built and transformed security functions for global organizations, including ASOS, Liberty Global, AEG Europe, and KPMG.

There’s a growing acceptance that AI is no longer optional in security. That battle is largely won. The more interesting question — and the one I keep getting asked — is what we actually believe AI should be responsible for, and where human authority must ultimately sit.

It’s a governance question. And right now, most organizations are getting it wrong.

Not because they’re being reckless. But because they’re thinking about AI governance the same way they thought about governing ChatGPT usage: as a risk to be managed rather than a capability to be designed.

That’s the wrong frame entirely.

Especially as technologies like Model Context Protocol (MCP) — the mechanism by which AI models communicate with each other — start to reshape the landscape in ways most governance frameworks aren’t remotely equipped to handle.

So let me share how I think about this. Where AI can and should own the work. Where humans must stay in the loop. And what a governance model that’s actually fit for purpose looks like in 2026.

The Accountability Gap: Extreme Ownership Starts With the CISO

Let me start with the question I get asked more than any other: If AI makes the wrong call and a breach happens, who’s accountable?

The answer is straightforward, even if it’s uncomfortable: the CISO.

It’s no different from recruiting a senior analyst you believed in, and they make a catastrophic mistake. The analyst may be at fault — but your head is on the block.

AI is the same. The CISO’s responsibility is to validate the technology, validate the approach, test the effectiveness, test the outcomes, and play in that judgment space in a safe environment before letting it anywhere near the enterprise. Then go through every step to de-risk it as much as possible. That accountability doesn’t transfer to the vendor. It doesn’t transfer to the board. It sits with you.

It’s a mindset Navy SEALs Jocko Willink and Leif Babin captured perfectly with the concept of Extreme Ownership — the idea that leaders must take full responsibility for everything in their world, including failure, with no excuses and no ego.

It’s one of the core values at Torq, and honestly, it’s a big part of why the culture resonated with me when I joined. Because this is exactly how I’ve always approached security leadership. You don’t get to point at the AI. You don’t get to point at the vendor. You own it.

And once you accept that, the whole question of where to draw the governance line becomes a lot clearer.

What AI Should Own, What It Should Inform, and What Stays Human

I think about this in terms of the three-layer model I outlined in the first piece in this series: Outcome, Judgment, and Execution.

In that model, the execution layer is where AI and automation operate — continuously, consistently, at machine speed, within predefined guardrails. This is where AI earns its keep in the AI SOC: Repeatable, rules-based, high-volume work. Tier 1 triage. Alert enrichment. Containment actions that are reversible, well-understood, and within clearly defined boundaries.

The judgment layer is where humans must stay in the loop. This is where I draw the line — and it’s not an arbitrary one. The decisions that require human authority are the ones that demand business context. Risk appetite. The political environment you’re operating in. The company’s financial situation. The strategic direction the board is pursuing this quarter.

No matter how well-trained an AI agent is, no matter how much historical incident data it can pull from, it will never have its finger on the pulse of all of that. You could add it to the knowledge base — but full contextual judgment isn’t something you can upload. That’s where humans must sit.

The outcome layer is where the strategic intent lives. This is entirely human. What are we trying to protect? What does success look like? How do we measure it? AI can inform this layer — surface patterns, highlight gaps, accelerate analysis — but it cannot define it.

The more capable AI becomes, the more important it is to be precise about where human authority is non-negotiable.

AI Trust Isn’t Given. It’s Earned.

One of the most common mistakes I see is organizations trying to go too fast, too soon. They see the potential, they’re under pressure to deliver results, and they push AI into complex, high-stakes decisions before they’ve built the foundation of trust that those decisions require.

Here’s how I think about the right sequence for building trust with AI: least critical to most critical, least complex to most complex.

Start with lower-level, repeatable tasks. Build workflows. Run them. Review the outcomes. Ask the honest question: did the workflow you just built actually achieve the outcome you wanted? If yes, take the learning and move further along the stack. If not, go back through the process, improve it, and run it again.

It’s a continuous improvement loop — the objective is to build trust incrementally as you go. And it’s the only approach that’s actually sustainable.

Think about how trust works — with a new colleague, a new friend, a new direct report. It’s never given. It’s earned through consistent actions that match intent. You start small, observe, and expand as the track record develops. And when something doesn’t go as planned, you use it to recalibrate, not give up.

Building trust with AI is no different. The actions the system takes are a direct reflection of the foundations and boundaries you built: the workflows you designed, the guardrails you set, the outcomes you defined. If it’s producing the right results, that’s your foundation holding. If it isn’t, that’s the feedback loop telling you to go back and rebuild before you go further.

You Can See Automation. You Have to Trust AI.

The apprehension around AI in SecOps is significantly higher than the apprehension around traditional security automation, and for good reason. With automation, the input-output relationship is transparent. With AI — particularly agentic AI — the system is making a learned judgment about what should happen next. That’s a fundamentally different kind of relationship to build.

To get comfortable with AI, CISOs need to go back to the basic building blocks. Understand how decisions are being made. Understand what guardrails are in place. Understand what the boundaries are. And then expand them deliberately, as the evidence builds. Just like you would with anyone new you’re learning to trust.

What Governance Actually Needs to Cover

Most governance models being applied to AI right now were designed to manage GenAI usage — the “who’s using ChatGPT” era of governance. They’re not built for governing AI within security tooling itself. And they’re certainly not built for what’s coming next with MCP, where AI models are communicating with each other in ways that create entirely new chains of decision-making and action.

When I think about a governance model that’s actually fit for purpose, I see three dimensions:

The people dimension treats AI as you would a new employee. What decisions is it authorized to make? What requires escalation? What is it never permitted to do? These aren’t technical questions. They’re policy questions, and they need to be answered at the organizational level — not by the security team in isolation.
The legal dimension covers data processing, how AI interacts with sensitive information throughout the company, and how its usage is documented for regulatory purposes. This isn’t just a security problem. Legal needs a seat at this table.
The technology dimension covers what technology you’re using, how you’re using it, and the integrity of the system. This is where the security and technical teams lead — validating the platform, the architecture, the integrations, and the guardrails.

None of these dimensions operate in isolation. The day-to-day governance can sit with the security and GRC teams. But the policy has to be organizational. It has to be holistic. Enforcing it comes down to the technical teams, but owning it requires the whole organization to be aligned.

And this isn’t a new role. It’s an existing role that is adapting. The people responsible for policy today need to develop new skills, understand the new technology, and update their frameworks accordingly. The answer isn’t to hire a Chief AI Governance Officer and call it done. The answer is to build the capability into the teams you already have.

When Security Gets It Right, the Whole Org Catches Up

Here’s something I’ve noticed consistently: once adjacent teams see the outcomes security is delivering with AI and automation, they want in.

GRC is the most natural next step. Identity and access management. IT operations. Any function that involves repeatable processes, assurance activity, or continuous monitoring stands to gain significantly. The model translates directly.

And that’s actually one of the most compelling arguments for security teams to lead the initiative on AI advancements.

When security builds a working model — an outcome layer, a judgment layer, an execution layer that actually delivers — it becomes a common language the wider organization can adopt.

Security becomes the team that figured it out first. Everyone else becomes a customer of that thinking.

And maybe the most exciting possibility? A real-time CISO-level SOC dashboard that reflects actual organizational risk posture as it stands right now, not as it stood at last quarter’s reporting cycle. CISOs being able to finally see everything has been the holy grail for years.

With AI doing the continuous monitoring, the continuous enrichment, the continuous assessment, we might finally be close to it.

The One Place Humans Will Always Sit

I want to be direct about this, because I think it gets obscured in the excitement around AI’s capabilities.

The most complex investigations will always require a human in the loop.

Not because AI can’t process the data. It can process more data, faster, than any human team. But the decision that comes out of that investigation isn’t solely a data decision — it’s a judgment call that requires knowing the business, the risk appetite, the stakeholders, and what’s politically viable right now. That judgment doesn’t sit in a knowledge base. It lives in the people who’ve built relationships across the organization, who’ve sat in the board meetings, who understand the strategy, the pressures, and the history.

AI can inform that judgment. It can surface the evidence, structure the analysis, and highlight the options. But the call? That’s human. That stays human.

The organizations that design their AI governance around this principle — AI at machine speed in the execution layer, human authority at the points where it genuinely matters — will be the ones that build something sustainable.

The organizations that sacrifice that line for a quick fix of speed or efficiency will find out exactly why it mattered in the first place — and not at a moment of their choosing.

And that moment will come.

Machine speed where it counts. Human authority where it matters. Get the AI or Die Manifesto and start building.

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Keep reading John’s CISO to CISO Blog Series on Redesigning SecOps for AI.

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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 Hyperautomation Security Automation 101

Agentic AI & Hyperautomation: Your SOC Guide for 2026

By Torq

March 12, 2026

19 Minute Read

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TL;DR

40% of security alerts go uninvestigated — legacy tools and SOAR simply can’t keep up.
Hyperautomation is what SOC teams need. It replaces static, engineer-heavy playbooks with AI-generated, no-code workflows that scale.
Agentic AI goes even further — it doesn’t just automate tasks, it reasons, plans, and acts autonomously.
The winning model is “human-on-the-loop”: AI handles alert volume, humans handle strategic judgment calls.
Start small — phishing triage is the ideal first use case to build trust before expanding AI autonomy.
The SOCs that thrive in 2026 will treat AI as the foundation — not just another feature in the stack.

What is Agentic AI in Security Operations?

Agentic AI in security operations refers to autonomous AI systems that reason through security threats, plan multi-step investigation workflows, and execute response actions — without requiring constant human direction for each step. Unlike AI tools that summarize or recommend, agentic AI acts. It ingests an alert, pulls context from across your security stack, correlates signals, reaches a verdict, and initiates containment — all within defined guardrails your team controls.

The distinction that matters most is between AI-assisted and AI-autonomous operations. AI-assisted tools advise analysts. AI-autonomous systems act on their behalf. A tool that surfaces a summary of a phishing alert and a system that triages, scores, remediates, and documents that alert are fundamentally different things — and only one of them closes the gap between attacker speed and defender capacity.

In the SOC context, agentic AI operates as a digital analyst that works 24/7 — processing alert volume that no human team can match, applying consistent judgment across every case, and escalating to human analysts only when the situation genuinely requires strategic authority. According to IDC, organizations using the Torq AI SOC Platform achieve 95% of Tier-1 cases auto-investigated, with MTTR dropping from hours to minutes.

What Skills Do SOC Analysts Need for Agentic AI?

SOC analysts working alongside agentic AI need a different skill set than analysts working in traditional environments. Technical triage skills matter less — the AI handles that. Strategic judgment, threat hunting, AI oversight, and the ability to direct AI agents using natural language matter more.

Specifically, analysts benefit from familiarity with MITRE ATT&CK and how attacker TTPs map to observable behaviors, experience interpreting AI-generated investigation summaries and audit logs, and the ability to configure escalation thresholds and governance guardrails. Workflow literacy — understanding how to build, modify, and quality-check automated response workflows — is increasingly essential. Platforms like the Torq are designed so analysts work in natural language rather than code, which lowers the bar significantly for teams without deep scripting expertise.

How Much Can Agentic AI Reduce Alert Fatigue?

Alert fatigue is one of the most measurable problems agentic AI solves. According to the SACR 2025 AI SOC Market Landscape report, 40% of security alerts go uninvestigated with legacy tooling. Agentic AI addresses this directly by handling the full Tier-1 investigation lifecycle autonomously — enriching alerts, suppressing false positives, and closing low-risk cases without analyst involvement.

The practical result: analysts stop spending their shifts on repetitive triage and start spending them on the threats that actually require human judgment. Valvoline’s SOC team saved 7 analyst hours per day after deploying Torq — time previously consumed by manual phishing review and alert queue management. RSM automated 82% of global MSSP customer cases. The ROI from reducing alert fatigue compounds quickly: lower burnout, better retention, and a team that can take on more without adding headcount.

Your AI SOC Guide Starts Here

According to the SACR 2025 AI SOC Market Landscape report, 40% of security alerts go uninvestigated. The average alert investigation takes 70 minutes. Meanwhile, attackers achieve breakout in under 48 hours. That math doesn’t work in anyone’s favor — except the adversary’s.

Today’s SOCs are fighting a losing battle with legacy tools. Alert volumes are exploding, skilled analysts are nearly impossible to hire and retain, and traditional automation can’t keep pace with AI-powered threats that evolve faster than any playbook can be written.

The answer isn’t more analysts or more tools. It’s a smarter approach to how security operations work altogether. Agentic AI powered by Hyperautomation represents a fundamental shift from automated (static playbooks that execute predefined steps) to autonomous (AI that reasons, plans, and acts). Organizations that embrace this shift will outpace threats. Those that don’t will fall further behind.

This guide covers the evolution of SOCs, how to implement agentic AI powered by Hyperautomation, the challenges you’ll face, and a practical checklist to overcome them.

The SOC Glow-Up: Manual to Autonomous

To understand where SOCs are headed, it helps to understand how they got here.

The traditional SOC was built on human expertise and manual investigation. Analysts triaged alerts by hand, pivoted between siloed tools, and followed static runbooks. It worked — until alert volumes outpaced human capacity. Alert fatigue set in. Analyst burnout followed. And threat actors got faster.

The first wave of automation (SOAR) promised relief. And to its credit, it helped teams automate repetitive, well-defined tasks. But SOAR had a fundamental flaw: it required heavy scripting, constant maintenance, and a dedicated engineering team just to keep workflows running. Worse, it couldn’t adapt to novel threats. Every new attack vector meant another playbook to write, test, and maintain. SOAR became a second job.

The shift to Hyperautomation changed the equation. Instead of static, hand-coded workflows, security Hyperautomation delivers seamless integration across the entire security stack, with AI-generated workflows, no-code orchestration, and automation that scales without engineering dependency. Security teams stopped spending cycles maintaining automation and started spending them on what actually matters.

The emergence of agentic AI took it a step further. Agentic AI doesn’t just execute playbooks — it reasons through problems, plans multi-step investigations, and takes autonomous action within defined guardrails. It can investigate an alert, gather context from across the stack, and respond autonomously, with humans on the loop only for critical judgment calls.

The distinction that matters most here is between AI-assisted and AI-autonomous operations. AI-assisted tools advise. AI-autonomous systems act. A chatbot that summarizes an alert and a system that triages, investigates, and remediates it are fundamentally different things — and only one of them closes the gap between attacker speed and defender capacity.

The results speak for themselves. According to IDC, organizations using Torq can automate more than 95% of Tier 1 analyst tasks, reducing MTTR from hours to minutes. The autonomous SOC isn’t a future-state aspiration. It’s happening now.

How Does Hyperautomation Differ from Legacy Security Automation?

Hyperautomation and legacy security automation both aim to reduce manual work in the SOC — but they take fundamentally different approaches, and the gap between them shows up immediately in production.

Legacy security automation executes predefined, static playbooks. An analyst or engineer writes a script: if X happens, do Y. That works well for known, repeatable scenarios. The moment attack patterns deviate from what the playbook expected, the automation breaks and an analyst has to step in. Maintaining those playbooks at scale requires a dedicated engineering team, and every new threat vector means a new playbook to build, test, and maintain.

Hyperautomation takes a different approach. Rather than static scripts, it delivers AI-generated workflows that adapt to new inputs, no-code orchestration that security engineers — not developers — can build and modify, and seamless integration across the entire security stack through API-first architecture. Hyperautomation connects your EDR, SIEM, identity, cloud, and ticketing tools into a single orchestration layer — so when an alert fires, the response draws on context from everywhere, not just the tool that triggered it.

Here’s how the two approaches compare across the dimensions that matter most in production.

Flexibility. Legacy automation requires manual playbook updates for each new threat type. Hyperautomation generates and adapts workflows using AI, handling novel scenarios without engineering intervention.

Maintenance. Legacy automation demands constant playbook upkeep and dedicated engineering resources. Hyperautomation is built to be managed by security professionals directly, with no proprietary scripting required.

Scale. Legacy automation scales by adding more playbooks and more engineers. Hyperautomation scales by expanding AI autonomy — the same team handles significantly more alert volume.

Integration. Legacy automation relies on proprietary connectors that lock teams into a rigid vendor stack. Hyperautomation uses API-first architecture with 300+ native integrations (https://torq.io/integrations/) and unlimited extensibility.

Speed. Legacy automation executes predefined steps at human-defined intervals. Hyperautomation operates at machine speed — detecting, correlating, and responding in seconds.

What are the Key Benefits of Agentic AI for SOC Teams?

SOC teams that deploy agentic AI powered by Hyperautomation see improvements across four dimensions: speed, scale, consistency, and analyst experience.

Speed. Automated incident response executes containment actions — isolating endpoints, disabling compromised accounts, blocking malicious IPs — in seconds. The average adversary breakout time from initial access to lateral movement is 62 minutes, according to CrowdStrike. Agentic AI closes that window. Human-speed response keeps it open.

Scale. AI agents process thousands of security events simultaneously, around the clock, without fatigue or shift limitations. A SOC running agentic AI handles alert volume that would require a significantly larger human team to match.

Consistency. Every alert receives the same quality of investigation, every time. Agentic AI applies the same enrichment logic, the same escalation criteria, and the same documentation standards regardless of which analyst is on shift, what time it is, or how high the alert queue is.

Analyst experience. When AI absorbs Tier-1 triage, analysts stop doing the work that drives burnout and start doing the work that drives career growth — complex investigations, threat hunting, strategic security improvements. According to the Torq 2026 AI SOC Leadership Report, when security leaders were asked about the number-one expected benefit of agentic AI, their top answer was quality of life — not faster detection, not better MTTR.

A Roadmap for Implementing Agentic AI Powered by Hyperautomation

Knowing the technology is one thing. Getting it into production is another. Here’s how to do it right.

1. Assess organizational readiness

Before deploying anything, audit your current environment. Map your existing tools, workflows, and integration points. Identify where the biggest bottlenecks are — the high-volume, repetitive use cases that consume the most analyst time without requiring deep human judgment. Common candidates: phishing triage, impossible travel alerts, cloud misconfiguration remediation, and user verification workflows.

2. Define objectives and success metrics

What does success actually look like for your team? Get specific. Define target metrics before you start: percentage of Tier 1 alerts auto-resolved, MTTR reduction, analyst hours saved per week, false positive rate. Tie those metrics to business outcomes, because security leadership needs to be able to explain the value to the board.

3. Select the right platform

Not all automation platforms are created equal. Avoid legacy SOAR solutions with AI bolted on as an afterthought — the architectural limitations will follow you. Look for platforms built AI-native from the ground up, with multi-agent systems, advanced case management, no-code and AI-generated workflow building, MCP support, and deep integrations across your stack.

The Torq AI SOC Platform was built for exactly this. With 300+ integrations, no-code workflow generation, and Torq Socrates — the AI SOC Analyst that operates as an agentic OmniAgent, coordinating a system of specialized AI gents — organizations can go from deployment to value in days, not months. Socrates handles deep research, planning, autonomous remediation, and natural language collaboration with analysts. It’s not a copilot. It acts.

4. Start with high-impact, low-risk use cases

Don’t try to automate everything at once. Pick one or two well-defined use cases where the stakes of an error are manageable. Phishing triage is a great starting point — high volume, well-understood, and easy to measure. Build trust with your team and your stakeholders before expanding AI autonomy.

5. Train personnel and establish governance

This step is non-negotiable. Define clear guardrails: what can AI act on autonomously, and what requires human approval? This is the “human-on-the-loop” model — where AI handles volume and humans supervise strategy, stepping in only when predefined thresholds require it. Upskill analysts to work alongside AI agents, collaborate in natural language, and escalate appropriately.

Read now: Where should AI operate autonomously in security — and where must human authority always sit? >

6. Iterate and expand

Use feedback loops to continuously refine workflows. As confidence grows, expand AI autonomy incrementally. The teams getting the most out of these platforms aren’t the ones who deployed everything at once — they’re the ones who iterated their way to full autonomy.

How Do You Implement Agentic AI in Your SOC?

Successful agentic AI implementation follows a six-step pattern. Teams that skip steps, especially governance and iteration, consistently run into the trust and adoption problems that slow deployment.

Assess organizational readiness. Audit your current environment before deploying anything. Map existing tools, workflows, and integration points. Identify the highest-volume, most repetitive use cases that consume analyst time without requiring deep human judgment. Strong starting candidates: phishing triage, impossible travel alerts, cloud misconfiguration remediation, and user verification workflows.
Define objectives and success metrics. Specificity matters here. Define target metrics before deployment: percentage of Tier-1 alerts auto-resolved, MTTR reduction, analyst hours saved per week, false positive rate. Tie those metrics to business outcomes so security leadership can communicate value to the board clearly.
Select the right platform. Prioritize platforms built AI-native from the ground up. Look for multi-agent architecture, advanced case management, workflow building, and deep integrations across your stack. The Torq AI SOC Platform includes Socrates, Torq’s agentic SOC orchestrator, which coordinates specialized AI agents across the full Tier-1 case lifecycle — from enrichment through containment — escalating to human analysts only when genuine judgment is required.
Start with high-impact, low-risk use cases. Deploy one or two well-defined use cases where the cost of an error is manageable. Phishing triage is the most common and highest-ROI starting point. Build team confidence and stakeholder trust before expanding AI autonomy.
Train personnel and establish governance. Define clear guardrails: what AI acts on autonomously, and what requires human approval. This is the human-on-the-loop model — AI handles volume, humans supervise strategy. Upskill analysts to direct AI agents, interpret AI-generated findings, and escalate appropriately.
Iterate and expand. Use feedback loops to continuously refine workflows. Expand AI autonomy incrementally as confidence grows. The teams achieving the highest automation rates are the ones that iterated their way there, not the ones that tried to deploy everything at once.

What Challenges Should You Expect When Deploying Agentic AI?

Four challenges show up consistently across agentic AI deployments. Each one is solvable.

Analyst skepticism: Analysts who have dealt with unreliable automation before bring healthy skepticism to agentic AI deployments. Address it directly by framing AI as the solution to the work analysts dislike most — the repetitive, high-volume triage that causes burnout — and showing early wins on a contained use case before expanding. Transparency matters enormously here. Analysts trust AI systems that show their work. Platforms with clear audit logs and explainable decision-making earn adoption faster than black-box systems.

Data privacy and governance: Security teams rightly scrutinize AI systems that access sensitive data and make autonomous decisions. Solve this by selecting platforms with strong compliance postures — SOC 2 Type II, HIPAA, GDPR — combined with configurable guardrails that keep AI actions within approved boundaries and full audit trails on every action taken.

Integration complexity: Legacy tools, fragmented data, and siloed systems are the biggest technical barriers to agentic AI adoption. Prioritize platforms with broad native integrations and API-first architecture. Every connector that requires a professional services engagement adds cost and delay that compounds across your stack.

Measuring ROI: Quantifying what did not happen is genuinely hard. Solve this by defining baseline metrics before deployment — alert volume, investigation time, MTTR, analyst hours on Tier-1 work — so post-deployment comparisons are meaningful. The 2026 AI SOC Leadership Report found that the number-one barrier to AI adoption is visibility into what the AI did and why. Teams that build explainability and reporting into their deployment from day one sustain executive support through the full rollout.

The Part Where Things Get Difficult (And What to Do About It)

Even the best-planned implementations hit friction. Here’s what to expect and how to push through it.

Resistance to change. Analysts who’ve been burned by unreliable automation before are right to be skeptical. Address it directly. Frame AI as augmentation, not replacement — something that eliminates the tedious, soul-crushing work and elevates analysts to the strategic, high-judgment roles they actually want to be doing. Socrates is designed for exactly this: it absorbs Tier 1 case load so analysts can focus on critical threats that genuinely require human expertise.

Data privacy and governance concerns. Security teams are rightfully cautious about AI accessing sensitive data or making unauthorized decisions. The answer is choosing platforms with a strong compliance posture — SOC 2 Type II, HIPAA, GDPR — combined with explainable AI that produces full audit trails and configurable guardrails that keep AI actions within approved boundaries. Every Socrates decision comes with a clear record of what it observed, what it concluded, and why it acted.

Integration complexity. Legacy tools, fragmented data, and siloed systems are the biggest technical barriers to adoption. Prioritize platforms with broad native integrations and API-first architecture. If every new connector requires a professional services engagement, that’s not scale — that’s just a new maintenance burden. The economics of a fragmented SOC compound quickly: tool sprawl, integration debt, and overlapping functionality drain budgets and engineering hours before a single alert is resolved.

Measuring ROI. It’s hard to quantify what didn’t happen. Define your baseline metrics before implementation so you have something to measure against. According to IDC, Torq customers achieve 95% of Tier-1 cases auto-investigated, and MSSPs using Torq onboard customers 18x faster. Valvoline reclaimed 6–7 analyst hours per day through automated phishing triage alone — time that’s now spent on higher-priority work.

Real-World Agentic AI Use Cases in Security Operations

The following use cases come directly from Torq customers who have deployed agentic AI and Hyperautomation in production environments. Each one is real — the problems, the workflows, and the outcomes.

Automated Phishing Response and Containment: Valvoline

When Corey Kaemming stepped into the Senior Director of InfoSec role at Valvoline, his team had been cut in half — down from 24 to 12 analysts — while alert volume stayed the same. Their legacy automation was brittle, heavily customized, and required specialist engineers just to keep running. Phishing triage alone consumed up to 12 analyst hours per day.

After deploying the Torq AI SOC Platform, Valvoline saw operational value within 48 hours. Torq automated phishing triage by continuously monitoring inboxes, correlating activity across Microsoft 365, Defender, and CrowdStrike, and escalating only when necessary. When a user clicks a malicious link, Torq automatically initiates password resets, terminates active sessions, and executes containment actions across integrated platforms — with everything tracked in case management. A Rapid7 integration their previous platform had failed to build after hundreds of hours was running in under a week.

Results: 6-7 analyst hours saved per day. Phishing triage went from a 12-hour daily burden to a largely automated workflow. The team expanded Torq’s use beyond security into adjacent operational teams.

“My team is in love with the product. Sometimes, I have to tell them to stop having so much fun and go do something else.”

— Corey Kaemming, Senior Director of InfoSec, Valvoline

Read the full case study: https://torq.io/resources/valvoline-soc-automation/

Autonomous SOC Operations and Incident Response for an MSSP: HWG Sababa

HWG Sababa, a global managed security provider serving enterprise clients across energy, utilities, finance, and healthcare, hit a growth ceiling with their in-house automation tool. Custom coding every workflow was too slow and resource-intensive to scale as they onboarded more clients and expanded their tool stack. With hundreds of customers and a wide range of playbooks to manage, they needed automation their team could build and iterate without heavy engineering overhead.

After deploying Torq, HWG Sababa shifted from months of custom coding to building years’ worth of automations in weeks. They built automated workflows across their multi-tenant client environments, connecting tools across their full stack and enabling investigation and response to happen nearly simultaneously for most case types.

Results: MTTI and MTTR improved by 95% for medium- and low-priority cases and by 85% for high-priority cases. Investigation and response now happen in under eight minutes for most incidents. The efficiency gains translated directly into a competitive advantage — HWG Sababa delivers faster, more consistent outcomes to clients without proportionally growing their analyst headcount.

Read the full case study: https://torq.io/resources/hwg-sababa-mssp-case-study/

Tier-1 and Tier-2 Automation at MDR Scale: Deepwatch

Deepwatch, a leading MDR provider protecting enterprise clients globally, needed to scale their managed detection and response operations without simply adding more analysts. They wanted to automate more deeply across both Tier-1 and Tier-2 tasks — not just the easiest, most repetitive work — while continuing to deliver fast, consistent outcomes to clients with demanding SLAs.

Deepwatch deployed Torq Hyperautomation to automate analysis, triage, and response workflows across their client environments. Torq’s low-code and no-code capabilities allowed the Deepwatch team to build and ship new automations and features at speeds previously impossible with their prior tooling. Torq also streamlined their customer onboarding process, enabling them to iteratively improve it over time.

Results: Deepwatch automates over 90% of Tier-1 and Tier-2 tasks, leading to faster case validation and shorter response times. Customer onboarding is faster than it has ever been.

“New customers are seeing faster onboardings than we’ve seen ever.”

— Micah Donald, Former Sr. Director, Deepwatch

Read the full case study: https://torq.io/resources/deepwatch-case-study/

10 Steps to Integrate Agentic AI and Hyperautomation AI into Your SOC

Conduct a readiness assessment of current tools, workflows, and integration gaps.
Identify your top 3–5 high-volume, repetitive use cases to automate first.
Define clear objectives and success metrics aligned to business outcomes.
Evaluate vendors based on AI-native architecture, integrations, and explainability.
Establish governance guardrails — what AI can do autonomously vs. with human approval.
Start with a pilot use case (phishing triage is a great first step) to build trust and demonstrate value.
Train analysts on AI supervision, natural language collaboration, and escalation workflows.
Deploy with full audit logging to ensure compliance and transparency.
Measure outcomes against baseline metrics and iterate based on feedback.
Expand AI autonomy incrementally as confidence and trust grow.

Will Your SOC Be One That Wins?

Agentic AI and Hyperautomation are already transforming how the best security teams operate. Organizations that adopt them now will scale their operations without scaling headcount, reduce MTTR from hours to minutes, and make the shift from reactive firefighting to proactive defense.

The SOCs that thrive in 2026 will be the ones that figured out how to let AI handle volume while humans handle strategy — shifting from human-in-the-loop to human-on-the-loop, and from AI as a feature to AI as the foundation.

Ready to see how to transform your SOC in 90 days?

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FAQs

What's the difference between Hyperautomation and traditional SOAR?

SOAR automates predefined, hand-coded workflows but requires constant engineering maintenance and can’t adapt to new threats. Hyperautomation uses AI-generated, no-code workflows that scale without engineering dependency and adapt dynamically.

How does agentic AI work in a SOC?

It operates as a collaborative system of specialized agents, each handling a distinct part of the threat response lifecycle. Torq’s Socrates acts as an agentic OmniAgent, coordinating a network of specialized agents torq that cover investigation, planning, remediation, and case management — working together to handle threats from detection through resolution.

Does agentic AI replace human analysts?

No. It handles high-volume, repetitive Tier 1 work autonomously while escalating critical cases that require human judgment. Analysts can also collaborate with the system directly using natural language, staying in control of decisions that matter most.

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 Hyperautomation Security Automation 101

SOAR vs. AI SOC: The Category That Left SOAR Behind

By Bob Boyle

March 10, 2026

15 Minute Read

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TL;DR

SOAR was security automation’s first draft. But static playbooks, custom scripting, and 12–18 month implementations couldn’t keep pace with a threat landscape that moves at machine speed.
The numbers tell the story. Most SOAR deployments cover 30–40% of alerts. 40% of alerts are never investigated. And the engineering hours required to keep playbooks running keep climbing every quarter.
AI SOC changes everything. Agentic AI investigates every alert — including scenarios for which no playbook exists. It reasons through problems, adapts to context, and executes a response within guardrails.
Migration isn’t starting over. Your existing workflows run on Torq’s Hyperautomation layer at 10x the speed. The AI SOC layer adds what SOAR never could: autonomous investigation, adaptive triage, full case management, and real remediation actions. RSM migrated 200+ customers in three weeks. Valvoline was live in 48 hours.

If you’ve been in security operations for more than a few years, you’ve lived through the automation hype cycle at least twice. First, it was SIEM that was going to solve everything. Then SOAR was supposed to fix what SIEM couldn’t. Now, AI SOC platforms are delivering what SOAR always promised but never actually could.

Each wave solved real problems. But SOAR’s issues have become bigger than its solutions. Static playbooks that break when APIs change. Custom scripting that only two people on the team understand. Implementations that take 12–18 months before showing ROI. A coverage ceiling that tops out at 30–40% of your alert volume, no matter how many engineering hours you throw at it.

GigaOm recognized this shift when it renamed its SOAR Radar to the SecOps Automation Radar in 2025 — because the category itself has evolved past SOAR. Torq has been named a leader and outperformer in that report for three consecutive years, specifically for Hyperautomation capabilities that legacy SOAR can’t touch.

This piece breaks down what SOAR and AI SOC actually are, where SOAR falls short, and why AI-native Hyperautomation is the clear path forward.

What is SOAR?

SOAR (Security Orchestration, Automation, and Response) emerged around 2015 to solve a specific problem: SOC analysts were drowning in manual, repetitive tasks across disconnected tools. SOAR platforms promised to connect those tools and automate the workflows between them.

At its core, SOAR does three things. It orchestrates actions across your security stack (e.g., fire an API call to your EDR, update a ServiceNow ticket, send an email notification). It automates predefined response playbooks (e.g., if a phishing alert, extract IOCs, check reputation, quarantine the email). And it collects and organizes investigation data.

That model worked when the threat landscape moved slowly enough for playbooks to keep up. It doesn’t anymore.

Every playbook has to be built, tested, and maintained by someone — usually a security engineer with scripting skills your team can’t afford to lose. When vendor APIs change, playbooks break. When a new threat type emerges that doesn’t match an existing workflow, the alert sits in the queue until a human gets to it. SOAR platforms are code-heavy, rigid, and expensive to scale, so most organizations end up automating only a fraction of their workflows and manually handling the rest.

As highlighted in GigaOm’s SecOps Automation Radar, legacy SOAR’s inherent complexity, management overhead, and high costs have made it increasingly unsustainable. The SANS 2024 SOC Survey found that automation itself had become the top barrier to effective SOC operations — ranked higher than staffing — reflecting just how badly the SOAR generation of tools has failed to deliver on its promise.

What is an AI SOC?

A true AI SOC model isn’t just bolted-on “AI in the SOC.” It’s an operating model — a fundamentally different way of structuring how your SOC detects, investigates, and responds to threats.

An AI SOC must include:

Complete threat lifecycle coverage. The Security Operations Center is responsible for every action surrounding a threat to the organization — the work doesn’t end when a threat is detected, and the “this is real!” verdict is made. An AI SOC must accelerate not only mean-time-to-detection or investigation, but also mean-time-to-response.
Agentic operations: AI that plans, reasons, and executes end-to-end security tasks like determining real threats from false positives, extracting key attack details across disparate systems, or coordinating case management autonomously. And in successful deployments, AI can fully remediate alerts on its own.
Automation modernization: Teams replace playbook-heavy systems with platforms designed for AI-speed workflow creation, better reuse, and stronger governance.
More consistent execution: The SOC shifts from “people clicking buttons” to “processes that run consistently,” with humans approving sensitive actions.

Three principles define it:

Agents drive execution. In a legacy SOC, execution depends on whoever is on shift and what they remember to do. In an AI SOC, every alert passes an AI Agent — not a static playbook that breaks when the threat deviates, but an adaptive process in which agentic AI reasons through the situation, selects the right tools to query, gathers evidence, and executes response actions within guardrails. The agent documents an immutable system of record for what happened and why each decision was made. Analysts don’t drive execution manually; they supervise it, intervene on escalations, and refine the logic over time.
Cases centralize accountability. In most SOCs today, accountability is scattered across ticketing systems, Slack threads, email chains, and analyst memory. Nobody can see the full picture of a given incident without manually assembling it from five different tools. In an AI SOC, the case is the single source of truth — automatically created from correlated alerts, enriched with evidence from across the stack, prioritized by business impact, and tracked from detection through resolution. Every automated action, every AI decision, every human intervention is logged in one place. When leadership asks “what happened and how did we respond?” the answer lives in the case, not in someone’s head.
Governance keeps automation from becoming a liability. Automation without governance accelerates risk. An AI SOC builds governance into the operating model itself: approval gates for high-impact actions, immutable audit trails for every decision, scope boundaries that limit what agents can touch, and regular validation cycles where the team reviews AI-closed cases to ensure accuracy. This isn’t a compliance checkbox bolted on after deployment. It’s the architecture that makes autonomy safe enough to trust at scale, and explainable enough to defend to auditors, insurers, and the board.

The shift from SOAR to AI SOC isn’t a tool swap. It’s a fundamental move from “we have some automation” to “AI-driven automation is how we operate” — with the structure, accountability, and controls to make that sustainable.

How Long Does AI SOC Implementation Take Compared to SOAR?

AI SOC platforms go live in days to weeks. Legacy SOAR implementations take 12 to 18 months to show meaningful ROI. That’s the gap and it’s the single largest time-to-value delta in enterprise security tooling today.

SOAR deployments stall: custom playbook development for each use case, brittle integration work per tool, scripting that only specialized engineers can maintain, and long QA cycles because a broken playbook breaks production response. AI SOC platforms remove those dependencies. Agentic AI investigates without predefined playbooks, native integrations ship with the platform, and any analyst can build and modify workflows through natural language or a no-code builder.

Timelines from Torq customers:

Valvoline was live on top-priority use cases within a week, and saving six to seven analyst hours per day from day one. ROI landed inside 48 hours.
RSM migrated 200+ managed customers off legacy SOAR in three weeks.
Deepwatch recreated years of legacy SOAR automations in weeks after standardizing on Torq.
Lennar Corp. replaced XSOAR and cut phishing response from hours to minutes.

The average enterprise SOAR implementation takes longer than the average enterprise AI SOC deployment delivers measurable ROI. For security leaders building a business case, the implication is direct: every month spent rebuilding or maintaining SOAR playbooks is a month of risk and capacity the organization doesn’t recover.

SOAR vs AI SOC: Key Differences

Capability	Legacy SOAR	The Torq AI SOC Platform
How it works	Executes predefined playbooks built by engineers	Agentic AI reasons through alerts dynamically
Playbook dependency	Every scenario needs a playbook; no playbook = no automation	Investigates and responds without predefined workflows
Maintenance burden	High: Playbooks break when APIs change, or new threats emerge	Low: AI adapts to new patterns and learns from feedback
Alert coverage	Covers only the scenarios you’ve built playbooks for (typically 30–40%)	Investigates every alert, including novel and unknown threat types
Investigation depth	Enrichment and triage based on static logic	Contextual reasoning across the full stack, like an experienced analyst
Integration model	Custom scripting per tool; brittle at scale	300+ native integrations, 4,000+ actions, AI-generated connectors
Time-to-value	12–18 months for meaningful ROI (typical)	Days to weeks (Valvoline achieved ROI within 48 hours)
Human-in-the-loop	Binary: Fully automated or fully manual per playbook	Configurable guardrails: Autonomy calibrated by action type and risk
Scalability	Degrades under volume spikes; serial execution queues	Elastic, cloud-native; processes millions of events without bottlenecks
Skill requirement	Requires dedicated security engineers for playbook development	No-code builder + natural language interface accessible to any analyst

This isn’t a matter of preference or maturity level. Legacy SOAR solutions fall short across every dimension that matters to a modern SOC: coverage, speed, maintenance costs, scalability, and accessibility. The only column where SOAR holds up is deterministic playbook execution for known scenarios… and Hyperautomation does that too, 10x faster.

Which Threats Does AI SOC Handle Better Than SOAR?

AI SOC platforms handle every threat category better than SOAR — because the limiting factor in SOAR isn’t the threat type, it’s the playbook. If a playbook exists and holds up, SOAR can execute it. Everything else sits in the queue.

The gap is widest in five categories:

Novel and zero-day attacks. By definition, no playbook exists when a threat is new. SOAR drops those alerts into the manual queue. Agentic AI reasons through unfamiliar patterns — correlating signals, querying enrichment sources, building a hypothesis — without a pre-built workflow.
Malware. Static playbooks pattern-match on known IOCs and behaviors. Polymorphic malware mutates specifically to break that matching. AI SOC evaluates behavior and context rather than signature, flagging threats that would slip past a rule-based system.
Multi-stage and multi-vector attacks. Modern campaigns span email, identity, endpoint, and cloud — sometimes over weeks. SOAR playbooks typically run per-alert and per-tool, lacking a cohesive thread. An AI SOC correlates across the full attack surface and builds a single case that shows the whole campaign.
Identity-based attacks. Account compromise, session hijacking, and OAuth abuse require contextual reasoning about user behavior, device posture, and access patterns. SOAR playbooks handle discrete signals well; they struggle with the “is this user actually doing this?” judgment call. Agentic AI assembles the context automatically.
Cloud misconfigurations and supply chain threats. These require reasoning across tools that didn’t exist when most SOAR platforms were built. Native AI SOC integrations cover AWS, Azure, GCP, CrowdStrike, Microsoft Defender, and 300+ more out of the box.

The common thread: anywhere a human analyst would say “I’d need to look at this in context,” SOAR can’t help. An AI SOC can.

The Case Against Keeping SOAR

The most common argument for staying on SOAR is sunk cost: “We’ve already invested in playbooks, and they work for what they cover.”

Consider what that actually means. Your team has spent years building automation that covers a third of your alerts. The other two-thirds sit in the queue or remain uninvestigated. SACR’s 2025 AI SOC Market Landscape research, based on a survey of 300+ CISOs, found that 40% of alerts are never investigated — and of those that are, 90% turn out to be false positives. That’s the reality of your SOAR investment.

Meanwhile, the engineering hours required to keep those playbooks functional keep climbing. Every vendor API update is a maintenance cycle. Every new tool in the stack needs custom integration work. Every novel threat type requires a new playbook that takes weeks to build and test. You’re running on a treadmill that speeds up every quarter.

And the talent math makes it worse. The engineers who built your SOAR playbooks are the same engineers every company in your industry is trying to hire. When one leaves, they take the tribal knowledge encoded in your automation with them. Legacy SOAR’s reliance on custom scripting and constant maintenance creates a dependency on scarce, expensive talent that most organizations can’t sustain.

SOAR’s deterministic model made sense when attack patterns were slower and more predictable. That era is over. Attackers use AI. They move at machine speed. They don’t wait for your team to write a new playbook.

Why AI SOC Is the Clear Path Forward

For organizations evaluating automation in 2026, AI SOC solves the problems SOAR created and the problems SOAR was never designed to address.

Coverage, not just speed. SOAR makes workflows faster. AI SOC investigates everything — 100% of alerts that hit your queue, not just the 30–40% with matching playbooks. That’s the difference between automating tasks and automating outcomes.

Adaptability over rigidity. Novel attack techniques, evolving TTPs, and multi-stage campaigns don’t wait for someone to write a playbook. Agentic AI investigates unfamiliar scenarios by reasoning through them — correlating signals, enriching context, making policy-aware decisions — not by pattern-matching against a static ruleset.

Accessible to your whole team, not just your engineers. Torq’s agentic workflow builder and natural language interface mean any analyst can build, modify, and trigger automations. You stop being dependent on two senior engineers who understand the Python scripts holding your playbooks together.

Time-to-value is measured in days. Valvoline was live on top-priority use cases within a week. A stalled Rapid7 integration that had been blocked for months under their legacy SOAR was delivered in days. They were saving 6 to 7 hours of analyst time every day from the start. Legacy SOAR implementations typically take 12–18 months to show meaningful ROI. That gap is 12–18 months of risk.

Scale without degradation. Legacy SOAR platforms queue work serially during volume spikes. When alert volume surges — exactly when you need your automation most — response times slip, pipelines back up, and containment gets delayed. Torq’s cloud-native architecture processes millions of daily security automations without bottlenecks because it was built for elastic scale from the start.

What Happens to Existing SOAR Playbooks During Migration?

This is the question that keeps teams on legacy SOAR longer than they should be. It’s also the question Torq was designed to answer. Migrating to Torq Hyperautomation doesn’t mean burning down what you’ve built. It means running it better — and adding capabilities your SOAR platform could never deliver.

Your proven workflows run on Torq’s Hyperautomation layer, executing 10x faster than they did on legacy SOAR. Your integrations stay intact through 300+ native connectors. And on top of that orchestration layer, Torq’s multi-agent system handles the agentic investigation, autonomous triage, and adaptive response that your playbooks never covered.

Deepwatch standardized its entire global security infrastructure on Torq after leaving legacy SOAR, recreating years’ worth of automations in weeks. RSM migrated 200+ managed customers in three weeks. Lennar Corp. replaced XSOAR and cut phishing response from hours to minutes. None of them started from scratch. All of them got more from Torq in weeks than they got from SOAR in years.

The migration path is straightforward. Torq’s team helps you audit your current SOAR workflows, integrations, and pain points — prioritize key use cases, and define measurable success metrics before you start. The JumpStart implementation program gets priority use cases live fast, and Torq Academy, plus 24/7 access to the Knowledge Base, ensures long-term adoption.

Staying on legacy SOAR to protect an existing investment is like keeping a pager because you already paid for the service plan. The cost of staying is higher than the cost of switching.

When Should You Move From SOAR to AI SOC?

Be honest about where your SOC is today. These five questions will tell you whether your SOAR investment is still working — or whether it’s holding you back.

1. What percentage of your alerts are actually investigated? If the answer is under 80%, you have a coverage gap that playbooks can’t close. AI SOC investigates everything. SOAR only covers what someone built a workflow for.

2. How many full-time engineers maintain your automation? If you need dedicated security engineers just to keep playbooks running, your automation has become a cost center and your talent is being underutilized. Modern platforms reduce engineering dependency; they don’t require it.

3. How long does it take to operationalize a new use case? If the answer is weeks or months, your automation can’t keep pace with your threat landscape. Torq customers operationalize new workflows in minutes using natural language or the no-code builder.

4. What happens when an alert doesn’t match an existing playbook? If it sits in the queue, your automation gap grows every time a new attack technique emerges. Agentic AI investigates novel scenarios without waiting for someone to write the logic.

5. How does your platform perform during alert volume spikes? If response time degrades when you need it most, your architecture has a structural problem that more playbooks won’t fix.

If you answered honestly and two or more of these points to problems, your SOAR isn’t serving you anymore. It’s time to evaluate what replaces it.

SOAR Promised Automation. AI SOC Delivers It.

SOAR was an important step. It proved that security operations could benefit from automation and orchestration. But it also proved that static playbooks, custom scripting, and code-heavy platforms can’t keep pace with a threat landscape that moves at machine speed.

AI SOC — powered by agentic AI and Hyperautomation — delivers what SOAR always promised: every alert investigated, every response executed fast, every action auditable, and your analysts focused on work that actually requires human judgment. Not 30% of alerts. All of them.

The organizations that have already made the switch aren’t looking back. Carvana. Valvoline. Deepwatch. RSM. Kenvue. They didn’t settle for incremental improvements to a broken model. They replaced it.

Your SOAR had its run. See what comes next.

Kill Your SOAR

FAQs

What is the difference between SOAR and AI SOC?

SOAR automates predefined workflows through static playbooks that require engineering resources to build and maintain. AI SOC platforms use agentic AI to investigate, reason through, and respond to alerts autonomously — including threat scenarios no playbook exists. SOAR handles a subset of known, repeatable processes. AI SOC handles the full spectrum at machine speed.

Is AI SOC a replacement for SOAR?

Yes. AI-native Hyperautomation platforms like Torq do everything SOAR does — orchestration, automation, case management — but faster, with less maintenance, and without the playbook ceiling that limits SOAR’s coverage. Torq also adds agentic AI investigation and autonomous response that SOAR architectures can’t deliver. GigaOm has named Torq a leader and outperformer for three consecutive years for exactly this reason.

What is the best SOAR alternative?

Torq is the leading SOAR alternative. It combines the orchestration capabilities of SOAR with agentic AI that reasons, adapts, and responds without rigid playbooks — executing workflows 10x faster than legacy SOAR with 300+ native integrations and a no-code builder accessible to any analyst. Customers such as Valvoline, Carvana, Deepwatch, and RSM have migrated from legacy SOAR solutions and achieved measurable results within days.

How long does it take to migrate from SOAR to an AI SOC platform?

With Torq, migration happens in days or weeks. RSM migrated 200+ managed customers in three weeks. Valvoline replaced its legacy SOAR and was live on priority use cases within one week, achieving ROI in 48 hours. Compare that to the 12–18 months of legacy SOAR that typically require before delivering meaningful value.

What happens to my existing playbooks if I switch from SOAR?

They don’t disappear. Torq’s orchestration layer runs existing workflows 10x faster than legacy SOAR, while the AI SOC layer adds agentic investigation, autonomous triage, and adaptive response on top. Organizations like Deepwatch recreated years’ worth of legacy automations in weeks on Torq — and immediately started building capabilities their SOAR could never deliver.

How does AI SOC total cost of ownership compare to SOAR?

AI SOC TCO is lower than SOAR TCO in most enterprise deployments, primarily because SOAR’s hidden costs dwarf its license fees. SOAR requires dedicated security engineering headcount (typically $400K to $600K+ per year in loaded cost), ongoing integration and maintenance work (40+ hours per week at scale), and leaves 40 to 60% of alerts uninvestigated — latent risk with measurable breach-exposure cost. AI SOC platforms run with minimal engineering dependency, 300+ native integrations, and 100% alert coverage.

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

Security Automation 101 Use Cases

How IT Automation Tools Transform Security Operations

By Torq

March 6, 2026

9 Minute Read

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See how Torq harnesses AI in your SOC to investigate, prioritize, and respond to threats faster.

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TL;DR

IT automation isn’t about replacing your team — it’s about stopping them from spending their best hours on work that never required human judgment in the first place. Provisioning, access requests, onboarding checklists: these should run themselves.
The difference between basic task automation and true IT workflow automation is platform depth. Connecting dozens of systems, enforcing security guardrails, and handling real-world complexity — conditional logic, exception handling, human-in-the-loop approvals — requires more than a point solution.
Torq gives IT operations teams enterprise-grade automation infrastructure that powers the world’s most sophisticated security teams — with the integrations, AI-driven decision-making, and governance controls to match.

IT teams aren’t overwhelmed because the work is hard. They’re overwhelmed because the work is endless. Provisioning requests. Access queues. Onboarding checklists duct-taped across a dozen disconnected systems. None of it requires a skilled engineer — it just requires one to be available. And available, at enterprise scale, means buried. That’s not an IT problem. That’s an automation problem.

IT automation changes that equation. When done right, it doesn’t just speed up existing processes — it fundamentally transforms how IT operations run, what your team focuses on, and how securely and efficiently your organization scales.

This is what modern IT process automation looks like, why it matters, and how solutions like Hyperautomation are enabling enterprises to get there faster.

What Are IT Automation Tools?

IT automation tools are software platforms that execute IT processes and workflows with minimal or no human intervention. Instead of a technician manually stepping through a ticket, an automated workflow handles the trigger, the logic, the cross-system actions, and the outcome — consistently, at scale, and at machine speed.

This spans a wide range of IT processes: access provisioning, employee lifecycle management, service desk requests, compliance documentation, software deployment, system configuration, and more. The common thread is that these are high-volume, rule-based processes where manual execution creates bottlenecks, inconsistencies, and risk.

IT automation can be narrow (automating a single repetitive task) or expansive (orchestrating complex, cross-functional workflows across your entire technology stack). The difference between those two ends of the spectrum is the platform you build on.

What IT Automation Tools Are Not

IT automation tools are not meant to replace IT professionals. They’re about redirecting them. When your team isn’t spending half their day provisioning accounts, chasing approval chains, or resetting passwords, they have the bandwidth to tackle the work that actually requires their expertise.

It’s also not a “set it and forget it” proposition — at least not at the enterprise level. Effective IT workflow automation requires thoughtful design, strong governance, and a platform that can handle real-world complexity: conditional logic, exception handling, human-in-the-loop checkpoints, and cross-system integrations that actually hold up in production.

What Are the Benefits of IT Automation Tools?

Efficiency and Time Savings

The most immediate impact of IT automation tools is time — specifically, time reclaimed from repetitive, low-value tasks. Consider what a typical IT team handles on any given day: access requests, onboarding and offboarding workflows, software installations, password resets, compliance checks. These tasks are necessary. They are not, however, a good use of skilled engineers.

Automated IT software executes these workflows in a fraction of the time, without the delays introduced by manual handoffs, approval queues, or business-hour dependencies. Access provisioning that once took three to five days can be completed in minutes. Help desk tickets that piled up in queues get resolved — or never generated in the first place — through self-service automation.

Improved Security Posture

Manual processes are inherently inconsistent. When a human executes a workflow, there’s variance: steps get skipped, exceptions get made informally, and documentation lags. Automation enforces consistency. Every workflow runs the same way, every time, with a full audit trail.

This matters especially for access management. Departing employees who retain system access after their last day represent a real, well-documented security risk. Automated offboarding eliminates that window entirely. Just-in-time (JIT) access workflows ensure that elevated permissions are granted only when needed and revoked automatically when the need expires — reducing your standing attack surface without creating operational friction.

Scalability and Integration

IT operations teams don’t scale linearly with headcount. As organizations grow, there are more employees, more systems, and more complexity — the volume of IT work grows faster than any team can manually absorb. Automation is the only way to scale IT operations without increasing costs in proportion.

The right IT automation platform doesn’t operate in isolation. It connects across your full technology stack: HR systems, identity providers, cloud platforms, SaaS applications, communication tools, and ticketing systems. That integration depth is what separates a narrow automation tool from a true IT automation solution — and it’s what enables the kind of cross-functional, multi-step workflows that drive real operational transformation.

How Do You Build a Roadmap for IT Automation?

Enterprises rarely achieve full IT automation in a single initiative. The organizations that get there do so in stages — building confidence, expanding scope, and deepening integration as they go. Here are some stages of IT automation success.

Phase 1: Quick Wins

Start with high-volume, low-complexity processes where the ROI is immediate, and the risk of getting it wrong is low. Password resets. Software access requests. Basic onboarding task lists. These are workflows your team executes dozens of times per week, where automation delivers instant time savings and a clear proof of value.

This phase is also about building the organizational muscle for automation: getting stakeholders aligned, establishing governance practices, and proving the concept internally before expanding scope.

Phase 2: Intermediate Automation

Once your team has initial wins under their belt, move into more complex, multi-step workflows that span multiple systems. Employee onboarding and offboarding is a prime example — it touches HR platforms, identity providers, communication tools, cloud applications, and more. Automating it end-to-end requires integration depth and workflow logic, but the payoff is significant: faster time-to-productivity for new hires, fewer access errors, and dramatically reduced IT overhead.

This phase also introduces more sophisticated patterns: conditional branching, approval routing, exception handling, and human-in-the-loop checkpoints for decisions that still warrant human judgment.

Phase 3: Full Orchestration

At the enterprise level, IT automation becomes Hyperautomation — the orchestration of complex, cross-functional workflows across security, IT, DevOps, and HR. This isn’t just automating what humans do today. It’s enabling systems to analyze context, make risk-based decisions, and act autonomously on complex data — so humans can intervene precisely when and where they add the most value.

This phase requires a platform built for enterprise-scale complexity: deep integration capabilities, strong security guardrails, agentic AI that can reason through multi-step decisions, and governance controls that keep automated processes auditable and compliant.

Common IT Automation Use Cases

Employee Onboarding and Offboarding

Manual identity lifecycle management is one of the most consequential inefficiencies in enterprise IT. Fragmented systems, manual coordination, and inconsistent processes — these create security vulnerabilities, compliance gaps, and a bad experience for the employees on both ends of the workflow.

Automated onboarding and offboarding orchestrates the full identity lifecycle: provisioning accounts across every relevant system, enforcing role-based access policies, generating compliance documentation, and — critically — executing offboarding the moment an employee departs, with no delay and no manual steps that could be missed.

Just-in-Time Access

Standing privileges are a persistent security liability. Users accumulate elevated permissions over time — permissions that remain active long after the operational need expires. JIT access automation flips this model: permissions are granted on demand, scoped to what’s actually needed, and automatically revoked when the window closes.

This reduces your attack surface without slowing down operations. Employees get access when they need it, through familiar self-service channels, without waiting for a manual approval chain.

Self-Service Employee Chatbots

Most IT help desk tickets are routine. Access requests, software installations, password resets, and account unlocks — these don’t require a skilled engineer. They require a reliable process. Self-service employee chatbots and automation deliver that process through channels employees already use: Slack, Microsoft Teams, and web forms.

The result is a dramatically lower ticket volume for IT teams and a dramatically better experience for employees who get their requests resolved in minutes instead of days.

How Do You Choose the Right IT Automation Tools?

Not all IT automation platforms are built the same. Evaluating them requires clarity about what you actually need — today, and as your operations scale.

Evaluating Maturity and Needs

Start with an honest assessment of your team’s current state. What processes are consuming the most time? Where are the most common points of failure or inconsistency? What does your integration landscape look like, and how complex are the workflows you want to automate?

Teams early in their automation journey often benefit from starting with a platform that offers both low-code accessibility and the depth to grow with them — so they’re not rearchitecting their automation stack eighteen months in. The right IT automation solution meets you where you are and scales to where you need to go.

Governance and Security Considerations

Automation amplifies whatever governance practices you have in place. If access controls and credential management are weak, automating workflows on top of that foundation makes the problem worse.

The platform you choose needs to take security seriously — not as a feature, but as a foundation. That means strong role-based access controls for the automation platform itself, encrypted credential management, comprehensive audit logging, and human-in-the-loop checkpoints for high-stakes actions. An automated workflow that grants privileged access to sensitive systems cannot be built on a flimsy foundation.

Why Torq Is the IT Automation Platform Enterprises Choose

The Torq AI SOC platform, powered by Hyperautomation™, supports enterprises that need IT automation to operate at the same level of rigor, scale, and security as their most critical business systems.

The platform connects SecOps, IT, DevOps, and HR through 300+ integrations and 4,000+ out-of-the-box actions — eliminating the visibility gaps and manual handoffs that come from siloed operations. It supports the full range of IT automation patterns: simple task automation, complex multi-step workflows, AI-driven decision-making, and human-in-the-loop approvals. And it does all of this without compromising on the security guardrails that enterprise operations demand.

For IT teams, this means automated employee onboarding and offboarding that reduces identity management costs by 60% and cuts access errors by 99%. It means just-in-time access workflows that eliminate standing privileges and provision access 70% faster. And it means self-service chatbots that reduce help desk ticket volume by up to 70% while giving employees a better experience.

IT automation isn’t a future capability. It’s a present-day competitive advantage — and the gap between organizations that have it and those that don’t is widening fast.

See how Agoda automated phishing response, password resets, and cloud security workflows with Torq.

Read the Case Study

FAQs

What are IT automation tools?

IT automation tools are software platforms that execute IT processes and workflows with minimal or no human intervention. This includes access provisioning, employee onboarding and offboarding, service desk requests, and compliance documentation — high-volume, rule-based processes where manual execution creates bottlenecks, inconsistencies, and security risk.

What is an example of IT automation?

A common example is automated employee onboarding. When a new hire is added to an HR system, an automated workflow provisions their accounts across every relevant platform — email, Slack, cloud applications, identity providers — assigns role-based access, and generates compliance documentation, all without a single manual step from IT.

Why are IT automation tools important?

IT teams are consistently asked to do more with the same or fewer resources. IT automation tools are the only way to scale operations without increasing headcount in proportion. Beyond efficiency, they improve security by enforcing consistent processes, reducing human error, and freeing skilled engineers to focus on work that actually requires their expertise.

What IT processes are best suited for automation?

The best candidates are high-volume, repetitive, rule-based processes — ones that follow a predictable path and don’t require nuanced human judgment on every instance. Employee onboarding and offboarding, access provisioning, just-in-time access requests, password resets, and help desk ticket routing are all strong starting points.

How do IT automation tools improve security?

IT automation tools enforce consistent execution of security-sensitive workflows, eliminating the variability that comes with manual processes. Automated offboarding ensures departing employees lose access immediately with no gaps. Just-in-time access provisioning eliminates standing privileges. Comprehensive audit logging provides the documentation that compliance and security teams require.

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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Security Automation 101

Phishing Monitoring for SOC Teams: Detect & Prevent Attacks

By Torq

March 4, 2026

10 Minute Read

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TL;DR

Phishing monitoring gives SOC teams continuous visibility into malicious emails, spoofed domains, and brand impersonations before they reach users.
Effective phishing monitoring combines email analysis, URL scanning, domain name monitoring, and threat intelligence into a single, unified strategy.
Credential theft, business email compromise, and spear phishing are among the most costly phishing variants SOC teams face today.
Torq’s AI SOC Platform automates phishing detection, alert triage, and remediation end-to-end, cutting manual workload and accelerating response.
Proactive phishing defense powered by agentic AI and Hyperautomation is the most scalable path forward for enterprise SOC teams.

Phishing remains one of the most persistent entry points for attackers targeting enterprise organizations. Despite years of user education and improved email filtering, attackers keep finding ways to mimic trusted brands, spoof domains, and engineer convincing lures. For SOC teams, speed is everything: the faster you detect, triage, and shut down a phishing attempt, the less damage it does.

That’s where phishing monitoring comes in. If it’s done well, it shifts your team from chasing incidents after the fact to catching threats before users ever click a malicious link.

What is Phishing Monitoring and Why Does It Matter?

Phishing monitoring is the continuous process of detecting and responding to phishing threats by analyzing emails, URLs, domains, and digital assets for signs of impersonation or malicious activity. While general phishing detection focuses on blocking known bad content at the inbox level, phishing monitoring spans the full attack lifecycle: from the moment a threat actor registers a lookalike domain to the point where a credential theft is attempted.

For SOC teams, that broader coverage is what makes the difference. Monitoring creates the visibility that makes identification possible in the first place. It covers the infrastructure attackers build before a campaign launches, the signals embedded in suspicious URLs, and the brand monitoring cybersecurity layer that tracks unauthorized use of your company’s identity across the web. Phishing takedown service workflows become far more effective when triggered by real-time monitoring rather than reactive discovery.

According to the 2026 Verizon Data Breach Investigations Report, phishing is involved in the majority of social engineering incidents, and attackers are finding even more success through voice and text than through email. The financial and reputational stakes make proactive monitoring a core SOC responsibility.

How Phishing Monitoring Works

At its core, phishing monitoring relies on four interconnected mechanisms:

Email analysis scans inbound and outbound mail for indicators like spoofed sender domains, suspicious attachments, mismatched reply-to addresses, and social engineering language patterns.
URL scanning checks links embedded in emails and web content against threat intelligence feeds, sandboxing suspicious destinations in real time before users can reach them.
Domain name monitoring tracks newly registered domains that closely resemble your organization’s legitimate domains, a common technique used by attackers to set up convincing fake login portals. A domain monitoring service flags these registrations early, often before a phishing campaign launches.
Real-time alerting ties these signals together, routing high-confidence detections directly into analyst workflows and triggering automated responses for well-understood threat patterns.

A practical example: your company is “acmecorp.com.” A threat actor registers “acme-corp-secure.com” and begins hosting a credential harvesting page. A domain monitoring service catches the registration within hours, an automated scan confirms the phishing infrastructure, and a takedown request is initiated well before a single employee sees a phishing email.

Key Components of an Effective Phishing Monitoring Strategy

A mature phishing monitoring program layers reactive and proactive defenses. Reactive controls stop threats in motion; proactive controls dismantle attacker infrastructure before it gets used. The most resilient programs combine both.

Domain and Brand Monitoring

Attackers routinely leverage brand recognition to earn trust. They register lookalike domains, create fake login portals, and impersonate executives in email threads. A domain monitoring service tracks newly registered domains for typographical similarities to your brand, including character substitutions, added hyphens, and TLD swaps, and flags them for review.

Brand monitoring in cybersecurity extends this coverage to social media, mobile app stores, and web content, catching unauthorized use of logos, trademarks, or executive identities. When suspicious registrations or brand abuse are confirmed, automation can initiate phishing takedown actions through registrars and hosting providers, compressing the window attackers have to weaponize that infrastructure.

Organizations that combine domain and brand monitoring with automated takedown workflows dramatically reduce the dwell time of phishing infrastructure. The faster a fake domain or login portal is removed, the fewer users are exposed.

Threat Intelligence and AI Detection

Threat intelligence feeds provide SOC teams with a continuously updated view of known phishing infrastructure, including malicious IPs, domains, URL patterns, and campaign tactics used by active threat groups. When ingested by an AI-powered detection layer, this intelligence helps analysts distinguish genuine threats from false positives far more efficiently than manual review allows.

AI detection models trained on phishing indicators learn to recognize subtle patterns: slight variations in sender display names, unusual link structures, and language characteristics associated with spear phishing and business email compromise (BEC). Modern phishing campaigns are highly targeted. Spear phishing attacks craft lures specific to the recipient’s role, relationships, or recent activity, and that level of personalization is exactly where AI-powered detection adds the most value over rule-based filters.

Automated phishing analysis tools powered by machine learning can evaluate thousands of alerts simultaneously, surfacing the highest-risk threats for analyst attention while handling clear-cut cases autonomously.

What Are the Main Types of Phishing Attacks?

Understanding the phishing landscape helps SOC teams tune their monitoring programs to the threats they’re most likely to face. The four most common phishing attack types are:

Email phishing: Mass campaigns sent to large recipient lists, relying on volume to generate successful clicks.
Spear phishing: Targeted attacks that personalize lures based on the recipient’s identity, role, or relationships. These have higher success rates and require AI-assisted detection to be reliably caught.
Smishing and vishing: Phishing delivered via SMS or voice call, increasingly used to reach targets outside email security controls.
Business email compromise (BEC): Attackers impersonate executives or trusted vendors to authorize fraudulent wire transfers or data disclosures. BEC attacks often lack malicious links entirely, which is why behavioral and contextual AI detection is so valuable here.

What Are the Signs of a Phishing Email?

When building or refining your monitoring ruleset, these are among the clearest indicators:

Sender domain mismatch: The display name looks legitimate, but the actual sending domain does not match
Urgency or fear-based language: “Your account will be suspended,” “Act immediately,” “Verify now”
Generic salutations: “Dear Customer” instead of a recipient’s actual name
Suspicious or obfuscated URLs: Hovering reveals a destination that does not match the link text
Unexpected attachments: Especially password-protected files or Office documents requesting macro execution

Automated phishing monitoring tools flag these patterns at scale, enabling SOC teams to triage high-confidence indicators across thousands of messages simultaneously.

How Torq Automates Phishing Monitoring and Response

High-volume phishing campaigns put real pressure on SOC teams. Analysts pulling indicators into multiple tools, running lookups by hand, and escalating or closing tickets one by one burn through capacity fast, even in well-staffed operations. Torq’s AI SOC Platform is built specifically to solve that problem, connecting phishing detection tools — including email gateways, domain scanners, SIEMs, and threat intelligence platforms — into automated workflows that run from alert through remediation.

Here’s how a Torq-automated phishing workflow looks in practice:

A phishing email is reported by a user or flagged by the email gateway.
Torq’s AI Agents, purpose-built to act at every stage of the threat management lifecycle, automatically extract indicators (sender domain, embedded URLs, attachment hashes) and enrich them against threat intelligence feeds and sandboxing services.
When the threat is confirmed, Torq quarantines the email across all mailboxes that received it, blocks the malicious URL at the proxy, and creates a case in Case Management with full investigation context.
When the case involves a spoofed domain, Torq triggers a phishing takedown service workflow through the appropriate registrar or hosting provider.
Analysts receive a fully enriched case with complete investigation context, ready for decisions rather than data collection.

The Torq Hyperautomation™ engine handles high-volume, well-defined cases end-to-end, while Torq Socrates™, Torq’s agentic SOC orchestrator, drives the complex, multi-step investigations that benefit from reasoning and contextual judgment. Torq HyperAgents™ ties it together — agentic technology that executes across your entire security stack so your team stays in control while your capacity scales. The result: Carvana now runs 100% of its Tier 1 security alerts through Torq’s agentic AI, automating 41 runbooks within the first month of deployment.

Torq also provides workflow templates for common phishing scenarios, including monitoring an Outlook mailbox for phishing with VirusTotal and handling phishing via IMAP, giving security teams a fast path to automated coverage.

For a deeper look at response strategies, explore six automated phishing response approaches SOC teams use to accelerate containment.

Building a Proactive Phishing Defense Strategy

The shift from reactive to proactive phishing defense takes a combination of monitoring coverage, automation, and ongoing refinement working together.

A proactive approach starts with building visibility across the external threat landscape: registering your own brand-adjacent domains before attackers can, implementing DMARC, DKIM, and SPF across all sending domains, and deploying a domain monitoring service to track lookalike registrations continuously.

Automation turns that visibility into action. When your monitoring layer surfaces a new threat — whether a lookalike domain, a credential harvesting page, or a spoofed executive email — automated workflows triage, escalate, and remediate without waiting for analyst bandwidth. Building phishing investigation and response playbooks into your automation platform and testing them regularly is what separates a program that scales from one that struggles to keep pace. And when a phishing attempt escalates into a broader incident, having automated SOC incident response workflows ready means your team responds at machine speed, across every affected system simultaneously.

User reporting is a meaningful layer of defense too. Employees who recognize phishing indicators and flag suspicious emails feed signal back into your monitoring system. The more phishing reports your team processes through automated phishing analysis, the sharper your detection models get over time.

The Future of Automated Phishing Detection

Phishing attacks are evolving alongside the AI tools defenders use to stop them. Generative AI now enables attackers to craft personalized, grammatically flawless lures at scale, removing the spelling errors and awkward phrasing that users have been trained to spot. Deepfake audio and video add another layer of credibility to vishing and BEC campaigns.

The opportunity on the defense side is equally significant. Machine learning models are getting better at detecting subtle semantic and structural anomalies in phishing content, even when surface-level indicators are clean. AI-powered sandboxing can detonate suspicious URLs in real time and analyze behavior rather than relying on static signatures. And agentic AI platforms are making it practical for SOC teams to build, deploy, and iterate on phishing response workflows faster than ever — Torq’s Agentic Builder is a prime example, turning human intent into production-grade AI Agents in minutes.

Over the next few years, the most effective phishing defense programs will treat monitoring and response as a continuous, automated loop they build, test, and refine constantly. The teams that build that foundation now will be better positioned as the threat landscape keeps shifting.

Phishing Moves Fast. Your SOC Should Move Faster.

Phishing is a persistent, evolving threat that demands continuous monitoring, fast response, and a security operation built to scale. The organizations that stay ahead of phishing campaigns invest in the visibility layer, connecting domain monitoring, brand monitoring, and email analysis to automated response workflows that act without delay.

Torq’s AI SOC Platform gives SOC teams exactly that: an end-to-end automation layer that connects phishing detection to triage, remediation, and case management in a single, orchestrated workflow. Whether you’re handling a high-volume phishing campaign or a precision spear phishing attempt, Torq keeps your team focused on the decisions that matter.

Ready to explore how Torq helps automate phishing responses for enterprise SOC teams? Don’t die. Get Torq.

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FAQs

What is phishing monitoring in cybersecurity?

Phishing monitoring is the continuous detection and analysis of email, URL, domain, and brand threats used in phishing attacks. A strong phishing monitoring program combines domain name monitoring services that track lookalike registrations, brand monitoring tools that flag impersonations, and threat intelligence feeds that provide context on active campaigns. The goal is to detect phishing infrastructure early and respond before users are exposed.

What are the most common types of phishing attacks SOC teams need to monitor for?

The four most common types are email phishing (mass campaigns), spear phishing (targeted, personalized attacks), business email compromise (BEC), and smishing and vishing (SMS and voice-based attacks). Each requires different detection techniques, and spear phishing and BEC in particular benefit from AI-powered detection because behavioral and contextual signals carry more weight than traditional indicators alone. See our guide to security incident categories for a broader view of threat types.

How does automated phishing response work?

Automated phishing response uses a security automation platform to connect detection signals — from email gateways, URL scanners, and threat intelligence feeds — to response actions like quarantine, URL blocking, and case creation. When a phishing email is confirmed, the automation platform executes the response playbook immediately. Torq automates phishing investigation and response end-to-end, reducing mean time to respond and freeing analysts for complex investigations.

What is a phishing takedown service and how does it help?

A phishing takedown service submits removal requests to domain registrars, hosting providers, and content delivery networks to remove phishing infrastructure such as fake login portals, lookalike domains, and impersonation sites. Security automation platforms like Torq trigger takedown workflows automatically when a confirmed phishing domain is detected, significantly compressing the window attackers have to run a campaign.

What is brand monitoring in cybersecurity?

Brand monitoring in cybersecurity is the practice of tracking unauthorized use of your organization’s identity — including logos, domain names, executive names, and trademarks — across the web, social media, and app stores. It’s a key component of proactive phishing defense because attackers frequently build credibility with targets by impersonating trusted brands. Combining brand monitoring with automated response enables faster detection and takedown of impersonation campaigns.

How does AI improve phishing detection accuracy?

AI models trained on phishing indicators identify subtle patterns — including sender anomalies, link obfuscation techniques, and language associated with urgency and social engineering — that rule-based filters are prone to miss. They also improve over time as more data flows through the system. In high-volume environments, AI significantly reduces false positive rates and helps SOC analysts focus attention on genuine threats. Torq’s AI Agents apply this intelligence inside automated response workflows to enrich and triage phishing alerts at every stage of the threat lifecycle.

How can SOC teams reduce alert fatigue from phishing monitoring?

The most effective approach combines AI-powered detection — which routes low-confidence alerts to automated handling — with a security automation platform that resolves high-confidence cases end-to-end. Torq’s Hyperautomation™ engine processes well-defined phishing cases autonomously, so analysts spend time on the investigations that need human judgment.

What makes Torq different for phishing response automation?

Torq is an AI SOC Platform that combines Torq HyperAgents™, Torq Socrates™ (Torq’s agentic SOC orchestrator), and Torq Hyperautomation™ into a single platform purpose-built for enterprise security operations. Where point tools handle one piece of the phishing response workflow, Torq orchestrates the full lifecycle — from initial detection and enrichment through containment, takedown, and case closure — with AI Agents acting at every stage. That end-to-end orchestration is what drives outcomes like closing over 90% of security cases autonomously.

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 AI SOC Hyperautomation Security Automation 101

How AI Should Actually Work in Your SOC

By Torq

December 20, 2025

9 Minute Read

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See how Torq harnesses AI in your SOC to investigate, prioritize, and respond to threats faster.

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TL;DR

The problem: Attackers achieve breakout in under 48 minutes. The average alert investigation takes 70 minutes. And 40% of security alerts are never investigated. Most AI in the SOC helps at the margins — summarizing alerts, suggesting actions — but doesn’t close the gap.

What actually works: AI-autonomous security operations, where agentic AI triages, investigates, and remediates end-to-end without human intervention on routine cases. Not AI that advises. AI that acts.

Five questions to ask vendors: Does it act or just advise? Does it integrate across your full stack? Is every decision explainable? Can you configure where autonomy ends, and human judgment begins? Can they show measurable outcomes from real deployments?

Bottom line: The distinction between AI-assisted and AI-autonomous is between incremental improvement and operational transformation. The SOCs that win in 2026 aren’t the ones with the biggest headcount — they’re the ones that let AI handle volume while humans handle strategy.

The math doesn’t work anymore. Attackers now achieve breakout — moving from initial access to lateral movement — in under 48 minutes. Meanwhile, the average alert investigation takes 70 minutes.

AI in security operations was supposed to fix this. Instead, most implementations have delivered chatbots bolted onto legacy workflows, alert summarization that still requires human action, and ML-based detections that generate more noise than signal. These implementations help at the margins, but they don’t solve the core problem: volume, speed, and the widening gap between attacker efficiency and defender capacity.

And it gets worse. According to the SACR AI SOC Market Landscape 2025 report, 40% of security alerts are never investigated at all. Another 61% of security teams admitted to ignoring alerts that later proved to be critical incidents.

The real opportunity isn’t AI-assisted security operations. It’s AI-autonomous security operations. And the difference between those two concepts is where outcomes live.

Why Does Most AI in Security Operations Fall Short?

Let’s be honest about what AI in the SOC has actually delivered over the past few years. Mostly, we’ve seen alert summarization tools that save analysts a few minutes of reading. Chatbot interfaces that answer questions but don’t take action. Machine learning detections promise precision but deliver false positive rates that make analysts want to throw their laptops out the window.

These tools help at the margins. But they don’t fundamentally change the operational reality. Analysts are still drowning. The SANS 2025 SOC Survey confirms that 66% of teams cannot keep pace with incoming alert volumes. Almost 90% of SOCs report being overwhelmed by backlogs and false positives.

Most AI Stops at Analysis — That’s the Problem

Here’s the thing most AI vendors won’t tell you: their solutions only address the first step of the threat lifecycle. Triage? Covered. Investigation? Partially. Response? “That’s on you.”

A true AI SOC must manage the complete threat lifecycle — from triage through investigation to response. The work doesn’t end once you’ve identified a threat. The Agentic SOC takes action and closes cases. Autonomously.

Most “AI in the SOC” products are really just analysis tools with a chat interface. They’ll tell you what’s happening. They might even tell you what to do about it. But they won’t actually do anything. That still requires a human to click buttons, switch tabs, copy data between systems, and execute remediation steps manually.

The AI SOC that actually works looks different:

Triage: AI ingests and normalizes telemetry from across your security stack, correlating and deduplicating events to reduce noise. It delivers verdicts that separate false positives from actual risk — before alerts ever reach a human.
Investigate: Specialized AI agents gather evidence, assemble timelines, and summarize findings. No more manual enrichment across six browser tabs.
Respond: Contain. Coordinate. Remediate. AI executes response actions autonomously and ensures critical threats reach the right people.

What Should AI in Security Operations Actually Do?

The shift that matters isn’t from manual to AI-assisted. It’s from AI-assisted to AI-autonomous. That means AI that doesn’t just summarize alerts, but triages, investigates, enriches, and remediates — end-to-end, without human intervention unless escalation is genuinely required.

This is where agentic AI enters the picture. Unlike traditional automation or generative AI that responds to prompts, agentic AI sets goals, plans actions, and executes. It reasons through problems. It adapts to context. It operates with the autonomy of a skilled analyst, but at machine speed and scale.

Here’s what this looks like in practice:

An alert fires from your EDR. Within seconds, AI enriches the alert with data from your SIEM, correlates related events across IAM and cloud infrastructure, identifies the affected user and endpoint, checks asset criticality, and reviews recent behavior patterns.
If needed, it contacts the user via Slack to verify suspicious activity.
Based on the investigation findings and predefined runbooks, it either remediates autonomously — isolating the endpoint, revoking sessions, updating blocklists — or escalates to a human analyst with full context and recommended actions.

No human touched that workflow unless escalation was required. The entire process completes in minutes, not hours.

At Torq, this is exactly what our AI SOC delivers. Socrates, our AI SOC Analyst, coordinates a multi-agent system where specialized AI Agents handle triage, investigation, remediation, and case management in parallel. According to IDC, organizations using Torqcan automate more than 95% of Tier-1 analyst tasks. That’s operational transformation.

The human role doesn’t disappear; it evolves. Analysts stop clicking through repetitive alerts and start supervising AI operations, handling the truly complex cases, and doing what they actually got into security to do: hunt threats, improve defenses, and outthink adversaries.

What Does AI-Autonomous Security Operations Look Like in Practice?

These are production outcomes from organizations running Torq HyperSOC.

Carvana

Carvana‘s lean security team was buried in Tier-1 alert volume — repetitive investigations that consumed hours but rarely surfaced real threats. Critical work like threat hunting and posture improvement kept getting pushed back. After deploying Torq’s agentic AI, the platform now handles 100% of Tier-1 and Tier-2 security events autonomously. The team operates at the effectiveness of a SOC five times its size, with analysts focused on strategic projects instead of monotonous triage. They took a deliberate “crawl-walk-run” approach — starting with AI-assisted triage before expanding to full autonomous remediation.

Valvoline

A corporate divestiture cut Valvoline‘s security team in half. Their legacy SOAR was brittle and slow to build on. A Rapid7 integration had stalled for months. After replacing their SOAR with Torq, the team was live on phishing response and EDR alert handling within the first week. The stalled integration was delivered in days. Result: six to seven analyst hours saved per day, with ROI measured in 48 hours — not the 12–18 months legacy SOAR typically requires.

Kenvue

Kenvue‘s SOC faced fragmented security data across a highly customized IT environment. Manual data collection ate into investigation time, and the team couldn’t measure its own performance. After building a full lifecycle case management infrastructure in Torq — automating case creation, IOC extraction, enrichment, and response actions — analysts now start investigations with full context already assembled.

What’s Next for AI in Security Operations?

Attackers aren’t waiting for defenders to figure out AI. They’re using it now — to generate convincing phishing campaigns, automate reconnaissance, identify vulnerabilities faster, and scale attacks that would have required teams of humans. According to the Verizon 2025 DBIR, synthetically generated text in malicious emails has doubled over the past two years. Here’s how defenders can win.

Near-term: Agentic AI becomes the standard operating model for high-performing SOCs. Organizations that don’t adopt will fall further behind as attackers increasingly leverage AI to accelerate their own operations. The asymmetry between offense and defense will widen for those relying on human-only workflows.

Multi-agent systems: Rather than a single AI handling everything, specialized agents coordinate complex investigations in parallel — one analyzing network traffic, another examining endpoint behavior, another correlating identity signals. These agents collaborate and cross-reference findings, achieving investigative depth that would require a team of senior analysts working in concert.

5 Key Considerations for Implementing AI in Your SOC

Before you sign another vendor contract, ask these questions:

1. Does it act or just advise? AI that suggests actions still requires human execution. That’s a copilot, not an autopilot. Look for AI that can execute remediation within defined guardrails — isolating hosts, disabling accounts, removing malicious emails — without waiting for human approval on routine cases.

2. How does it integrate? Point-tool AI creates more silos. If your AI solution only works with one data source or one workflow, it can’t deliver cross-environment correlation or end-to-end automation. You need AI that orchestrates across your entire stack — SIEM, EDR, IAM, cloud, ticketing, collaboration tools — simultaneously.

3. Is it explainable? Black-box AI doesn’t fly with auditors, compliance teams, or analysts who need to trust the system. Every decision, every action, every escalation should have a clear audit trail showing exactly what the AI observed, what it concluded, and why it took the action it did.

4. What’s the human-on-the-loop model? Full autonomy isn’t always appropriate. High-severity incidents, sensitive systems, and novel attack patterns may warrant human review. Look for configurable guardrails and escalation paths that let you define where autonomy ends and human judgment begins — and adjust those boundaries as trust develops.

5. Can you measure outcomes? If the vendor can’t show concrete metrics — MTTD reduction, MTTR improvement, alert clearance rates, analyst hours saved — it’s vaporware. Demand proof of impact from real deployments, not theoretical capabilities.

Can You Afford to Stay at Human Speed?

AI in security operations isn’t new. But AI that actually works — AI that operates, not just assists — is.

The difference between AI-assisted and AI-autonomous is the difference between incremental improvement and operational transformation. Between hiring more analysts to handle more alerts and fundamentally changing the economics of security operations. Between drowning in volume and actually getting ahead of threats.

The SOCs that thrive in 2026 and beyond won’t be the ones with the biggest headcount or the most tools. They’ll be the ones that figured out how to let AI handle volume while humans handle strategy. The ones that shifted from human-in-the-loop to human-on-the-loop. The ones that made the leap from AI as a feature to AI as the foundation.

The attackers aren’t slowing down. The alert volumes aren’t decreasing. The talent shortage isn’t resolving itself. The only variable left to change is how you operate.

Ready to see AI in security operations that actually works? Download the Don’t Die, Get Torq Manifesto.

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FAQs

What is AI in security operations?

AI in security operations refers to the use of artificial intelligence to automate core SOC functions — including alert triage, threat investigation, case management, and incident response. Traditional implementations focus on AI-assisted workflows, where AI summarizes or recommends actions that still require human execution. More advanced implementations use agentic AI, where specialized AI agents autonomously triage alerts, gather evidence, make containment decisions, and remediate threats end-to-end — escalating to human analysts only when predefined thresholds require it.

What is the difference between AI-assisted and AI-autonomous security operations?

AI-assisted security operations use AI to help analysts work faster — summarizing alerts, suggesting next steps, or surfacing relevant context. The analyst still makes every decision and executes every action. AI-autonomous security operations use agentic AI to handle the full threat lifecycle independently: triaging alerts, investigating cases, executing response actions, and closing cases without human intervention on routine incidents. The human role shifts from executing tasks to supervising AI operations and handling complex escalations.

What is an agentic AI SOC?

An agentic AI SOC is a security operations center where AI agents autonomously manage the majority of alert triage, investigation, and response workflows. Unlike traditional automation that follows static playbooks, agentic AI reasons through problems, plans its own investigation steps, adapts to context, and executes response actions within defined guardrails. Multi-agent systems coordinate specialized AI agents in parallel — one analyzing network traffic, another examining endpoint behavior, another correlating identity signals — to achieve investigative depth at machine speed.

How does AI reduce alert fatigue in the SOC?

AI reduces alert fatigue by automating the triage and investigation steps that consume most analyst time. Rather than requiring humans to manually review, enrich, and prioritize every alert, AI ingests telemetry across the security stack, correlates and deduplicates events, filters false positives, and delivers high-confidence verdicts before alerts ever reach an analyst. According to the SANS 2025 SOC Survey, 66% of SOC teams cannot keep pace with incoming alert volumes. Organizations using AI-autonomous triage can investigate 100% of alerts — including the 40% that would otherwise go uninvestigated — while freeing analysts to focus on genuine threats and strategic work.

What questions should I ask vendors about AI in the SOC?

When evaluating AI for security operations, ask five key questions. First, does the AI act autonomously or just advise — can it execute remediation, or does it still require a human to click buttons? Second, does it integrate across your full stack (SIEM, EDR, IAM, cloud, ticketing), or does it only work with a single data source? Third, is every AI decision explainable with a clear audit trail? Fourth, what is the human-on-the-loop model — can you configure where autonomy ends and human judgment begins? Fifth, can the vendor demonstrate measurable outcomes from real deployments, including reductions in MTTD and improvements in MTTR, as well as analyst hours saved?

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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1. Assess organizational readiness

2. Define objectives and success metrics

3. Select the right platform

4. Start with high-impact, low-risk use cases

5. Train personnel and establish governance

6. Iterate and expand