AI AI SOC Hyperautomation Security Automation 101

How AI Should Actually Work in Your SOC

By Torq
February 18, 2026
8 Minute Read
How AI should actually work in security operations

Contents

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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 Most AI in Security Operations Falls 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 AI in Security Operations Should 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 Torq can 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.

The Future of 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?

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AI AI SOC

The AI SOC Org Chart for 2026 and Beyond

By John White, Field CISO
February 12, 2026
9 Minute Read
AI SOC org chart for 2026

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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 Virgin Atlantic, he built and transformed security functions for global organizations, including ASOS, Liberty Global, AEG Europe, and KPMG.

AI isn’t a tool you bolt onto your existing SOC. It’s forcing us to fundamentally rethink how security organizations are structured, staffed, and measured. CISOs who treat 2026 as a transition year will fall behind. The ones who redesign their AI SOC org chart now will build teams that operate at machine speed.

I believe there’s a real shift in the landscape that’s going to require organizations to completely rethink and redesign the way they deliver modern security. That’s not hyperbole; it’s why I made the move to Torq as Field CISO.

I’ve spent the better part of 15 years doing security transformation — current state to future state, rinse and repeat. But I’ll be honest: the piece in the middle has fundamentally changed. It’s no longer about shuffling headcount between ops,  GRC, and architecture. It’s about designing an entirely different operating model. And if you’re still thinking about AI as simply “adopting a new tool,” you’re not thinking big enough.

What’s Breaking in the Traditional SOC Model

Let me start with what made me realize incremental change wasn’t going to cut it.

It’s the scale. There’s always been a talent shortage — that’s nothing new. But the attack surface is growing more complex by the day. It’s not just attacks on your organization anymore. You’ve got third parties, cloud sprawl, and AI-powered threats that evolve faster than your team can write detection rules. And no matter how many human resources you throw at the problem, you’re always battling coverage, response time, and the fundamental limitation of human speed.

Here’s the uncomfortable truth: we keep trying to fix machine-speed problems with traditional methods, and the more we do, the further behind we get.

And the promise of “one platform that does everything”? That’s already disappointed most of us. What I’m seeing now is a shift toward thinking about data and automation as the horizontal layers that cut across every vertical, rather than buying another point solution for another discipline.

So if everyone agrees AI adoption is necessary, why hasn’t it happened at scale? It’s not budget. It’s not belief. It’s hesitation.

There’s an accountability gap. Everyone’s looking at each other — IT, data, security — asking, “Who’s going to grasp the nettle?” Who’s going to put a stake in the ground and take a direction on AI adoption? Leaders hesitate because they don’t want to go in a direction that might not work out. It’s not fear exactly. It’s waiting for permission.

From my experience? Whichever function steps forward first will benefit most. The others become customers of that team. And security is uniquely positioned to lead this, because automation and AI cut across everything we do.

The New AI SOC Org Chart: Outcome, Judgment, Execution

If a CISO were building a security organization from scratch today (no legacy structure, no inherited headcount), what would it look like?

I’ll tell you what it wouldn’t look like: the traditional vertical model based on hierarchical structures, siloed roles and responsibilities, and tenure-based progression. That model is dissolving, whether we like it or not.

Today’s forward-thinking CISO is about to embark on a revolutionary step change. It’s time to embrace a purposeful shift to outcome-based teams, working holistically across pools of human and technical resources to achieve innovative and optimized risk reduction.

I see the model moving toward three distinct layers:

  1. Outcome layer: This is where you define strategic objectives: where we are now, where we need to be, and what success looks like. The people here are your architects, strategists, risk practitioners, and transformation leads. They’re no longer managing a vertical. They’re defining the outcomes the entire function needs to deliver.
  2. Judgment layer: This is where specialists provide oversight. They ensure quality and policy compliance. They make decisions on irreversible actions. They lead complex incidents and facilitate post-incident learning. These are your senior practitioners, people with deep expertise who can validate whether the execution layer is delivering the right results.
  3. Execution layer: This is where AI and automation operate, continuously, consistently, at machine speed, within predefined guardrails. This layer never sleeps. It provides 24/7/365 coverage. It’s the foundation everything else is built on.

The transformation model I’ve used throughout my career still exists: current state, future state, and a program to get from one to the other. But the piece in the middle has changed. It’s no longer about “What does the org look like? How many people in ops versus GRC versus architecture?” Those silos and verticals… they’re going to dissipate.

Instead, groups of people will come together and use elements of different technologies to deliver a service or product that achieves an outcome. It’s almost like a dev squad. Agile teams. That’s not something security organizations are used to, but it’s where we’re headed.

Will AI Replace SOC Analysts? Displaced, Not Replaced

Now, the question I get asked most: “If AI handles 90-95% of Tier-1 work, does that mean we’re cutting headcount?” In my humble opinion, that’s completely the wrong way to think about it.

AI isn’t there to replace people. It’s there to increase capacity, coverage, and response speed — continuously and consistently, within predefined guardrails that ensure outcomes.

Ask anyone in a security function, from CISO to Tier-1 analyst, and they’ll tell you they haven’t got anywhere near enough time to cover all the aspects of their role that they should. AI gives that time back.

The way I think about it: analysts won’t be replaced, they’ll be displaced: 

  • Those with architectural and engineering skills, the thought leaders, and innovators keeping up with technological advances, will move into the outcome layer, helping define what the organization needs.
  • Those who are GRC-focused, specialists in their domain, very experienced, and who know what they’re looking for — they’ll move into the judgment layer, building workflows, validating outputs, ensuring the function is delivering the right results.
  • The execution layer becomes AI-native. Fewer and fewer humans working at human speed will be required in roles that demand machine speed. We can’t have that function lagging as it does today.

And here’s the thing: CISOs are desperate for headcount. If I can take people doing fairly mundane, repeatable operational tasks and move them into something that motivates them more, gives them career development, and allows them to use new skills? That’s a good thing.

You can’t replace the face-to-face skills needed to liaise with your business, understand strategy, educate stakeholders, or provide context and judgment on complex situations. That’s very, very hard for AI at the moment. So it’s back into that judgment box. Human skills become more valuable, not less.

What the AI SOC Org Chart Looks Like in Practice

Let me give you a concrete example of how this AI SOC org chart works in practice: a Detection, Response & Containment team in this new model. The outcome: Rapidly detect, contain, and limit business impact.

AI SOC org chart in practice: a Detection, Response & Containment

What traditional teams does this replace? Tier-1 and Tier-2 SOC. The low-judgment, low-automation work that’s been burning analysts out for years.

The future is high judgment plus high automation: AI-orchestrated, outcome-driven teams. Strategy and architecture designing outcomes. Specialists assuring operations through judgment. Automation and AI performing continuous and consistent execution.

The great thing about this model is that it’s just as applicable outside the AI SOC. It will soon start making sense to adjacent functions like Privacy, GRC, and IT Operations. It won’t be long before the wider organization adopts this as a common language.

What’s Stopping CISOs from Redesigning Around AI?

So if this is the only path forward, what’s stopping people from moving? There’s unclear ownership. IT, data, security — they’re all looking at each other, asking, “Which one of us is going to do it?” There’s fear of stepping forward first and getting it wrong. There’s a tendency to view AI as just another tool requiring effort and time that teams don’t have.

Here’s how to break through:

  • Accept that the future is now. Check Point just documented a threat actor using AI to build an entire malware platform. What was planned as a 30-week development cycle was executed in hours. When threats move at that speed, a security org built around 9-to-5 shifts and procurement cycles isn’t just inefficient. It’s indefensible.
  • Start with your current state. Look across your architecture, processes, skills, and resources. But instead of thinking in disciplines, think in outcomes.
  • Design the organization of the future with AI and automation at the heart. Start with machine speed. Start with 24/7/365 coverage that never sleeps and delivers consistent results. That’s the foundation. Everything else is built around the edges.

The CISOs who map this out now will be able to deploy and sustain AI-native operations when they need it most — when they’re being attacked. The organizations that try to bolt it on later, that haven’t done the thinking, are going to throw these tools in and find it doesn’t work. It won’t be sustainable. It’ll put them in a worse position when they’re under pressure.

The Security Orgs That Get AI Right… and What Happens to Those That Don’t

In two to three years, the organizations that started designing their adoption journey now will be the ones able to sustain that change when they potentially need it most.

Those that don’t? They’re going to be the ones held up as examples. The companies that hesitated. The ones still looking for perfection instead of recognizing this is no longer early adoption; it’s a necessity.

The model I keep coming back to is this: humans at the edges, AI working at machine speed in the middle. A continuous improvement loop where outcomes are defined, execution is automated, and judgment provides the feedback that keeps everything aligned.

It’s a revolutionary step change. I appreciate that’s quite a leap. But why take a small step when you need to make a jump? 

The future isn’t about who has the most analysts or the biggest budget. It’s about who figured out how to let AI handle volume while humans handle strategy. The organizations that design that model now will be the ones still standing when the machine-speed attacks arrive.

And they will arrive.

See how Torq can save your team, strategy, and budget. 

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

Todd Willoughby, Director

Compuquip logo in white

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

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Yossi Yeshua, CISO

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Hyperautomation Security Automation 101 Use Cases

How to Create an Incident Response Plan in Four Steps 

By Torq
February 10, 2026
9 Minute Read
Build an Incident response plan with automated workflows

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TL;DR

  • What is an incident response plan (IRP)? A documented strategy for detecting, containing, eradicating, and recovering from cybersecurity incidents like ransomware, data breaches, and insider threats.
  • Why it matters: U.S. data breach costs hit $10.22 million in 2025, and most organizations take 100+ days to recover. A static plan won’t cut it; you need a living, automated system.
  • The 4 steps to build an effective IRP: Build your IRP around four core pillars: defining ownership and accountability, establishing detection and triage processes, creating response playbooks, and continuously improving based on real incident data. Each step builds on the last to create a system that actually executes under pressure.

Is your incident response plan a dusty PDF hidden in a drive that nobody’s read since compliance season?

According to IBM’s 2025 Cost of a Data Breach Report, the average cost of a data breach for U.S. companies hit an all-time high of $10.22 million in 2025. And nearly two-thirds of breached organizations are still recovering — with recovery typically extending beyond 100 days.

Outdated procedures aren’t going to cut it. This guide is for Security Architects and Operations Analysts. The ones who get notified at 2am when something goes wrong. Here’s how to build a modern incident response plan that holds up under fire.

What is an Incident Response Plan?

An Incident Response Plan (IRP) is your organization’s documented strategy for detecting, containing, eradicating, and recovering from cybersecurity incidents — ransomware, data breaches, insider threats, and everything in between.

But here’s where most organizations get it wrong: they treat the IRP as a compliance checkbox. A static document that satisfies auditors but crumbles under real-world pressure.

An effective IRP reduces downtime through clear action paths, meets compliance requirements for frameworks like NIST and ISO 27001, and builds organizational resilience through continuous improvement. Your IRP should evolve with every incident, every tabletop exercise, and every new threat vector.

Static plans fail under pressure. Automated, adaptive response systems don’t.

6 Key Components of a Strong Cybersecurity Incident Response Plan

NIST’s April 2025 guidance sets forth six principles aligned with CSF 2.0: Govern, Identify, Protect, Detect, Respond, and Recover.

1. Governance and preparation: Establish your incident response policy, define what constitutes an incident, and secure executive buy-in. NIST now recommends expanding incident response involvement beyond IT to include leadership, legal, PR, and HR.

2. Asset identification: Map your critical systems, data repositories, and crown jewels — the assets that would cause catastrophic damage if compromised.

3. Protection mechanisms: Access management, network segmentation, endpoint protection. These reduce the attack surface and buy your team time.

4. Detection and analysis: According to Software Analysis Cyber Research, enterprises with 20k+ employees are drowning in more than 3k alerts daily, generated by an average of 28 different tools. Detection isn’t just generating alerts — it’s enriching them with context, eliminating false positives, and surfacing signals that actually matter.

5. Containment, eradication, and recovery: When an incident is confirmed, speed is everything. Each phase needs predefined playbooks that execute in seconds, not hours.

6. Post-incident review: Blameless postmortems, updated playbooks, refined detection rules — this is how good SOCs become great ones.

Why These Components Aren’t Enough on Their Own

The six components above give you the framework. But a framework is only as good as its execution — and that’s where most incident response plans quietly fail.

The gap isn’t knowledge. Security teams know what needs to happen. The gap is speed, consistency, and coordination under pressure. When an incident hits, analysts are expected to query multiple tools, correlate data manually, follow runbooks step by step, notify the right stakeholders, and document every action — all while the clock is ticking and the blast radius is expanding.

According to the SANS 2025 SOC Survey, 66% of SOC teams can’t keep pace with incoming alert volumes. Sophos’s 2025 research found that 76% of IT and cybersecurity professionals experienced burnout or fatigue over the past year — and 69% said it’s getting worse.

This is exactly why Hyperautomation has become essential to modern incident response. Hyperautomation doesn’t replace your IRP; it makes it executable. It turns static playbooks into automated workflows, routes tasks to the right people instantly based on your RACI matrix, enriches alerts with context before an analyst ever touches them, and generates audit-ready documentation without manual effort.

The four steps below are designed with this reality in mind. Each one includes guidance on how Hyperautomation transforms that step from a static process into an operational system that holds up at 2am on the worst night of the year.

4 Steps to Create an Effective Incident Response Plan

Step 1: Define Scope, Roles, and Responsibilities

Every incident response failure has a root cause, and “nobody knew who was supposed to do what” is near the top.

Avoid this and start by mapping your systems and assets. What’s in scope? Where does your data live? Document your communication channels and escalation paths.

Then build your RACI matrix for every incident type, define who is Responsible, Accountable, Consulted, and Informed.

ActivitySOC AnalystIncident CommanderLegalCommsExecutive
Initial TriageResponsible AccountableInformedInformedInformed
ContainmentResponsible AccountableConsultedInformedInformed
Evidence CollectionResponsible AccountableConsulted– Informed
External CommunicationConsultedAccountableConsultedResponsible Accountable
Recovery DecisionConsultedAccountableConsultedInformedAccountable

However, with Hyperautomation, task routing becomes instant. When an incident hits a severity threshold, the right people are notified automatically — no frantic Slack messages and no dropped handoffs.

Step 2: Develop Detection and Triage Workflows

Your Security Information and Event Management (SIEM) screen is lighting up with every color in the sunset. Your Endpoint Detection and Response (EDR) is going off. Now what?

Start with high-fidelity data sources: EDR, identity providers, network detection, cloud security posture management. Your SIEM should correlate events across these sources — not just aggregate them.

Then build triage criteria. Not every alert deserves human attention. Define what gets auto-closed, what gets investigated, and what triggers immediate escalation.

The problem? Research shows almost 90% of SOCs are overwhelmed by backlogs and false positives, and more than 70% of SOC analysts report burnout from alert fatigue.

Hyperautomation transforms this. Instead of analysts manually enriching every alert — checking VirusTotal, querying Active Directory, pulling user context — automation handles it instantly. Alerts arrive pre-enriched. False positives get auto-resolved. Real threats get fast-tracked with all relevant evidence attached.

The result? According to IBM’s 2025 Cost of a Data Breach Report, organizations using AI and automation extensively saved an average of $1.9 million in breach costs and reduced the breach lifecycle by 80 days.

Step 3: Create Containment and Remediation Procedures

The moment you confirm an incident, the clock is already ticking. Every second an attacker spends in your environment is another second they’re moving laterally, escalating privileges, or staging ransomware.

Build playbooks for your most common incident types:

  • Phishing and credential compromise: Disable accounts, force password resets, revoke sessions, check for mail forwarding rules, scan for lateral movement
  • Malware and ransomware: Isolate endpoints, block C2 communications, identify patient zero, assess spread, preserve evidence
  • Data exfiltration: Identify data accessed, block egress channels, assess notification requirements, preserve logs
  • Insider threat: Revoke access immediately, preserve evidence, coordinate with HR and legal

Each playbook should include specific actions with tool names: “Isolate endpoint X using EDR tool Y. Block IP range Z at the firewall.”

Manual execution is slow and error-prone.With Hyperautomation, these playbooks don’t live in a wiki — they execute automatically. A confirmed phishing incident can trigger account disablement, session revocation, domain blocking, and case creation simultaneously across every tool in your stack. Containment that used to take 30 minutes happens in seconds.

Step 4: Establish Post-Incident Review and Continuous Improvement

Every incident is expensive. Extract value from it.

Within 72 hours of resolution, conduct a blameless postmortem. What did you detect well? What did you miss? Where did handoffs break down?

Track key metrics consistently:

  • MTTD (Mean Time to Detect): Time from compromise to detection
  • MTTA (Mean Time to Acknowledge): Time from alert to analyst assignment
  • MTTR (Mean Time to Respond): Time from detection to containment and resolution

Organizations with mature threat intelligence integration demonstrate 28-35% faster MTTR than those relying solely on internal data.

Feed lessons back into playbooks, detection rules, and training. Update your RACI if roles are unclear. Hyperautomation can generate audit-ready reports automatically and track metrics across incidents to identify trends.

Incident Response Plan Templates: Essential Components

Your IRP template should include:

1. Incident Classification Matrix: Severity levels (Critical, High, Medium, Low) with response time SLAs and escalation triggers

2. Contact and Escalation Directory:Internal teams and external parties (forensics firm, legal counsel, law enforcement, regulators)

3. Playbook Library: Step-by-step procedures for your top ten incident types with tool-specific instructions

4. Communication Templates: Pre-drafted internal updates, customer notifications, regulatory disclosures, and press statements

5. Evidence Collection Checklist: What to collect, how to collect it, and chain of custody requirements

How Torq Hyperautomation Transforms Incident Response Planning

When an incident hits, analysts don’t have time to flip through a 200-page document or manually query six different tools.

This is exactly what Torq Hyperautomation™ solves. Torq turns your incident response plan from a static document into a living, executable system — one that orchestrates your entire security stack, automates repetitive tasks, and empowers analysts to respond at machine speed.

The impact is real: for the first time in five years, global data breach costs declined, driven by faster containment through AI-powered defenses. Organizations experienced breaches on average for 241 days, the lowest in nine years.

Here’s how Torq transforms each phase of incident response:

  • Alert enrichment happens instantly: Torq connects your entire security stack (SIEM, EDR, identity, threat intel) and correlates signals across tools, presenting analysts with unified, context-rich insights in a single pane.
  • Triage decisions are consistent: Multi-layered AI agents handle alert triage automatically, filtering false positives and routing critical incidents to the right response workflows.
  • Containment executes in seconds: One click (or automatic trigger) initiates coordinated response across your entire stack: isolate endpoints, revoke credentials, block IPs — simultaneously, at machine speed.
  • Reporting generates automatically: Immutable activity logs and automated compliance reporting ensure regulatory requirements are met while providing complete visibility into incident response activities.

This isn’t about replacing analysts. It’s about amplifying them. SOC analysts say manual work eats up more than half their time. This is time that could be spent on threat hunting and strategic improvements. Torq gives them that time back.

The results speak for themselves: Valvoline cut analyst workload by 7 hours per day after implementing Torq, and RSM automates 82% of all managed SOC cases — freeing analysts to focus on strategic work instead of repetitive triage.

Ready to transform your incident response plan with Torq? 

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FAQs

What are the 6 phases of an incident response plan?

According to NIST’s CSF 2.0 framework, the six phases are: Govern, Identify, Protect, Detect, Respond, and Recover. These phases work together as a continuous cycle — preparation activities (Govern, Identify, Protect) support the active response phases (Detect, Respond, Recover), while lessons learned feed back into continuous improvement. Torq helps organizations operationalize every phase of the incident lifecycle by connecting tools, automating workflows from detection through remediation, and ensuring consistent execution at machine speed.

How can automation improve incident response times?

Automation dramatically reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) by eliminating manual tasks that slow down response. Instead of analysts manually querying multiple tools, correlating data, and executing containment actions, automation handles alert enrichment, triage, and response actions in seconds.

What roles should be included in an incident response team?

An effective incident response team extends beyond the SOC. NIST recommends including: an Incident Commander (accountable for overall response), SOC analysts (responsible for technical investigation and containment), IT/infrastructure teams (consulted for system access and recovery), legal counsel (consulted for regulatory and liability issues), communications/PR (responsible for external messaging), HR (consulted for insider threat scenarios), and executive leadership (informed and accountable for major decisions). A RACI matrix helps define these roles clearly before an incident occurs.

What's the difference between an incident response plan and a playbook?

An incident response plan is the overarching strategy document that defines your organization’s approach to handling security incidents — including roles, responsibilities, communication protocols, and escalation paths. Playbooks are tactical, step-by-step procedures for responding to specific incident types (like phishing, ransomware, or data exfiltration). Your IRP provides the framework; playbooks provide the execution details. With Torq Hyperautomation, playbooks become automated workflows that execute instantly, ensuring consistent response regardless of who’s on shift.

How often should organizations test and update their incident response plan?

Organizations should review and test their incident response plan at least once a year, typically through tabletop exercises or simulated drills. Beyond that scheduled review, plans should also be updated after any real incident, major organizational or technology changes, or shifts in the threat landscape. A good rule of thumb: if the plan hasn’t been touched in 12 months, it’s overdue.

Are there any industry-specific considerations for building an incident response plan?

Yes. While core IR principles apply universally, industries like healthcare (HIPAA), financial services (PCI DSS, GLBA), and energy/utilities (NERC CIP) have strict regulatory requirements around breach notification timelines and data handling. Critical infrastructure sectors also need to account for OT/ICS systems, where taking a system offline can have physical safety consequences. Always layer your IR plan on top of the specific compliance and operational requirements of your industry.

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

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Integrations Security Automation 101

API Authentication 101: Methods, Pitfalls, and the Power of Real-Time Monitoring

By Torq
February 5, 2026
9 Minute Read
API authentication methods for security automation

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TL;DR

  • APIs are your automation’s Achilles’ heel: When authentication breaks, your security workflows fail silently.
  • Legacy SOAR can’t keep up: Static playbooks weren’t built for modern API ecosystems where tokens expire, endpoints shift, and permissions change without notice.
  • Not all auth methods are equal: API keys are simple but leak easily. OAuth 2.0 is robust but complex. JWTs scale but can’t be revoked. mTLS is secure but operationally heavy. Choose based on risk, not convenience.
  • The real problem isn’t choosing auth — it’s knowing when it fails: Broken authentication doesn’t announce itself. By the time you notice, you’ve accumulated hours or days of security gaps.
  • Real-time API monitoring is non-negotiable: Solutions like Torq Hyperautomation™ continuously validate integration health, alert before tokens expire, and keep your stack connected even when vendors ship breaking changes.

APIs constantly change. Authentication tokens expire, endpoints break, and new permissions appear out of nowhere. And when your API connections fail, your security automation fails with them… silently, without a single alert.

Legacy SOAR and SIEM tools can’t keep up. They weren’t built for modern API ecosystems, and the result is workflow failures, security blind spots, and broken toolchains that nobody notices until an incident exposes the gap.

This blog breaks down the most common API authentication methods, their tradeoffs in modern security contexts, and why real-time API monitoring is the key to keeping your integrations resilient. Because choosing the right authentication method is only half the battle. The other half is knowing when it breaks.

What is API Authentication and Why Does it Matter in Security Architecture?

API authentication answers one question: “Are you who you claim to be?”

Don’t confuse it with authorization. Authentication verifies identity. Authorization determines what that identity can do. Authentication is the bouncer at your SOC’s door — if the bouncer’s asleep, your VIP list doesn’t matter.

Your SIEM needs authenticated access to pull cloud logs. Your automation platform requires credentials to execute containment actions. Your identity provider uses API authentication to sync user data. When any of these mechanisms fail, critical security workflows flatline, often without a single alert.

The stakes? According to the Gartner Market Guide for API Connection, API breaches leak 10 times more data than regular breaches. And the attack surface keeps expanding as organizations bolt on more integrations and automated workflows they never actually monitor.

The 7 Most Common API Authentication Methods (And When They’ll Fail You)

Not all authentication methods deserve your trust. The right choice depends on your security requirements, performance needs, and how much operational pain you’re willing to endure. Here’s the unvarnished truth about each approach.

1. API Keys

API keys are the “just ship it” approach to authentication. Generate a random string, slap it in your request headers, and you’re in. Dead simple.

When to use it: Internal services and situations where simplicity trumps security. API keys work for internal services but become a liability without rigorous management, per OWASP API Security guidelines.

The good: Minimal friction, zero learning curve, instant integration.

When it fails: API keys don’t expire automatically, don’t distinguish between users, and when they leak — over 39 million secrets were exposed last year — you’re exposed until someone manually rotates them.

2. Basic Authentication

Basic auth sends your username and password (Base64 encoded, not encrypted) with every request. It’s the authentication equivalent of writing your password on a sticky note.

When to use it: Never in production.

The good: It works everywhere and requires nothing fancy.

When it fails: Your credentials are one network sniffer away from compromise without TLS. No token expiration. No granular permissions. A relic that persists only because legacy systems refuse to die.

3. OAuth 2.0

OAuth 2.0 lets applications access resources without sharing passwords, using tokens that can be scoped, expired, and revoked.

When to use it: Third-party integrations and any modern API that takes security seriously. The OAuth 2.0 specification is the industry standard for good reason.

The good: Tokens expire. You can revoke access instantly. Scopes grant precisely the permissions needed. When implemented correctly, OAuth 2.0 is genuinely robust.

When it fails: “Implemented correctly” is doing heavy lifting. OAuth defines multiple grant types — authorization code, client credentials, implicit — and choosing wrong creates security holes. Misconfigurations are rampant.

4. JWT (JSON Web Tokens)

JWTs are self-contained tokens that carry everything needed to authenticate a request — the header, payload, and signature — without database lookups.

When to use it: Microservices and distributed systems needing stateless authentication that scales.

The good: Speed and scalability. Services verify the signature and trust the claims without round-trips to an auth server.

When it fails: Expiration. Need to revoke access immediately? Too bad — that token keeps working. Revocation requires workarounds that undermine the stateless benefits you chose JWTs for.

5. Mutual TLS (mTLS)

Mutual TLS is authentication for the paranoid — and sometimes paranoia is warranted. Both client and server present certificates and verify each other. Two-way trust, cryptographically enforced.

When to use it: Zero-trust architectures, financial transactions, and regulated industries. Per NCSC guidance, mTLS defends against credential stuffing, spoofing, and man-in-the-middle attacks.

The good: Rock-solid security with both parties authenticating. Since TLS operates at the network layer, your application code stays clean.

When it fails: Certificate management is operational overhead that compounds at scale. The handshake adds latency. Middleboxes like API gateways must terminate connections, complicating security guarantees.

6. HMAC (Hash-based Message Authentication Code)

HMAC proves both identity and message integrity. Both parties share a secret key used to generate and verify a signature over the request. Match? Authentic and untampered. Mismatch? Rejected.

When to use it: Webhooks and financial APIs where message integrity matters as much as identity. HMAC is the authentication method of choice for 65% of webhook implementations.

The good: Blazing fast — millions of verifications per second. If an attacker modifies a single byte, the signature breaks.

When it fails: Key management complexity scales with your organization. Both parties need the secret, making distribution and rotation operational challenges. And HMAC authenticates but doesn’t encrypt — message content remains visible.

7. OpenID Connect

OpenID Connect layers identity verification on top of OAuth 2.0. Where OAuth answers “what can this application access?”, OIDC adds “who is this user?” It’s the backbone of enterprise SSO, used by Google, Microsoft, and Amazon per the OpenID Foundation.

When to use it: Enterprise applications and SSO implementations requiring standardized identity verification alongside authorization.

The good: Industry-standard identity verification with OAuth’s authorization capabilities baked in.

When it fails: Inherits all of OAuth’s complexity, plus adds its own. Token validation, secure storage, scope management — get any wrong, and you’ve created vulnerabilities.

The Hidden Risk: What Happens When API Authentication Fails

Here’s what keeps security architects up at night: authentication failures don’t announce themselves. They don’t trigger alarms or page the on-call team. They just stop working. Quietly. While your dashboards show green.

Your EDR integration’s OAuth token expires. The refresh mechanism silently fails because someone changed a permission scope three weeks ago. Your containment workflows continue to trigger, but execute nothing. Threats slip through because your “automated response” is a corpse nobody’s noticed.

A cloud provider updates their API endpoint. Your SIEM integration breaks. Dashboards still display data — stale data getting older by the hour. You have zero visibility into a critical segment of your environment until an analyst manually discovers the gap during incident response.

These scenarios play out constantly in SOCs running legacy automation. Traditional tools assume integrations work until proven otherwise. They weren’t designed to monitor API health proactively or handle a world where APIs change constantly.

The fallout extends beyond missed detections: broken alerting, incomplete investigations, manual workarounds devouring analyst time. When automation becomes unreliable, your team stops trusting it. Untrusted automation is worse than none because it creates false confidence while delivering nothing.

Why Real-Time API Monitoring is Critical for Resilient Security Workflows

Modern SOCs don’t run on a handful of integrations. They run on dozens. Hundreds. Each one a potential failure point. Each one depends on authentication that can break without warning.

Real-time API monitoring flips the script. Instead of discovering failures during incident response — the worst possible time — proactive monitoring catches issues before impact. Token expiring in 48 hours? You know now, not when your containment workflow fails during an active breach.

Track expiration schedules across your entire integration portfolio. Receive alerts before credentials need rotation. Maintain visibility into which integrations are healthy versus dead. Identify patterns that predict failures before they occur.

Legacy SOAR platforms lack this by design. They execute playbooks but don’t monitor the health of integrations that those playbooks depend on. That architectural gap creates silent failures everywhere.

Building a Secure, Self-Healing Integration Strategy with Torq

Torq Hyperautomation™ was built for the world that actually exists, the one you’re living in right now. One where APIs change constantly, authentication is complex, and “set it and forget it” integrations are a fantasy.

The platform monitors integration health continuously, alerts on authentication issues proactively, and keeps your security stack connected even when vendors make breaking changes. Real-time API monitoring ensures uninterrupted automations 24/7/365.

Every authentication method we’ve covered? Torq handles it. OAuth 2.0 with multiple grant types, API keys, JWT, mTLS, and custom schemes — the Integration Builder enables rapid connection to any system. Configure bearer tokens for API access. Build custom integrations with whatever authentication your tools demand.

For teams building beyond pre-built integrations, Torq eliminates the complexity. No wrestling with JSON formatting. No becoming an unwilling expert in every vendor’s API quirks. Custom steps get saved to your workspace library and shared across your team. See how Torq solves the integration problem at scale.

When vendors update their APIs, Torq handles the adaptation. Your team focuses on security, not integration babysitting. Check out the Torq Knowledge Base to see API key management in practice.

Dead Integrations Don’t Send Alerts

API authentication is foundational to modern security operations. Every automated workflow, every cross-tool integration, every detection-to-response pipeline depends on it working correctly and continuously. But selecting the right method is only half the battle. The other half — the half legacy tools ignore — is ensuring integrations stay healthy as APIs evolve, tokens expire, and vendors ship breaking changes.

Real-time API monitoring changes the game. Proactively validating integration health and surfacing authentication issues before they impact operations delivers the resilience security teams actually need.

Your automation should work as hard as your team does. It’s time to demand tools that keep up.

Ready to see how Torq keeps your security stack connected — even when APIs change?

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FAQs

What are the 3 most common methods of API authentication?

API keys, OAuth 2.0, and JWT. API keys win on simplicity. OAuth 2.0 dominates third-party integrations with token-based delegated access. JWTs rule microservices where stateless authentication matters. Choose based on security requirements, not what’s easiest. Torq’s Integration Builder supports all three — plus mTLS and custom schemes — so you’re never locked into a single approach.

How do I authenticate API requests?

Depends on the API. For API keys, include the key in headers. For OAuth 2.0, obtain an access token and include it as a bearer token. For JWT, generate a signed token and pass it in the authorization header. Non-negotiable: always use HTTPS. Torq handles the complexity of token management and refresh automatically, so your integrations stay authenticated without manual intervention.

Why do we need authentication in API?

Unauthenticated APIs are open invitations for attackers. Authentication ensures only legitimate users and applications access your resources. In security contexts, broken authentication is how threats bypass your tools and execute actions your workflows were supposed to prevent. That’s why real-time monitoring of authentication health matters as much as choosing the right method.

How to test REST API with authentication?

Obtain valid credentials for your test environment. Use Postman or cURL to construct requests with proper headers. Validate authenticated requests succeed and unauthenticated requests get rejected. Test edge cases: malformed tokens, expired credentials, revoked access. In Torq, you can test each workflow step in real time — getting instant feedback before deploying to production.

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

Todd Willoughby, Director

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

Phillip Tarrant, SOC Technical Manager

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AI SOC Hyperautomation

The Economics of an Agentic SOC: How AI Reduces Security Operations Costs

By Leonid Belkind, Co-Founder and CTO
February 2, 2026
6 Minute Read
Agentic SOC economics powered by Torq HyperSOC

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This article was originally published on Security Info Watch. 

Running a SOC has never been cheap — but in 2026, it’s become unsustainable. The combination of surging alert volumes, rising labor costs, sprawling tool stacks, and skyrocketing breach expenses has pushed the traditional model to the breaking point.

For years, SOC leaders tried to solve the problem the same way: Throw more people and tools at it. But with burnout at an all-time high, analyst hiring pipelines empty, and budgets shrinking, that strategy has hit a wall.

The only path forward is automation — and more specifically, an agentic SOC powered by AI Agents, Hyperautomation, and enterprise-grade architecture.

The True Cost of Running a SOC

Even the most mature SOCs are weighed down by cost drivers that compound year after year:

People Costs

  • High salaries, high turnover: The average SOC analyst salary tops $100K, but with burnout rampant, many leave within 18–24 months. Each departure triggers recruiting, onboarding, and retraining costs that can easily exceed six figures.
  • Lost productivity: Every time an analyst exits, tribal knowledge leaves with them. Teams spend months rebuilding expertise.
  • Overtime and coverage gaps: When teams are short-staffed, the cost isn’t just money — it’s missed alerts and rising risk.

Tooling Costs

  • Tool sprawl: Enterprises now average 80+ security tools. Each comes with licensing fees, integration complexity, and maintenance overhead.
  • Overlapping functionality: Multiple tools often perform similar functions but don’t integrate well, forcing analysts to swivel-chair between dashboards.
  • Integration debt: Legacy SOAR requires brittle scripts and manual upkeep just to keep tools connected — draining engineering hours and budgets.

Breach Costs

  • Rising price tags: The average cost of a breach is $4.88M. Costs multiply across legal, compliance, brand reputation, and customer trust.
  • Machine-speed adversaries: The SACR 2025 AI SOC Market Landscape reports that phishing breaches succeed in under 60 minutes, while average SOC investigations still take 70 minutes. 
  • Downtime and recovery: Beyond fines and settlements, businesses lose millions in downtime, incident response contracts, and recovery operations.

Hidden Costs

  • Training and onboarding: Legacy platforms demand deep coding knowledge. Getting analysts proficient can take months.
  • Compliance prep: Without automation, audit readiness takes weeks of manual evidence gathering.
  • Cloud bloat: Unmanaged accounts, unused service credentials, and unchecked data storage silently drive up cloud bills.

Outsourcing Costs

  • Costs rise quickly: MSSPs and MDRs play an important role in helping organizations extend security coverage, but contracts can run into hundreds of thousands of dollars annually, with fees tied to log volume, endpoint count, or premium services. As the business scales, so do the costs.
  • Shared responsibility: Outsourcers monitor and notify, but the business remains ultimately accountable for a breach. This makes in-house visibility and control essential.
  • Context gaps: Providers manage many customers at once, so they may not always have the deep, continuous familiarity with your environment that your own team develops.

From AI-Enabled to Agentic Autonomy: The Next Leap in SOC Economics

AI already helps analysts sift through noise, but layering GenAI features on top of a legacy SOC isn’t enough. A chatbot that summarizes alerts or a point tool that uses machine learning for detections doesn’t solve the real problem: scale.

The leap from an AI-enabled SOC to a truly autonomous SOC comes when AI isn’t just analyzing data — it’s made up of AI agents orchestrating, investigating, and remediating at machine speed, with humans only stepping in when judgment and strategy are required. These AI agents become an extension of your SOC team, collaborating alongside human analysts, while autonomously taking action across your security stack based on logic and reasoning. 

That’s the difference between an AI-enabled SOC and an agentic SOC. And that’s exactly what Torq delivers:

  • Agentic AI to act like a full Tier-1 analyst team
  • Event-driven Hyperautomation to connect the entire security stack
  • Enterprise-grade AI architecture to scale with business growth

The Three Pillars of an Autonomous SOC

1. Hyperautomation

An autonomous SOC just isn’t possible without automation. When legacy SOAR platforms couldn’t deliver on their promise of security automation, Security Hyperautomation emerged.

Unlike SOAR, Hyperautomation offers unlimited integrations, cloud-native scalability, automated case management, and the ability to create impactful workflow automations in minutes — all of which combine to Hyperautomate 90% of Tier 1 and Tier 2 SOC operations.

2. AI Agents

SOC teams are overloaded with false positives and nonstop alerts from growing security stacks. Agentic AI can handle the majority of everyday alerts autonomously, triaging the majority of daily alerts, reducing burnout, and speeding response.

With LLMs powering AI agents, incidents are enriched, correlated, and resolved end-to-end — much like a human team, only faster and at scale. These agents learn from every case, getting smarter over time. As a result, SOCs can automatically clear out up to 95% of Tier-1 and Tier-2 tickets, while analysts focus on critical threats with richer context and faster decision support.

3. Enterprise-Grade AI Architecture

An autonomous SOC needs a flexible, extensible architecture that integrates seamlessly with the entire security stack and handles data in any format.

At scale, this pipeline can generate tens of thousands — even millions — of alerts, events, and requests. To keep pace, it must have elastic scalability, automatically adjusting resources as demand spikes. This ensures concurrent processing across diverse data types, with priority-based speeds that guarantee critical alerts are always addressed first — even at peak load.

Don’t pay for shelfware. Invest in a system that actually reduces MTTR and consolidates costs.

“Architecture is changing. Automation tools like Torq are being plugged directly into FDR and identity systems — not after the SIEM, but before it.”

– Francis Odum, Software Analyst Cyber Research

What an Agentic SOC Fixes

An agentic SOC doesn’t mean replacing people. It means using automation and AI to handle the volume, so human expertise is focused on the threats that truly matter. This shift delivers tangible economic benefits:

  • Staffing efficiency: Automation absorbs Tier-1 and Tier-2 work, enabling teams to handle 4× more alerts with the same headcount.
  • Tool consolidation: A single Hyperautomation layer connects 300+ integrations, replacing overlapping point automations and cutting down on maintenance costs.
  • Reduced breach impact: Faster MTTR shrinks attacker dwell time, stopping lateral movement before it causes multimillion-dollar damage.
  • Lower training costs: AI-guided workflows accelerate onboarding, letting new analysts contribute in weeks.
  • Improved retention: By eliminating repetitive toil, analysts stay engaged and productive longer — lowering turnover costs.
  • Compliance efficiency: Audit-ready logs and AI-generated case reports save weeks of manual prep per year.

“[With Torq], we have materially improved our operations. We’ve dramatically reduced the cost of operating a security operations center to the point where we can reallocate those funds to different technologies that we need.”

– Dina Mathers, Carvana CISO

The Future of SOC Economics

The old SOC model of more people and more tools has broken SOC economics. With Hyperautomation slashing MTTR, consolidating tools, and reducing manual workloads, organizations can run world-class security operations at a fraction of today’s cost. 

If your SOC is drowning in alerts, shrinking margins, or ballooning headcount costs, it’s time to rethink the model.

Go autonomous in less than 90 days with Torq.

Get the Roadmap
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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 HyperSOC offers unprecedented protection and drives extraordinary efficiency for RSM and our customers.”

Todd Willoughby, Director

Compuquip logo in white

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

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AI AI SOC Security Automation 101

Top Cybersecurity Automation Tools for 2026

By Torq
January 27, 2026
9 Minute Read
Top cybersecurity automation tools for 2026

Contents

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TL;DR

  • Alert overload is crushing SOCs: The average enterprise SOC receives tens of thousands of daily alerts. At least 30% are never investigated.
  • The talent gap keeps widening: The global cybersecurity workforce shortage has hit 4.8 million unfilled positions, a 19% year-over-year increase.
  • Legacy SOAR is failing: Static playbooks require intensive, ongoing maintenance and break when threats evolve, or APIs change.
  • AI-powered Hyperautomation is the answer: Platforms like Torq HyperSOC™ automate the full incident lifecycle — detect, triage, investigate, contain, remediate — with agentic AI that reasons through problems.
  • Real results matter: Torq customers achieve outcomes like 100% Tier 1 alert automation (Carvana), 95% MTTI/MTTR improvement (HWG Sababa), and ROI within 48 hours (Valvoline).

The cybersecurity industry has spent a decade selling you security orchestration automation and response (SOAR) tools that create more work. Static playbooks. Fragile integrations. Six-month implementations. “Just add another connector” — until your SOC looks like a Rube Goldberg machine held together by Python scripts and hope.

Attackers move in minutes. Your legacy SOAR moves in sprint cycles. That gap isn’t a problem. It’s an open door.

This guide breaks down the top cybersecurity automation tools for 2026, how they differ, and how to choose the right one for your organization.

What is Cybersecurity Automation?

Cybersecurity automation uses technology to execute security tasks — detection, investigation, response, remediation — with minimal human intervention. It’s the difference between having analysts manually sift through alerts one by one or having machines handle the noise so humans can focus on what matters most.

Why does this matter now more than ever?

Alert volumes are crushing SOC teams. The average enterprise SOC receives tens of thousands of daily alerts, with at least 30% never investigated. Research shows that 62.5% of security teams are overwhelmed by the sheer volume of data, and analysts spend 75% of their time on manual triage rather than on actual threat hunting.

Attackers move faster than humans. Threat actors exploit vulnerabilities within minutes of discovery. Manual response that takes hours or days? That’s not a gap — it’s a canyon.

The talent shortage isn’t getting better. The global cybersecurity workforce gap has hit 4.8 million unfilled positions, a 19% year-over-year increase according to ISC2 data. You can’t hire your way out of this problem.

Compliance demands consistency. Regulations require documented, repeatable responses. Manual processes are inherently inconsistent and difficult to audit.

The evolution tells the storyFirst came basic scripts and scheduled tasks, better than nothing, but brittle. Then came SOAR platforms with static playbooks — an improvement, but they required constant maintenance and broke when vendor APIs changed. 

Now, we’re in the era of AI-powered Hyperautomation with adaptive reasoning that can actually think through problems instead of just following predetermined paths.

Here’s the thing: automation isn’t only about speed. It’s about enabling your team to focus on threats that require human judgment while machines handle the rest.

7 Types of Cybersecurity Automation Tools

Not all automation tools do the same thing. Understanding the categories helps you identify where the gaps are — and where you’re overpaying for overlapping capabilities. It’s like realizing you’re subscribed to Netflix, Hulu, and Max but only ever watch one. Consolidate or get stuck with the bill.

So with that in mind, let’s break down the core categories of cybersecurity automation tools and what each one actually does.

1. Endpoint Detection and Response (EDR)

What it automates: Threat detection, endpoint isolation, malware removal

Key capabilities: Real-time monitoring, behavioral analysis, automated containment. Modern EDR solutions use machine learning to identify unknown threats and can automatically quarantine infected endpoints before malware spreads.

Limitations: EDR is endpoint-focused. It doesn’t orchestrate across your full security stack, so an endpoint threat that originates from a phishing email or compromised identity requires manual correlation across tools.

Example vendors: CrowdStrike, SentinelOne, Microsoft Defender

2. Security Information and Event Management (SIEM)

What it automates: Log aggregation, correlation, alerting

Key capabilities: Centralized visibility across your environment, compliance reporting, and threat detection through correlation rules. SIEMs are the data backbone of most SOCs.

Limitations: SIEM tools gather logs from a variety of sources and use detection rules to highlight suspicious activities. But generating alerts isn’t the same as resolving them. SIEMs tell you something might be wrong — they don’t fix it. Without additional automation, every alert still requires human investigation.

Example vendors: Microsoft Sentinel, Google Chronicle

3. Email Security

What it automates: Phishing detection, malicious attachment analysis, email quarantine

Key capabilities: URL scanning, sender reputation analysis, automated remediation for malicious messages across all inboxes.

Limitations: Email-only coverage. When a user clicks a malicious link before it’s caught, the threat has already jumped to the endpoint and potentially to identity systems. Email security doesn’t chase it there.

Example vendors: Proofpoint, Mimecast, Abnormal Security

4. Identity and Access Management (IAM)

What it automates: Access provisioning, authentication, credential management

Key capabilities: MFA enforcement, least-privilege access policies, automated deprovisioning when employees leave.

Limitations: IAM excels at managing who can access what, but it doesn’t correlate with threat activity happening across your other tools. A compromised credential generating suspicious behavior might trigger alerts in your SIEM and EDR, but IAM won’t automatically connect those dots.

Example vendors: Okta, Microsoft Entra ID, CyberArk

5. Vulnerability Management

What it automates: Scanning, prioritization, remediation tracking

Key capabilities: Risk scoring, patch management integration, compliance reporting.

Limitations: Vulnerability scanners identify problems but often stop there. The actual remediation — patching systems, updating configurations — typically requires manual intervention or integration with other tools.

Example vendors: Tenable, Qualys, Rapid7

6. Legacy SOAR

What it automates: Workflow orchestration, playbook execution, tool integration

Key capabilities: Connects security tools together, standardizes response procedures, and reduces manual steps in common workflows.

Limitations: According to recent CISA guidance, SOAR platforms are not “set and forget” tools. They require intensive, ongoing configuration and maintenance to function — a fact that underlines the limitations of a playbook-driven approach. Legacy SOAR solutions typically rely on static playbooks and manual script updates, which quickly become outdated and fail to adapt dynamically to new threats. The result? Your automation engineers spend more time maintaining playbooks than your analysts save using them. Learn more about why SOAR is dead.

Example vendors: Palo Alto XSOAR, Splunk SOAR, Swimlane

7. AI-Powered Hyperautomation / AI SOC Platforms

What it automates: The full incident lifecycle — detect, triage, investigate, contain, remediate

Key capabilities: Agentic AI reasoning, adaptive workflows, autonomous decision-making, and end-to-end automation across your entire security stack. Unlike legacy SOAR, these platforms don’t just follow playbooks; they reason through problems.

Considerations: Requires clear guardrails and policies defining what actions can be taken autonomously. Torq provides built-in governance frameworks, human-in-the-loop workflows, and full auditability to ensure safe, scalable AI operations.

Example vendors: Torq

The key insight: Most tools automate a slice of the security workflow. Only AI-powered Hyperautomation platforms connect everything and automate end-to-end.

The Torq Difference

Legacy automation handles pieces of the puzzle. Torq’s AI SOC handles the entire picture.

A true AI SOC platform must do more than orchestrate — it must reason. That means correlating telemetry across multi-vendor, multi-cloud environments. Generating and prioritizing cases automatically. Making policy-aware decisions in real time. Executing remediation safely and autonomously. And maintaining full auditability so you can explain exactly what happened and why.

Torq Hyperautomation™ delivers this through a fundamentally different architecture:

  • Generative AI handles investigation, summarization, and communication.
  • Agentic AI provides adaptive reasoning and autonomous action.
  • Hyperautomation orchestrates across your entire security stack, not just the tools with pre-built connectors.
  • Case management unifies triage, investigation, and response in a single view.
  • Multi-Agent System (MAS) enables coordinated, parallel execution across tools.

What does this look like in practice?

Torq’s AI SOC Agents, led by Socrates and bolstered by HyperAgents, don’t just suggest actions — they execute them within your guardrails. They interview users via Slack or Teams to validate suspicious activity. They investigate alerts across SIEM, EDR, IAM, cloud, and SaaS tools. They enrich, correlate, and summarize findings into a native case. They remediate threats automatically where policy allows. And they maintain an immutable, auditable trail of every step, so you can prove exactly what happened when the auditors come calling.

Real-World Results: What Torq Customers Achieved

The proof is in the numbers. Here’s what organizations are achieving with Torq:

  • Carvana: 100% of Tier 1 alerts automated with 41 runbooks deployed in just one month. No more alert backlog. No more analyst burnout from repetitive triage.
  • Valvoline: Their legacy SOAR couldn’t integrate their stack — a common story. With Torq, they save 6-7 analyst hours daily. ROI achieved within 48 hours of deployment.
  • Agoda: Phishing response fully automated 24/7. Incident reports that used to take 6-7 hours now generate in under 40 minutes.
  • HWG Sababa: MTTI/MTTR improved by 95% for medium- and low-priority cases. SOC productivity nearly doubled without adding headcount.

8 Questions to Ask When Evaluating Cybersecurity Automation Tools

Not all vendors will give you straight answers. These questions cut through the marketing:

  1. Does this tool automate a single function or the full incident lifecycle? Point solutions create integration headaches. End-to-end platforms reduce complexity.
  2. Can it integrate with our existing stack without months of custom work? Ask for specific integration timelines. Torq offers 300+ pre-built integrations.
  3. Does it use AI for reasoning and decision-making, or just static rules? There’s a massive difference between “AI-powered” marketing and actual adaptive automation.
  4. How quickly can we see measurable ROI? If the answer is “12-18 months,” you’re looking at a legacy approach.
  5. Can analysts at all skill levels use it, or does it require coding expertise? No-code workflows democratize automation. Script-heavy platforms create bottlenecks.
  6. What’s the maintenance burden? Ask specifically: when vendor APIs update, what breaks? How much engineering time does upkeep require?
  7. Does it provide full audit trails and explainability for compliance? “Black box” AI doesn’t fly with auditors. You need to show exactly how decisions were made.
  8. What do current customers say about real-world results? Ask for references in your industry. Generic case studies are marketing; peer conversations are truth.

It’s Time to Kill Your SOAR

Cybersecurity automation has evolved. Point tools that automate slices of your workflow aren’t enough anymore. Legacy SOAR that requires constant maintenance isn’t the answer.

The future is AI-powered Hyperautomation — platforms that reason, adapt, and act across your entire security stack.

Torq pioneered the AI SOC category for exactly this reason. 300+ integrations. Agentic AI that shows its work. 90-day ROI. Real results from organizations that made the shift.

Ready to automate your security operations?

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FAQs

What is cybersecurity automation?

Cybersecurity automation uses technology to execute security tasks — detection, investigation, response, and remediation — with minimal human intervention. It ranges from simple scripted tasks to sophisticated AI-powered platforms that can reason through complex incidents and take autonomous action within defined guardrails.

How do AI-powered security tools reduce alert fatigue?

AI-powered platforms like Torq’s AI SOC automatically triage, investigate, and resolve alerts without human intervention. Instead of analysts reviewing thousands of alerts manually, AI agents handle the investigation, correlate data across tools, and either resolve incidents automatically or escalate only the threats that truly require human judgment.

What's the difference between SOAR and Hyperautomation?

Legacy SOAR relies on static, pre-built playbooks that require constant maintenance and break when threats evolve or vendor APIs change. Hyperautomation uses agentic AI to dynamically reason through problems, adapt to new threat patterns, and orchestrate actions across your entire security stack without the maintenance burden.

How quickly can organizations see ROI from security automation?

With modern AI-powered platforms, ROI can be measured in days or weeks, not months. Valvoline achieved ROI within 48 hours of deploying Torq. Legacy SOAR implementations typically take 12-18 months to show value due to lengthy deployment timelines and high maintenance requirements.

What should I look for when evaluating cybersecurity automation tools?

Key evaluation criteria include: full incident lifecycle automation (not just single functions), seamless integration with your existing stack, true AI reasoning (not just static rules), fast time-to-value, no-code usability for all skill levels, low maintenance burden, full audit trails for compliance, and proven customer results in your industry.

How does security automation help with the cybersecurity talent shortage?

With a global workforce gap of 4.8 million positions, organizations can’t hire their way to security. Automation multiplies the effectiveness of existing teams by handling repetitive tasks, reducing alert fatigue, and enabling analysts to focus on complex threats that require human expertise. HWG Sababa nearly doubled SOC productivity without adding headcount.

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AI Hyperautomation Security Automation 101 Use Cases

How Security Orchestration Strengthens Ransomware Protection

By Torq
January 23, 2026
9 Minute Read
Ransomware Protection with Hyperautomation

Contents

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TL;DR

  • Ransomware encrypts in minutes, not hours. The median encryption time is 42 minutes; the fastest strains finish in under 4 minutes.
  • Manual response can’t keep pace. 30% of alerts are never addressed, and 83% of SOC analysts struggle with alert volume (IDC).
  • Orchestration closes the gap. Automated workflows can isolate endpoints, disable accounts, and segment networks in seconds, not hours.
  • Speed is the new metric. Mean Time to Contain (MTTC) matters more than detection scores alone.
  • Real results: Torq customers achieve up to 95% auto-remediation of Tier-1 cases and cut analyst workload by 7+ hours per day.

Ransomware doesn’t wait for your SOC to finish its morning coffee.

The moment an attacker gains access, the clock starts ticking. Research found that the entire attack chain, from initial access to encryption, now completes in under 30 minutes. Modern ransomware can encrypt nearly 100,000 files before most SOC teams even finish triaging the initial alert.

This timing gap is exactly what attackers exploit. And is exactly why the traditional approach to ransomware protection (prevention checklists, siloed tools, and manual investigation) fails when it matters most.

The enterprises winning the ransomware battle aren’t investing in better detection. They’re rethinking their entire response model through automated security orchestration — replacing reactive scrambling and swivel chairing with autonomous workflows that detect, contain, and remediate threats at machine speed. 

Hope isn’t a security strategy. Automation is.

What Is Ransomware Protection and Why Does Manual Response Fall Short?

Ransomware protection is a multilayered security discipline designed to prevent, detect, and respond to ransomware attacks before they encrypt critical data or disrupt operations. 

Effective protection spans: 

  • Email security
  • Endpoint detection
  • Network monitoring
  • Identity management
  • Backup verification
  • Incident response.

The issue? Most organizations treat these layers as separate silos. Your email security flags a suspicious attachment. Your EDR detects unusual file activity. Your SIEM correlates both events. 

But connecting those dots still requires a human analyst to investigate, pivot between tools, and manually execute containment steps. Meanwhile, the ransomware is spreading like wildfire.

Here’s the math that every SOC Director should be aware of: IDC previously reported that 30% of security alerts are never even addressed, while 83% of SOC analysts struggle with alert volume. Add a global cybersecurity workforce gap of 4.8 million professionals — a shortage that grew by 19% in just one year — and you have a perfect storm. Too many alerts, too few analysts, and attackers who move faster than manual processes can keep up.

The window between initial access and encryption is where ransomware attacks succeed or fail. Analysts context-switch between 20+ security tools, manually correlate data, decide on containment actions, and execute them one by one across multiple consoles.

Every minute of delay is a minute ransomware uses to spread laterally, escalate privileges, and encrypt more systems.

However, automation addresses this challenge by collapsing response time from hours to seconds. Automation platforms like Torq Hyperautomation™ connect your entire security stack — EDR, SIEM, identity, network, and backup tools — into unified workflows that execute containment actions the moment indicators are confirmed. 

No waiting. No ticket queues. No more “fingers crossed” that an analyst is available.

Preventing Ransomware Attacks With Automated Threat Detection

Prevention still matters. The best ransomware response is the one that never has to execute because the attack was stopped at the door. 

Effective ransomware prevention combines three core strategies:

  1. Automated email security, because phishing remains the primary delivery mechanism. Squish the phish.
  2. Behavioral analysis to catch threats that evade signature-based detection.
  3. Continuous vulnerability management to close the gaps that attackers exploit.

The keyword is automated. Prevention at enterprise scale requires continuous monitoring with real-time threat intelligence enrichment across your entire security stack, not periodic scans and manual reviews.

Torq Hyperautomation enables this by connecting prevention tools into workflows that share context automatically. When your email security solution detects a suspicious attachment, Torq Hyperautomation can instantly enrich that indicator with threat intelligence from tools like VirusTotal, Recorded Future, or GreyNoise — then correlate it with signals from your EDR and SIEM to determine if it’s part of a broader attack pattern. 

All before a human reviews the alert.

Email Phishing Defense and Behavioral Anomaly Detection

Phishing remains ransomware’s favorite front door. A malicious attachment slips past your email gateway. An employee clicks. And the race against encryption begins.

Automated workflows transform this scenario. Instead of relying on analysts to manually triage suspicious emails, Hyperautomation platforms analyze messages in seconds: extracting IOCs from attachments, detonating files in sandboxes, checking sender reputation, and comparing URLs against known malicious domains.

When indicators confirm a threat, automated containment triggers immediately — quarantining the email, removing it from other inboxes where it may have landed, and alerting the security team. The entire process completes before the employee finishes reading the first paragraph.

Torq Hyperautomation integrates with email security solutions like Abnormal Security and Proofpoint to build these workflows. Lennar, the national homebuilder, reduced phishing remediation from hours to minutes using Torq Hyperautomation for phishing response — freeing analysts to focus on threats that actually require human judgment. Behavioral anomaly detection adds another layer. 

Ransomware exhibits predictable patterns: 

  1. Rapid file enumeration
  2. Mass file modifications
  3. Shadow copy deletion
  4. Unusual encryption activity

EDR tools like CrowdStrike and Microsoft Defender detect these behaviors — but detection alone isn’t enough.

Torq Hyperautomation connects behavioral signals from multiple tools to correlate ransomware patterns across your environment. When your EDR detects suspicious encryption activity on one endpoint while your identity tool logs an unusual privilege escalation from the same user, Torq can automatically connect those dots and trigger containment, without waiting for an analyst to investigate.

Learn more about how Torq automates phishing investigation and response.

Stop Ransomware With Automated Response Workflows

Prevention will never be perfect. The question isn’t whether ransomware will breach your perimeter; it’s how fast you can stop it. 

This is where automated response workflows become the difference between a contained incident and a crisis.

SOC teams using platforms like Torq build automated workflows that execute the moment indicators are confirmed. The workflow looks something like this:

  1. Detection: Your SIEM or EDR identifies ransomware indicators, unusual file encryption, known malicious hashes, or behavioral patterns matching ransomware TTPs.
  2. Enrichment: Torq Hyperautomation automatically enriches the alert with threat intelligence, asset context, and user information. Is this endpoint critical? Is the user a privileged admin? Has this IOC been seen in other ransomware campaigns?
  3. Containment: Based on enrichment results, Torq executes containment actions across your stack — isolating the endpoint via CrowdStrike or Microsoft Defender, disabling the user account via Okta or Microsoft Entra, and triggering network segmentation via Zscaler or Palo Alto.
  4. Verification: Torq checks backup status via integrations with Veeam or other backup solutions, confirming recovery options before the situation escalates.
  5. Notification: Stakeholders receive instant alerts via tools like Slack or Microsoft Teams — complete with AI-generated case summaries that explain what happened and what actions were taken.

This entire sequence executes in seconds. 

Carvana demonstrated what this looks like at scale: Torq’s agentic AI now handles 100% of their Tier-1 security alerts and automated 41 different runbooks within just one month of deployment. A fundamental transformation of how their SOC operations work.

The orchestrated response model also enables continuous improvement. Every automated workflow generates data on response times, containment effectiveness, and false positive rates. 

SOC teams can refine playbooks based on real-world performance, progressively automating more scenarios as confidence grows.

For a deeper look at how automation transforms SOC operations, explore The Multi-Agent System: A New Era for SecOps.

Selecting a Ransomware Solution for Your SOC

Not all Hyperautomation platforms are created equal. When evaluating ransomware protection solutions, SOC Directors should look beyond detection scores and focus on three critical capabilities:

  1. Integration depth: Your ransomware response workflow is only as strong as its weakest integration. Can the platform connect to your EDR, SIEM, identity provider, network tools, and backup solutions? Torq offers 300+ pre-built integrations with 4,000+ pre-built steps — and AI-powered tools to build custom integrations when needed.
  2. Workflow flexibility: Ransomware attacks don’t follow scripts. Your response workflows shouldn’t be limited by rigid, pre-built playbooks. Look for platforms that support no-code, low-code, and full-code workflow building — so your team can start with templates and customize based on your environment.
  3. Autonomous remediation: Detection without response is just expensive alerting. The platform should enable true autonomous remediation — executing containment actions without requiring human approval for well-understood threats. Torq customers like BigID report that “what would normally require 10 security engineers just needs one or two with Torq.”

Key metrics to track:

  • Mean Time to Contain (MTTC): How fast can you isolate a compromised endpoint? Automated workflows should reduce this from hours to seconds.
  • Automation rate: What percentage of Tier-1 alerts are handled without human intervention? Torq customers achieve up to 95% auto-remediation of Tier-1 cases.
  • Analyst time saved: Valvoline cut analyst workload by 7 hours per day after implementing Torq. Time that now goes toward threat hunting and security improvement instead of repetitive triage.

Legacy SOAR platforms promised automation but delivered something completely different. Hyperautomation platforms like Torq represent the next evolution, combining AI-powered workflows, agentic reasoning, and deep integrations to enable truly autonomous SOC operations. It’s important to understand why SOAR is dead and what comes next.

Stop Ransomware Before It Stops You

The enterprises successfully defending against ransomware aren’t relying on prevention checklists and manual runbooks. They’re deploying Hyperautomation that detects threats in real time, enriches alerts with contextual intelligence, and executes containment workflows at machine speed.

Torq Hyperautomation and Torq HyperSOC™ give SOC teams the tools to build an autonomous ransomware response — connecting every security tool into unified workflows that stop attacks before encryption completes.

Ready to transform your ransomware protection from reactive to autonomous?

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FAQs

What is ransomware protection?

Ransomware protection is a multilayered security discipline that prevents, detects, and responds to ransomware attacks before they encrypt critical data or disrupt operations. Effective protection spans email security, endpoint detection and response (EDR), identity management, network monitoring, backup verification, and automated incident response workflows.

What is the best protection against ransomware?

The best ransomware protection combines prevention (email security, patching, MFA) with automated response capabilities. Since ransomware can encrypt systems in under 42 minutes, organizations need security automation platforms that can detect, contain, and remediate threats in seconds.

Which tools can be used to detect ransomware?

Ransomware detection typically involves EDR solutions (CrowdStrike, Microsoft Defender, Carbon Black), SIEM platforms (Splunk, Microsoft Sentinel), email security tools (Abnormal Security, Proofpoint, Mimecast), and threat intelligence feeds (VirusTotal, Recorded Future). However, detection alone isn’t enough, security automation platforms like Torq connect these tools into automated workflows that respond to threats at machine speed.

What software can prevent ransomware?

Ransomware prevention software includes email security gateways, endpoint protection platforms, vulnerability management tools, and identity security solutions. However, since no prevention is 100% effective, organizations also need Hyperautomation that can execute rapid containment when ransomware is detected, isolating endpoints, disabling compromised accounts, and segmenting networks within seconds.

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Todd Willoughby, Director

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

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AI SOC Use Cases

Cases Dashboards: Real-Time SOC Visibility in Torq 

By Roman Kunicher
January 22, 2026
9 Minute Read
SOC dashboards showing real-time case metrics, SLA tracking, and operational trends in Torq HyperSOC.

Contents

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Roman Kunicher is a Product Manager at Torq focused on HyperSOC case operations and SOC visibility. With 10+ years in cybersecurity and a hands-on technical background, Roman has spent his career partnering with R&D, Sales, customer teams, and partners to translate real SOC needs into practical outcomes. Before Product, he served as a Security Solution Architect and Product Specialist at Torq, bridging field reality and product execution.

Security teams spend too much time turning case data into decisions that other people can act on.

The data exists, but it’s rarely organized into a continuous, shared view of cross-case operations: one place that surfaces what’s driving pressure (e.g., open case backlog, SLA risk, critical spikes), how performance is trending over time, and where the SOC should focus next, so each role can work from the same up-to-date picture, tailored to what they need.

The Challenge: Staying Aligned as Things Change

The hard part isn’t finding a metric — it’s maintaining shared, situational awareness that stays useful as the SOC changes. Different personas need different answers, and the “right” view shifts daily: a case backlog spike, an SLA risk trend, a new noisy source, or a sudden concentration of critical work. 

When the view isn’t easy to tailor and reuse, teams end up re-answering the same questions with ad-hoc slices of case data. Torq Cases Dashboards are designed to make those answers continuously available instead of not a one-off exercise.

The questions are familiar:

  • What should we focus on right now — and what’s changing?
  • Where are we falling behind (SLA risk, triage bottlenecks, unassigned work)?
  • Are we getting more effective over time (MTTR, MTTA, throughput, SLA trends)?
  • How are AI and automation impacting my cases?
  • Where should we improve next (process, automation, AI)?

What Teams Actually Need

Impaired situational awareness creates a few practical problems:

  • Patterns show up late. Backlogs, SLA risk, duplicate spikes, or noisy detections become visible only after they’re already painful.
  • Operational decisions get harder. Workload balancing, escalation priorities, and coaching become guesswork when the data is fragmented.
  • Sharing insights is slow. The same questions get answered repeatedly for different audiences, and each answer requires another round of manual stitching.

The cost isn’t just time. It’s slower decisions, uneven execution, and fewer cycles spent improving triage, detections, and automation.

SecOps practitioners need a real-time operational dashboard for case data — one that shows trends across cases (and, when relevant, across workspaces), and that lets you transition quickly from “something’s changed,” to “these are exact cases that explain it.”

Meet Torq Cases Dashboards

Cases Dashboards make it easy to build and customize real-time views of SOC posture and case operations across workspaces, so teams can track trends, drill into the cases behind every metric, and share insights and outcomes with stakeholders.

Track trends, explore the cases behind every metric, and share outcomes with stakeholders.

They’re built for the way SOCs actually work inside Torq HyperSOCTM: fast pivots, dynamic prioritization, and translating operational data into decisions. All without adding another reporting ritual.

Cases Dashboards are designed to sit at the center of day-to-day SOC operations, addressing the unique needs of different users:

  • Leaders use dashboards to understand posture, performance, and risk exposure at a glance.
  • SOC managers track throughput, workload distribution, and SLA health.
  • Analysts use dashboards as an investigation starting point, moving from patterns to the exact cases driving them.

This is not reporting for reporting’s sake. No one has time for that. Instead, this is up-to-date operational visibility that directly informs action.

Key Capabilities and Benefits of Cases Dashboards

Build Dashboards That Answer Your Questions — Fast

Cases Dashboards are built for customization without ceremony. You can take a question you care about (SLA risk, MTTR/MTTI/MTTT, workload balance, a noisy source, a spike in criticals), turn it into a visual view across cases, and adjust it as the SOC changes. 

Instead of digging through lists, you build a dashboard that makes the signal obvious: what’s trending, what’s stuck, and what needs attention. 

Create a custom dashboard widget that tracks cases exceeding SLA, organized by source

The same dashboard can support “right now” operations and longer-term analysis. Track case volume and severity mix, SLA compliance, throughput, and performance over time — then zoom in when something starts drifting.

This is where dashboards stop being “status” and become operational awareness: you spot the change early, before it becomes a fire drill.

Torq Cases Dashboard showing trend widgets
Track case volume, severity mix, SLA compliance, and throughput in real time, then zoom in when something starts drifting.

Move from a Metric to the Cases Behind It

When a number looks off, you shouldn’t have to guess why. Cases Dashboards let you jump directly from a widget into the underlying cases that produced it: investigation and process follow-up are one click away. That’s what turns dashboards into a working tool: a spike isn’t just a spike — it’s a set of cases you can inspect and act upon.

Click any widget to see the cases behind the numbers — investigate and act without leaving the dashboard.

Start with the SOC Posture Template (Then Tailor It)

The SOC Posture Template gives you a head start on day one. Reuse it as is, or tailor versions for specific audiences, such as leadership, SecOps, a particular workspace, or a report for a business unit. You keep the common language, but each audience gets the view that fits their unique needs.

Tailor versions for leadership, SecOps, or specific business units.

Share the Story with Stakeholders

Dashboards are meant to be shared. When it’s time to update leadership, customers, or auditors, you can share a consistent view and point back to the same operational truth the SOC uses day to day. This means faster updates, with less friction and more alignment to the same data.

Cases Dashboards Customer Benefits

At its most basic distillation, Cases Dashboards deliver three practical outcomes:

  1. Less manual reporting work: Fewer exports, fewer screenshots, fewer “can you pull this number?” requests
  2. Faster operational decisions: Trends and risk are visible early which means quicker, better-informed decisions
  3. Clearer communication: A consistent view you can share internally or externally

How SOC Teams Use Cases Dashboards

Turn Cross-Case Data into Repeatable Answers with Widget Builder

The Widget Builder is where dashboards become specific to your SOC. You choose what you want to measure, how to break it down, and how to visualize it, so the same questions don’t have to be re-solved every week. You may even want to track the number of cases handled by AI or automation. The flexibility is yours.

  • Case count shows how many cases match your filters and groupings, so you can track volume, mix, and distribution across your case data.
  • Case events show what changed during a case lifecycle, so you can measure escalations, on-hold movements, and other transitions as they happened and assess your SOC health — not just what cases look like right now.
  • SLA timers show time-based performance using standard or custom SLAs. You can summarize performance using averages, medians, or long-tail-safe metrics like P90, then break it down by any dimension to understand where time is being spent.

You can group by one or more dimensions and choose the right visualization to see trends and breakdowns, for example, by SLA, category, assignee, tags, business unit, or any custom attribute. 

The following video shows how easy it is to create a dashboard widget that tracks the number of cases closed by our AI SOC Analyst, Socrates, over the last month, and categorizes them by resolution type (True Positive: Benign, Malicious, etc).

Create a widget that tracks cases closed by Socrates over the last month, categorized by resolution type

Operate Across Customers with Omni-View

For MSSPs and MDRs, the challenge is staying consistent across many customers without losing separation and control.

Omni-View lets you monitor posture and performance across workspaces in a single, convenient location, with cross-customer controls to keep visibility and access scoped appropriately. You can keep a reusable, board-ready view across tenants, then pivot to a specific customer when needed and tailor dashboards per customer.

One view across all your customers, with the controls to keep them separate.
One view across all your customers, with the controls to keep them separate.

Filter Live Dashboards and Drill into What Matters

In security operations, the goal is focus. Teams filter dashboards to the scope they care about — a team, a workspace, a case type, a severity band — and immediately see what’s changing.

When something looks particularly interesting, drill down from the metric to the underlying cases to take action. This keeps dashboards lightweight but actionable: spot the risk, click into the work, and move.

Filter dashboards by team, workspace, case type, or severity — then click any metric to drill into the underlying cases and take action.

Keep Dedicated Views for Each Audience

Teams can create dedicated dashboards for different outcomes — SOC Posture, Efficiency Report, SOC Operations, Compliance Report, or Executive Summary — each tuned to the audience and the decision it supports, and easy to share or export as a fixed snapshot when needed.

Instead of a single dashboard trying to serve everyone, senior leaders get a clear, board-friendly view, while the SOC focuses on operational details, all backed by the same live case data.

Get Started with Cases Dashboards

Cases Dashboards turn Torq HyperSOC case data into tailored, real-time operational visibility, which helps SOC teams track trends, understand posture, accelerate investigations, and communicate more clearly with stakeholders.

Torq is transforming SecOps for enterprises like Carvana, Valvoline, Virgin Atlantic, and PepsiCo. See how agentic AI and Hyperautomation can do the same for your team.

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

Todd Willoughby, Director

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

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AI SOC Platforms for Financial Services: What You Need in 2026

By Torq
January 22, 2026
9 Minute Read
AI SOC Platforms for Financial Services

Contents

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TL;DR

  • Financial institutions face SOC challenges that no generic platform is built for — overlapping regulatory frameworks (SOX, PCI DSS, GLBA), real-time speed requirements, and audit trails that satisfy examiners, not just security teams.
  • Attackers move faster than manual SOCs can respond: phishing breaches succeed in under 60 minutes, while the average SOC investigation takes 70, making AI-driven automation a risk management necessity, not a nice-to-have.
  • Financial institutions running AI SOC platforms are seeing dramatic results in production: MTTR reduced from one day to 14 minutes, MTTI cut from hours to minutes, 90%+ of alerts investigated and remediated automatically, and weeks of manual audit preparation reduced to hours.
  • The financial institutions that win won’t have the largest SOC headcount — they’ll be the ones operating at machine speed while satisfying every auditor and regulator in the room.

The time between compromise and data exfiltration now occurs before most SOCs finish their first triage. Phishing breaches succeed in under 60 minutes. The average SOC investigation takes 70. This is why financial institutions are operating at a structural disadvantage.

Financial services sit at the center of the global economy. A breach triggers regulatory scrutiny, reputational damage, and potential systemic risk. All at once. 

And yet, fewer than 25% of SOCs have fully automated their processes. Most organizations still rely heavily on manual intervention. The average enterprise ingests data from 83 security tools across 29 vendors. In 75% of breaches, the logging existed to catch the threat, but signals were still buried.

The answer isn’t more seats in chairs. It’s AI-driven SOC platforms that operate at machine speed, with the compliance controls and audit trails financial regulators actually demand.

What Makes Financial Services SOC Challenges Different?

Not all SOC challenges are created equal. Financial institutions face tremendous pressures that legacy cybersecurity platforms aren’t built to handle. Here are five reasons why financial institutions’ SOCs are different. 

1. The Compliance Stack is Unlike Any Other Industry

Financial institutions operate under overlapping frameworks simultaneously: SOX, PCI DSS, GLBA, OCC guidance, SEC requirements, and a patchwork of state regulations. Every automated action needs documentation that satisfies multiple auditors, often with different evidentiary standards. A single incident can touch four different compliance frameworks at once.

2. Speed is a Security Requirement

Trading operations, fraud detection, and payment systems demand real-time response. A 70-minute investigation window isn’t just slow, it’s negligent when attackers move in minutes. The window between credential compromise and lateral movement is shrinking every quarter.

3. Regulators Demand the Full Decision Trail

Financial regulators don’t just want to know what happened. They want to see the decision trail. Who authorized it? What data informed it? Why did the system respond the way it did? Black-box AI isn’t an option in this environment. Explainability it’s a requirement.

4. Financial Infrastructure Requires Deep, Specific Integrations

Trading systems, core banking platforms, fraud detection engines, SWIFT, payment rails — financial institutions have integration requirements that go far beyond what a generic SOC platform anticipates. If your AI SOC can’t talk to your financial infrastructure, it’s operating blind on the most critical attack surfaces.

5. The Talent Shortage is More Acute in Financial Services

The cybersecurity talent shortage hits financial services harder because of specialized compliance knowledge requirements. Finding an analyst who understands both EDR and OCC examination requirements? That’s a unicorn. 

4 Features Financial Institutions Need from an AI SOC Platform

When evaluating AI SOC automation platforms for financial services, the requirements go well beyond what a standard enterprise checklist covers. Here’s what actually matters.

1. Explainable AI with Complete Audit Trails

Regulators and auditors need to understand how decisions were made,not just what was decided. Every automated action must be traceable: what triggered it, what data informed it, who (or what) authorized it, and what the outcome was. Immutable logs that satisfy SOX, PCI DSS, and OCC examination requirements aren’t optional. They’re the price of admission.

If a vendor can’t show you exactly how their AI arrived at a containment decision, that’s a problem — not just for security, but for your next regulatory examination.

2. Machine-Speed Detection and Response

Financial institutions need sub-minute responses for credential compromise, fraud indicators, and lateral movement. Autonomous containment for high-confidence threats isn’t about removing humans from the loop — it’s about not letting attackers operate unchallenged while humans catch up.

3. Deep Integration with Financial Systems

Core banking platforms, trading systems, fraud detection, identity systems — these are your highest-risk attack surfaces. Privileged access is a primary attack vector across financial institutions. Your AI SOC needs to see and act across all of it, including your SIEM, EDR, cloud infrastructure, and case management systems.

4. Human-in-the-Loop Controls

Full autonomy may may not be appropriate for every action in your SOC, especially in a financial services firm. Configurable guardrails for high-impact decisions, clear escalation paths that align with internal policies, and unambiguous accountability for automated decisions — these are the mechanisms that keep regulators satisfied and analysts empowered rather than sidelined. The best AI SOC platforms make human oversight a design principle, not an afterthought.

What Happens When Financial Services SOCs Don’t Automate?

There’s a temptation to frame SOC automation as a cost center decision. It isn’t. It’s a risk-management decision — and the math is unforgiving.

The Speed Gap is the Breach Gap

When attackers move in minutes, and your SOC responds in hours, every minute of delay is an attacker’s opportunity. Manual triage, manual enrichment, manual escalation — each step is a window that stays open longer than it should.

Analyst Burnout is a Security Risk 

Financial services SOCs face the same alert fatigue as everyone else, compounded by compliance documentation burden. According to the SANS 2024 SOC Survey, security teams are overwhelmed, understaffed, and stuck in reactive mode despite significant technology investments. When experienced analysts burn out and leave, they take institutional knowledge with them. Tribal knowledge loss — understanding which alerts matter in your specific environment — is expensive and dangerous to rebuild.

Manual Processes Create Audit Exposure

Inconsistency is the enemy of compliance. Manual processes are inconsistent by definition. Inconsistency creates audit findings. Findings create remediation costs and regulatory attention. Automation creates consistency at scale. 

The numbers from organizations already running AI SOC platforms are stark. IDC validated that Torq enables SOC teams to cut investigation time by up to 90% and handle 3–5x more cases without adding headcount. 

The economics of an agentic SOC are straightforward: Hyperautomation absorbs Tier-1 and Tier-2 work so teams handle significantly more alerts with the same headcount, and audit-ready logs eliminate weeks of manual compliance prep every year.

And the alternative — adding that extra analyst you don’t need — runs directly into a global cybersecurity talent shortage of 4.8 million unfilled positions, according to the ISC2 2024 Cybersecurity Workforce Study. You can’t hire your way to machine speed. 

6 Questions to Ask When Evaluating AI SOC Platforms for Financial Services

Use this checklist when you’re in active evaluation. These are the questions that separate platforms built for financial services complexity from those that aren’t.

  1. Does it provide complete, immutable audit trails? Regulators need to see how every automated decision was made. If the vendor can’t demonstrate this in a live environment, walk away.
  2. What are the time savings at each stage of the complete threat lifecycle? Meant time to Assignment, Mean time to Investigation, Mean time to Response? Incremental improvements at each stage make for not only a faster, but much more efficient incident response strategy.
  3. How are human-in-the-loop controls configured? Full autonomy isn’t always appropriate for every action. Understand the guardrail options and who controls them.
  4. What’s the implementation timeline? Months-long implementations create risk. Look for time-to-value measured in weeks.
  5. How does it handle false positives? Financial services can’t afford to block legitimate transactions. Understand the accuracy metrics and how the platform learns from corrections.
  6. Can you speak with financial services references? Ask for peer conversations with institutions of similar size and regulatory complexity.

What Leading Financial Institutions Are Achieving with Torq

Financial institutions are running Torq in production today — with measurable outcomes that satisfy both security teams and regulators.

Top 30 U.S. Bank: Automated Fraud Detection Got Zelle Back Online: Before reinstating Zelle payment service — which had been suspended due to fraud — the bank needed to demonstrate it could detect and contain fraud at scale. Torq automated end-to-end fraud detection alerts to account lockdown, reducing mean time to investigate (MTTI) from hours to minutes. The bank reinstated the service with a fully automated, auditable response capability and unified its security stack with Torq, reducing IAM tasks from a full day to three minutes.

The team achieved 30% time savings with the vast majority of threat alerts automatically identified, analyzed, and remediated — freeing analysts to focus on higher-value security initiatives.

The throughout numbers tell the same story: 100,000+ events processed in seconds. MTTR improvements from days to minutes. Audit preparation reduced from weeks to hours. These are outcomes your team deserves.

Where AI SOC is Headed for Financial Services

The trajectory is clear, and financial institutions that understand it will have a significant advantage.

Cross-functional automation is breaking down the silos that attackers exploit. Security, fraud, compliance, and risk teams operating on shared AI infrastructure — sharing signals, sharing context, sharing response capabilities. Financial institutions that coordinate across these functions detect and contain threats faster than those that keep them separate.

Regulatory evolution will accelerate. Expect regulators to start requiring AI-driven security capabilities as baseline expectations, not differentiators. OCC and SEC guidance are already moving in this direction. Financial institutions that build AI SOC capability now are positioning ahead of mandates, not scrambling to meet them.

Secure AI by design is becoming a SOC responsibility. The threat landscape has shifted. AI is giving adversaries the ability to industrialize attacks — scaling phishing campaigns, compressing dwell times, and probing defenses faster than human analysts can respond. For financial institutions, the strategic imperative is clear: the SOC must evolve to meet the threat. You can’t defend what you don’t understand.

Torq’s multi-agent systems and agentic AI capabilities aren’t roadmap items. They’re in production.

The AI SOC Advantage for Financial Institutions 

The financial institutions that thrive won’t have the largest SOC headcount. They’ll be the ones that figured out how to operate at machine speed while satisfying every auditor and regulator in the room.

Financial services face unique SOC challenges: regulatory complexity, speed requirements, audit intensity, and integration demands that generic AI SOC platforms weren’t built to address. The platforms that serve financial institutions well are explainable, auditable, fast, and built for compliance from the ground up.

The regulatory direction is clear. The talent math is clear. The question isn’t whether financial institutions need AI SOC capabilities. It’s whether they build them before or after the next incident that demands it.

Ready to see how Torq is built for financial services complexity?

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FAQs

What is an AI SOC platform, and why do financial institutions need one?

An AI SOC platform is a security operations solution that uses agentic AI and automation to detect, investigate, and respond to threats — replacing slow, manual processes with machine-speed decision-making. Financial institutions need one because they face a unique combination of pressures: overlapping regulatory frameworks like SOX, PCI DSS, and GLBA; real-time speed requirements across trading and payment systems; and audit intensity that demands a complete, explainable decision trail for every automated action. Generic security tools weren’t built for this level of complexity.

How does an AI SOC platform help with financial services compliance?

Financial institutions should prioritize five things: explainable AI with complete, immutable audit trails; machine-speed detection and response measured in seconds, not minutes; deep integrations with financial systems, including core banking platforms, fraud detection, and identity systems; configurable human-in-the-loop controls for high-impact actions; and financial services-specific references. Always request a live demonstration of audit trail capabilities before making a decision.

What results are financial institutions achieving with AI SOC platforms?

Financial institutions running AI SOC platforms in production are seeing measurable outcomes across speed, scale, and compliance. One institution reduced MTTR from one day to 14 minutes. A major regional U.S. bank automated end-to-end fraud alert detection and account lockdown — cutting mean time to investigate (MTTI) from hours to minutes and enabling the reinstatement of Zelle payment services. A global money transfer platform reduced IAM investigation time from a full day to three minutes, with more than 90% of alerts investigated and remediated automatically. Across the board, audit preparation that previously took weeks is now completed in hours.

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

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Phillip Tarrant, SOC Technical Manager

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AI AI SOC Hyperautomation News & Events Research

A New Era of Asymmetric Warfare: The Case for the Agentic SOC

By Ofer Smadari, CEO & Co-Founder
January 21, 2026
4 Minute Read
Asymmetric warfare in cybersecurity showing AI-powered attacks versus autonomous SOC defense

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For the last decade, the cybersecurity industry has attempted to solve a technology problem with a human solution. We looked at the rising tide of alerts and the complexity of the threat landscape, and our answer was always “hire more people.” That approach has created a dangerous asymmetric warfare dynamic — one where attackers scale infinitely while defenders stay stuck in manual mode.

We recruited brilliant analysts and placed them in SOCs where we essentially forced them to act like robots. We asked them to stare at dashboards, copy-paste data between tools, run repetitive scripts, and manually close tickets. 

It didn’t work. It led to burnout, turnover, and missed threats. And as of this week, that strategy is not just failing, it is officially obsolete. 

You cannot fight machine speed with human speed.

Check Point Research recently published its findings on VoidLink, and it serves as a grim milestone for our industry.

We’ve seen AI-generated scripts before. We’ve seen attackers use LLMs to write better phishing emails. But VoidLink is different. This is one of the first known instances where AI was used to architect, build, and deploy an entire advanced malware framework — complete with rootkits, implants, and modular plugins.

The most terrifying metric from the research isn’t technical; it’s temporal. The researchers found that AI enabled a single actor to condense what used to be months of nation-state-level development into mere days.

The Economics of Cybercrime Have Flipped

This is a turning point. The barrier to entry for sophisticated, high-velocity attacks has collapsed.

In the past, building a complex malware framework required a well-funded team, significant time, and deep expertise. Today, the investment required to build sophisticated threats is dropping near zero.

When the cost of attack creates a floor of near-zero, the volume of attacks will naturally hit a ceiling of infinity. The incentive for attackers has never been higher because the risk and resource requirements have never been lower.

The Asymmetrical Warfare Gap

This creates a velocity gap that human teams can no longer bridge. We are now facing an asymmetry canyon:

  • The attackers are using AI to code, adapt, and scale attacks at machine speed.
  • The defenders are largely still waiting for a human analyst to wake up, read an alert, interpret the context, and manually run a playbook.

You can’t fight AI speed with human speed. If you try, you will lose every time. The “1-10-60” rule (1 minute to detect, 10 to investigate, 60 to remediate) is dead. In the age of VoidLink, 60 minutes is an eternity.

Enter the Agentic SOC

This reality is exactly why Torq raised our $140M Series D. We recognized that better automation wasn’t the answer. Automation is linear Iteration that follows a script. But AI-driven threats are dynamic. They don’t follow scripts.

We’re building the agentic SOC.

We’re moving the industry away from static, simple playbooks and toward autonomous AI Agents. These agents don’t just follow if/then logic. They possess the reasoning capabilities to investigate alerts, understand context, make decisions, and execute complex remediation autonomously.

We’re building a defense architecture where machines fight machines, freeing our human defenders to do what they do best: strategy, threat hunting, and high-level decision-making.

Machine-vs-Machine Defense: The Only Way to Win Asymmetric Warfare

The era of the Tier 1 analyst as a data-fetcher is over. We have to stop fighting the future with the past. The only way to survive asymmetric warfare in the VoidLink era is to fight fire with fire — or, more accurately, to counter autonomous threats with autonomous defense.

VoidLink is just the first wave of this new reality. And at Torq, we’re just getting started.

Asymmetric warfare demands an asymmetric response. The human-speed SOC can’t win against machine-speed threats — but the agentic SOC can. See how Torq is rewriting the rules of security operations.

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

Todd Willoughby, Director

Compuquip logo in white

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

Phillip Tarrant, SOC Technical Manager

Fiverr logo in black

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

Gai Hanochi, VP Business Technologies

Carvana logo in black

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

Dina Mathers, CISO

Riskified logo in white

“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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