AI AI SOC

The AI SOC Org Chart for 2026 and Beyond

By John White, Field CISO

February 12, 2026

9 Minute Read

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John White is the Field CISO for EMEA at Torq. A respected security executive with more than 20 years of leadership experience, John previously served as CISO at Virgin Atlantic, where he led a multi-year transformation deploying the Torq AI SOC Platform to modernize cyber operations. Prior to 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:

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.
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.
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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Todd Willoughby, Director

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Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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Dina Mathers, CISO

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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

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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.

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

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AI SOC Hyperautomation

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

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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

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI AI SOC Security Automation 101

Top Cybersecurity Automation Tools for 2026

By Torq

January 27, 2026

9 Minute Read

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

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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:

Does this tool automate a single function or the full incident lifecycle? Point solutions create integration headaches. End-to-end platforms reduce complexity.
Can it integrate with our existing stack without months of custom work? Ask for specific integration timelines. Torq offers 300+ pre-built integrations.
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.
How quickly can we see measurable ROI? If the answer is “12-18 months,” you’re looking at a legacy approach.
Can analysts at all skill levels use it, or does it require coding expertise? No-code workflows democratize automation. Script-heavy platforms create bottlenecks.
What’s the maintenance burden? Ask specifically: when vendor APIs update, what breaks? How much engineering time does upkeep require?
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.
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?

Get the Kill Your SOAR Migration Guide

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.

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI Hyperautomation Security Automation 101 Use Cases

How Security Orchestration Strengthens Ransomware Protection

By Torq

January 23, 2026

9 Minute Read

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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:

Automated email security, because phishing remains the primary delivery mechanism. Squish the phish.
Behavioral analysis to catch threats that evade signature-based detection.
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:

Rapid file enumeration
Mass file modifications
Shadow copy deletion
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:

Detection: Your SIEM or EDR identifies ransomware indicators, unusual file encryption, known malicious hashes, or behavioral patterns matching ransomware TTPs.
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?
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.
Verification: Torq checks backup status via integrations with Veeam or other backup solutions, confirming recovery options before the situation escalates.
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:

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.
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.
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?

Don’t Die Get Torq

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.

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI SOC Use Cases

Cases Dashboards: Real-Time SOC Visibility in Torq

By Roman Kunicher

January 22, 2026

9 Minute Read

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

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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 HyperSOC^TM: 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

See Trends and Posture in Real Time (and Over Time)

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.

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:

Less manual reporting work: Fewer exports, fewer screenshots, fewer “can you pull this number?” requests
Faster operational decisions: Trends and risk are visible early which means quicker, better-informed decisions
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.

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.

Get a Demo

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI 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

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

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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.

The VoidLink Wake-Up Call

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.

Read Our Manifesto

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI AI SOC Hyperautomation Security Automation 101

10 AI SOC Benefits That Actually Transform Security Operations

By Torq

January 20, 2026

11 Minute Read

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

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Your SOC is drowning. Industry estimates suggest that up to 60% of SOC analyst time is spent on Tier 1 triage, leaving less time for addressing real threats. According to Splunk’s State of Security 2025 report, 59% of security teams report being overwhelmed by too many alerts, and 55% waste precious hours chasing false positives. Analysts are burning out — 52% are considering leaving the field entirely due to stress.

Here’s the uncomfortable truth: legacy SOAR was supposed to fix this… but it didn’t. Instead, security teams got brittle playbooks, endless integration headaches, and automation that breaks every time the threat landscape shifts.

A true AI-driven SOC is fundamentally different. We’re not talking about slapping a chatbot on your existing tools or adding ML to triage. We’re talking about agentic insights, action, and automation which spans the entire incident lifecycle, from triage through remediation, that suppresses noise, prioritizes actual threats, and works alongside your staff.

Here are the 10 AI SOC benefits driving that transformation.

What is an AI SOC?

Traditional SOCs run on manual labor. Analysts triage alerts one by one, pivot between disconnected consoles to gather context, and execute remediation scripts by hand. It’s slow, tedious, and doesn’t scale.

In an AI SOC, agentic AI and automation act as connective tissue across your entire security stack — autonomously ingesting alerts, investigating across tools, making decisions based on logic and continuous learning, and executing remediation at machine speed. Human analysts apply their judgment and expertise to prioritized threats, while also providing oversight to their agentic counterparts. Your team spends their time on work with higher-value impact, instead of repetitive ditch-digging.

Top 10 AI SOC Benefits

1. Faster Threat Detection

Hackers use automation. If your defense relies on a human reading a ticket, you have already lost.

AI processes telemetry in milliseconds. One of the primary AI SOC benefits is the ability to detect a behavioral anomaly (like an impossible travel login combined with a massive data download) and trigger an alert instantly, drastically reducing Mean Time to Detect (MTTD).

Torq’s AI SOC Analyst, Socrates, handles the full case lifecycle autonomously. It doesn’t just tell you something looks suspicious — it investigates, gathers evidence, takes containment actions, and documents everything. By day 90 of a Torq implementation, customers typically see 90% of Tier-1 alerts resolved end-to-end without human intervention.

2. Reduced Alert Fatigue

The average SOC analyst is bombarded with thousands of alerts daily. This leads to burnout and decision fatigue, where real threats are ignored because they look like false positives.

The old approach was to tune your SIEM to suppress alerts and hope you don’t suppress the wrong ones. The AI SOC approach is smarter. Intelligent suppression reduces noise while retaining full evidence trails. When Torq suppresses an alert, it’s not deleting information; it’s clearing false positives, making informed decisions based on context and keeping the receipts in case you need them later.

AI acts as the ultimate filter. It autonomously triages low-fidelity alerts, correlates them, and closes the noise. It only wakes a human up for high-confidence, verified threats.

3. Machine-Speed Detection and Response

Here’s a number that should terrify you: the average legacy SOAR investigation takes hours. Sometimes days. Meanwhile, attackers move in minutes.

AI SOC benefits include collapsing that timeline dramatically. Torq’s multi-agent system deploys specialized AI Agents working in parallel — one analyzing network traffic, another checking identity logs, another correlating threat intelligence — all simultaneously. What used to take an analyst hours of manual pivoting happens in seconds.

Customers routinely achieve 60%+ MTTR reduction. One financial services organization went from day-long IAM investigations to three-minute resolutions. Not because they hired more analysts, but because AI handles the grunt work at machine speed.

4. Continuous Learning That Adapts

Static playbooks are the Achilles’ heel of legacy SOAR. You spend months building them, and they work… until the threat landscape shifts. Then you’re back to square one, manually updating brittle logic while attackers exploit the gaps.

True AI SOC platforms utilize adaptive reasoning rather than rigid rules. Torq learns from analyst feedback continuously. When an analyst corrects a decision or adds context to a case, that knowledge improves future automation.

This continuous learning means your SOC continuously improves. The AI evolves with threats automatically, adapting to new attack patterns without requiring your team to anticipate every possible scenario in advance.

5. Consistent Correlation Across Data Sources

78% of organizations are fighting with dispersed, disconnected tools. Every investigation requires manual pivoting between a dozen consoles. Critical context lives in silos that don’t talk to each other.

This fragmentation can be dangerous. Attackers exploit gaps between tools. A threat that appears benign in your SIEM may become obviously malicious when correlated with EDR telemetry, identity logs, and cloud activity.

AI SOC platforms excel at data fusion. Torq connects to 300+ tools out of the box — SIEM, EDR, cloud platforms, identity providers, ITSM, threat intelligence feeds — and correlates signals across all of them simultaneously.

Our multi-agent system doesn’t just aggregate data, it synthesizes insights. Disparate signals become coherent threat narratives. Analysts see the full picture, not fragments they have to piece together manually. Organizations with unified platforms achieve 59% faster incident response. When AI sees your entire environment at once, it catches what fragmented analysis misses.

6. Empowering Human Analysts

AI isn’t coming for your analysts’ jobs. What AI should do is handle the repetitive work that’s driving your best people out of the industry. Remember that 52% considering leaving? They’re not burned out from threat hunting. They’re burned out from clicking through the same alert types hundreds of times a day.

AI SOC benefits include genuine analyst empowerment through three key capabilities:

Orchestration coordinates actions across your entire tool stack automatically. No more manual pivoting between consoles or copy-pasting IOCs from one system to another.
Enrichment adds critical context to every alert before an analyst sees it. Threat intelligence, asset information, user history, related incidents — all surfaced automatically.
Guided response provides recommended actions based on similar past incidents and best practices. Analysts make decisions faster because they don’t have to start from scratch every time.

Valvoline‘s team saves six to seven hours per analyst each day with Torq. That time goes to threat hunting, detection engineering, and complex investigations that actually require human judgment.

The result isn’t fewer analysts. It’s analysts doing work that matters.

7. Proactive Threat Hunting

Traditional SOCs are reactive, waiting for the bell to ring. By the time you’re responding to alerts, attackers have already achieved initial access — quite likely more. The best SOCs don’t just respond to threats; they hunt them before alerts ever fire.

AI SOC platforms enable proactive threat hunting through predictive analytics. GenAI identifies patterns that precede known attack chains, flagging suspicious activity before it escalates into full-blown incidents.

Torq’s continuous learning means these predictive capabilities improve over time. The system learns what “normal” looks like in your environment, making deviations visible before attackers achieve their objectives.

8. Faster Root Cause and Impact Analysis

When an incident hits, seconds count: what’s happening, what’s the severity, and how do we contain it. These questions are soon followed by: how did this happen, how do we prevent it from happening again, and how do we recover?

With traditional investigation, analysts dig through logs, correlate timestamps, and build timelines manually. Sometimes days pass without any updates. Meanwhile, the scope of compromise remains unclear, and leadership wants answers.

AI SOC benefits include automated triage that answers these questions in minutes. Torq’s AI Agents automatically trace attack paths, identifying initial access vectors, lateral movement, and affected assets without manual log diving.

Impact analysis happens simultaneously. Which systems were touched? What data was accessed? Are there other indicators of the same attack elsewhere in the environment? AI correlates these signals across your entire infrastructure, automatically building comprehensive incident timelines.

9. Better Compliance and Reporting

Audit season shouldn’t mean weeks of manual evidence gathering. But for most SOCs, it does. Compliance requirements keep expanding. Every action needs documentation. Every decision needs justification. Every incident needs a complete paper trail.

AI SOC platforms make compliance automatic. Torq generates full audit trails for every automated action — what was detected, what was analyzed, what decisions were made, what actions were taken, and why.

This transforms compliance from a burden into a byproduct. When an auditor asks for incident documentation, you don’t spend days reconstructing what happened. You pull the automatically generated reports and move on.

10. Cost Efficiency and Resource Optimization

Every dollar spent on manual processes is a dollar not spent on better tools, better training, or better talent.

AI SOC benefits include measurable, provable ROI — typically within 90 days:

Days 1-30: Initial automations live, alert noise dropping, quick wins demonstrated
Days 31-60: Core use cases automated, MTTR improvements measurable
Days 61-90: 90% Tier-1 automation coverage, 60%+ MTTR reduction, full ROI realized

Real-World Use Cases: AI SOC Benefits in Action

HWG Sababa: Years of Automation Built in Weeks

Global MSSP HWG Sababa‘s custom-coded automation couldn’t keep pace with their growing customer portfolio. After switching to Torq, they recreated years’ worth of automations in just weeks.

The transformation:

Torq now automatically manages 55% of total monthly alert volume end-to-end
MTTI/MTTR improved by 95% for medium- and low-priority cases
85% improvement for high-priority cases
Investigation and response now occur simultaneously in under eight minutes
SOC productivity nearly doubled without adding headcount

Beyond efficiency, HWG Sababa focused on analyst experience. As Gianmaria Castagna, their Supervisor of Automation, explains: “It’s annoying for SOC analysts to do the same tedious tasks every day, so we try to help them by automating the most time-consuming processes so they can focus more on the interesting analysis that requires high-level thought.”

The impact extends to their MSSP customers too. Torq enables HWG Sababa to perform containment and remediation actions on the customer side — capabilities they couldn’t deliver manually at scale. For large clients, automated actions save days of reclaimed time.

Marco Fattorelli, Head of Innovation, notes that Torq has become a competitive differentiator: “By accelerating our automations and responses, Torq Hyperautomation helps us stay ahead of the curve and the competition.”

Check Point: Solving a 40% Staffing Gap

Check Point‘s SOC was operating 30-40% below optimal staffing. Too many alerts, too few analysts — a recipe for missed threats.

“If you have an alert that you’re not addressing, that alert might become an incident,” CISO Jonathan Fischbein said. “And that is something that, as the CISO, I don’t want.” Check Point chose Torq for its analyst-centric design and rapid deployment capabilities.

The transformation:

Deployed more than two dozen AI-driven playbooks within days of the POC
Torq now investigates, triages, and auto-remediates alerts without human intervention
High-priority incidents are intelligently routed for analyst oversight
Natural language processing enables the platform to ingest proprietary playbooks and cross-reference industry frameworks like MITRE ATT&CK during investigations

When human intervention is needed, the platform summarizes its workflows, presents relevant data, and offers next-step recommendations — helping analysts make faster, better-informed decisions.

True AI SOC Platform vs Legacy Approaches

Capability	Legacy SOAR	AI-Enhanced Tools	True AI SOC Platform (Torq)
Detection speed	Rule-based, reactive	Faster triage	Real-time pattern analysis
Alert filtering	Manual tuning	Basic ML	Contextual intelligent filtering
False positive rate	High	Moderate	Low with continuous learning
Scalability	Limited	Varies	Cloud-native, unlimited
Data correlation	Manual pivoting	Partial	Full cross-platform fusion
Analyst experience	Tool fatigue	Some relief	Orchestration + enrichment
Threat hunting	Resource-prohibitive	Limited	AI-enabled proactive hunting
Root cause analysis	Manual investigation	Assisted	Automated triage
Compliance	Manual documentation	Partial	Auto-generated evidence
Time to ROI	6-12 months	Varies	30-90 days

Is Your SOC Ready for AI?

Take a quick assessment:

Are analysts spending more time on tools than actual threats?
Do false positives consume over 50% of triage time?
Is MTTR measured in hours instead of minutes?
Are your tools disconnected, requiring manual data pivoting?
Has analyst turnover exceeded 20% in the past year?
Do investigations lack full context and evidence?
Does deploying new integrations take months?
Can you clearly measure automation ROI?

If you checked three or more boxes, your SOC needs an AI transformation.

Stop Chasing Alerts. Start Transforming Your SOC.

AI SOC benefits aren’t about incremental improvement. They’re about fundamental transformation — from reactive alert chasing to proactive security operations, from analyst burnout to analyst empowerment, from months-to-value to weeks-to-value.

Torq delivers full lifecycle automation, proven 90-day ROI, and enterprise-scale performance that works for teams of any size. Organizations across the Fortune 500 have already made the shift.

Ready to transform your security operations?

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FAQs

What is an AI SOC?

An AI SOC utilizes agentic AI and automation to manage the entire security incident lifecycle autonomously — from triage through remediation — rather than just alert triage alone. True AI SOC platforms, like Torq, use adaptive reasoning that learns and evolves, replacing static playbooks with intelligent automation.

What's the difference between AI-enhanced tools and a true AI SOC platform?

AI-enhanced tools often limit automation to alert triage, then hand everything back to analysts. True AI SOC platforms like Torq streamline the entire incident lifecycle: triage, investigation, containment, remediation, and documentation, end-to-end.

What are the main AI SOC benefits?

The primary AI SOC benefits include faster threat prioritization (due to machine speed), reduced alert fatigue for analysts, lower false positive rates through improved context, and the ability to scale incident response operations without adding headcount.

How does AI improve threat detection?

AI improves threat detection by analyzing vast amounts of telemetry data to identify subtle patterns and anomalies that static correlation rules often miss. It can detect unknown unknowns by learning what normal looks like for your environment.

Can AI replace human SOC analysts?

No. AI replaces tasks, not roles. It automates the repetitive Tier-1 work (triage, data enrichment), allowing human analysts to focus on high-value, creative, and strategic security work.

What is the ROI of AI in SOC operations?

The ROI comes from two main areas: Risk reduction (stopping breaches faster, minimizing financial impact) and operational efficiency (allowing the existing team to handle 5x-10x more alerts without increasing headcount).

How quickly can we see ROI from an AI-driven SOC?

With Torq, customers see measurable impact within 30 days and achieve 90% tier-1 automation coverage with 60%+ MTTR reduction by day 90. Traditional SOAR deployments take 6-12 months to reach similar value.

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI SOC News & Events

The Week Torq Became a Unicorn — And What It Means for the Future of SecOps

By Torq

January 16, 2026

4 Minute Read

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

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$140 million Series D. $1.2 billion valuation. A Nasdaq takeover. Trevor loose in Manhattan.

It’s been a week.

From Bloomberg breaking the news to our Torq skeleton on screen through Times Square, Torq’s unicorn moment played out across every major business and cybersecurity outlet — and a few NYC sidewalks. But beyond the headlines (and the chaos), the coverage revealed something bigger: the market has officially declared that the AI SOC is the future of security operations.

Here’s the full recap.

The Headlines

Bloomberg kicked off the week with an exclusive, and the coverage snowballed from there. Over 100 global media placements later, the message was clear: the AI SOC era has arrived, and Torq is leading it.

Bloomberg: “The Israeli cybersecurity startup Torq is planning to announce Sunday that it has closed a $140 million funding round, raising its valuation to $1.2 billion.”

Forbes: “As new entrants crowd into the space with ambitious roadmaps and evolving terminology, Torq increasingly functions as the reference point others are measured against. In that sense, Torq is more or less the de facto leader of the AI SOC space. “

Reuters: “This funding accelerates our mission to define and dominate the AI SOC market,” said Ofer Smadari, CEO and co-founder, Torq.”

SiliconANGLE: “Rather than using simple scripted playbooks that run the same steps every time, Torq uses AI and multi-agent systems that can adapt to changing threat contexts, triage alerts, enrich data with context and decide on next actions autonomously.”

TechRepublic: “‘Our agents are now deeply embedded in the SOCs of Fortune 500 leaders like Marriott, PepsiCo, Procter & Gamble, Siemens, Uber, and Virgin Atlantic. They are running millions of agentic security actions every single day — handling everything from complex investigations to rapid response,’ said Ofer Smadari, CEO & Co-Founder, Torq.”

SecurityWeek: “‘Torq is redefining security operations. They’ve fused automation and human judgment into a new AI SOC Platform built for asymmetric threats and real-world scale,’ Merlin Ventures managing partner Shay Michel said.”

Unicorn status: official. 🦄

Read the official press release >

Read Ofer’s take on what this means for the AI SOC era >

Let’s Hear it For New York…

While the press was filing stories, Torq took over Manhattan.

The Nasdaq Tower: Yes, we put skeletons and lasers on the Nasdaq marquee in Times Square.

The New York Stock Exchange: Ofer sat down with NYSE Live to talk about Torq’s momentum.

On the competition: “We’re fighting big competitors — and we’re winning almost 100% of those.”
On the market: “$40 billion today, $100 billion in five years. We want to take as much as possible out of it.”
On federal expansion: “We have a huge pipeline in the federal market. The need from federal agencies is huge.”

J.P. Morgan HQ: CEO Smadari and Merlin Ventures Managing Partner Shay Michel joined a panel at J.P. Morgan headquarters to discuss the future of AI in the SOC.

The Series D Party: You don’t hit $1.2B valuation without celebrating. The Torq team and our partners, Evolution Equity Partners, Notable Capital, Bessemer Venture Partners, Insight Partners, and Greenfield Partners, gathered in NYC to mark the milestone.

Trevor’s NYC Adventure: Trevor, our media intern, also made the trip to New York — unauthorized and unapproved by HR. Seems like he had fun.

What’s Next For Torq

This funding accelerates three priorities:

Scaling the AI SOC.More integrations. Deeper automation. Expanded multi-agent capabilities. We’re building the infrastructure that lets security teams do more without adding headcount.

U.S. Federal market expansion. With Merlin Ventures as a partner, we’re accelerating into federal and public sector markets — bringing autonomous security operations to the agencies protecting critical infrastructure.

Growing the Torq team. We’re hiring 200+ people in 2026 across engineering, go-to-market, and customer success. If you want to build the future of security operations, join us.

This is Just the Beginning

This week validated what we’ve been building since 2020: a fundamentally different approach to security operations, built on agentic AI and Hyperautomation, and designed for enterprise scale.

The AI SOC isn’t coming. It’s here. And Torq is just getting started.

🔥 LFG.

Read the Press Release

Get a Demo

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

Get a Demo

AI AI SOC Security Automation 101

Alert Fatigue Is Killing Your SOC. Here’s What Actually Works in 2026.

By Torq

January 14, 2026

11 Minute Read

Get a Personalized Demo

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

Request a Demo

Your SOC received 10,000 alerts yesterday. How many were real threats?

Most SOC teams operate in a constant state of triage. Alerts pour in from dozens of tools, each one demanding attention, each one potentially critical. The reality? Your analysts are making high-stakes decisions about which alerts to investigate based on gut instinct and whatever time they have left in their shift.

This approach worked when SOCs dealt with hundreds of alerts per day. It’s completely unsustainable at 10,000+.

The math is brutal: 59% of leaders report too many alerts as their main source of inefficiency. Your team is burning cognitive energy on noise while sophisticated threats exploit the chaos. Attackers know this. They’re counting on it.

Something has to change. In 2026, it finally is.

The Alert Fatigue Crisis: Why Traditional Approaches Failed

Alert fatigue isn’t about volume alone. It’s about the cognitive load of constantly context-switching between tools, the frustration of investigating the same false positives repeatedly, and the pressure of knowing a missed alert could mean catastrophe.

Research shows that 47% of analysts point to alerting issues as the most common source of inefficiency in the SOC — work that’s repetitive, draining, and prone to human error. When you’re reviewing your 8,000th alert of the day, even critical indicators start to blur together.

The psychological toll is staggering. Analyst burnout rates hit record highs in 2025, with the average analyst only staying in the role 3-5 years.

The consequences compound. High turnover means institutional knowledge walks out the door. New analysts take months to ramp up, and meanwhile, attackers keep evolving, and alert volumes keep climbing.

Traditional solutions haven’t solved this. Adding more analysts just distributes the misery. Tuning SIEM rules creates blind spots. Legacy SOAR promised automation but delivered brittle playbooks that break constantly.

The problem isn’t effort. It’s architecture. Modern cybersecurity alert management requires a fundamentally different approach.

What’s Changed: The Rise of Agentic AI in Alert Management

The 2026 SOC looks nothing like its predecessors.

From rule-based to reasoning-based. Traditional alert management relied on static rules: if X happens, do Y. But threats don’t follow predictable patterns. Agentic AI uses adaptive reasoning to evaluate alerts in context, making decisions based on learning rather than rigid logic.

From triage-only to end-to-end. Legacy tools automated the easiest part — sorting alerts into buckets. Then they handed everything back to analysts. Modern AI SOC platforms handle the full lifecycle: detection, triage, investigation, containment, and remediation. Autonomously.

From single-tool to cross-environment. Attacks pivot across email, endpoint, cloud, and identity. Effective cybersecurity alert management requires correlating signals across your entire stack simultaneously — something humans can’t do at scale, but multi-agent systems can.

From black-box to explainable. Early AI security tools made decisions nobody could understand or trust. Today’s platforms show their work. Every action is logged, auditable, and reversible. Analysts can see exactly why the AI made each decision.

How AI-Powered Alert Management Actually Works

The best way to understand modern alert management is to follow an alert through the system.

Step 1: Intelligent Ingestion

An alert fires from your SIEM: suspicious login from an unusual location. In a traditional SOC, this joins a queue of hundreds waiting for human review.

With Torq, the alert is immediately ingested and enriched. The system pulls context automatically: the user’s normal login patterns, endpoint health, recent authentication history, and threat intelligence on the source IP.

Step 2: Automated Investigation

Torq’s Multi-Agent System deploys specialized AI Agents to investigate in parallel. One checks identity logs. Another queries the endpoint. Another correlates with recent phishing attempts targeting this user. All simultaneously.

What would take an analyst 30-45 minutes of manual pivoting happens in seconds.

Step 3: Contextual Decision-Making

The AI evaluates the evidence: This user normally logs in from the US. The login came from Eastern Europe. But the user also submitted a travel request last week for a conference in Prague. The endpoint shows no signs of compromise. Recent MFA challenge was successful.

Verdict: legitimate travel, not a threat. The alert is suppressed with full evidence retained.

Step 4: Autonomous Action or Escalation

For confirmed threats, the AI takes immediate containment action — isolating endpoints, revoking sessions, blocking IPs — all within seconds. For ambiguous cases, it escalates to analysts with a complete investigation summary and recommended next steps.

The analyst doesn’t start from scratch. They review the AI’s work and make the final call.

Step 5: Continuous Learning

When analysts correct or confirm AI decisions, the system learns. Accuracy improves over time. The AI adapts to your specific environment, your risk tolerance, and your organizational patterns.

This is what modern cybersecurity alert management looks like. Not humans racing against an endless queue, but humans and AI working together, each doing what they do best.

8 Criteria for Choosing the Right Alert Management Solution

Not all SOC automation is created equal. When evaluating alert management platforms for 2026, demand answers to these questions:

Does it eliminate, not just reduce, false positives? Look for solutions that achieve false positive reduction rates above 90%. Anything less still leaves analysts buried.
Can it handle your alert volume today and tomorrow? Scalability isn’t optional. The system should process alerts at machine speed regardless of volume spikes.
Does it integrate natively with your existing stack? Pre-built integrations with your SIEM, EDR, cloud security tools, and ticketing systems are non-negotiable. Custom API work shouldn’t be required.
How transparent is the decision-making process? Black box AI erodes trust. Choose platforms that explain why alerts were prioritized, escalated, or dismissed.
Can analysts teach it what matters to your organization? The best systems learn from feedback. Every analyst decision should improve the model.
Does it automate response, not just detection? Alert management should trigger automated containment, isolation, or remediation for known threat patterns.
What’s the time to value? Deployment shouldn’t take months. Modern platforms deliver measurable impact within weeks.
Can it prove ROI? Demand concrete metrics: hours saved, MTTR improved, and analyst capacity freed up.

How AI SOC Platforms Actually Solve Alert Overload

The shift from traditional SOAR to AI SOC platforms represents a fundamental change in how organizations manage security operations. Instead of forcing analysts to adapt to rigid playbooks, modern solutions like Torq adapt to how your team actually works.

Here’s what sets AI SOC platforms apart:

Agentic AI that reasons, not just executes: Traditional automation follows if-then logic. AI agents reason through problems. When an alert fires, Torq’s AI Agents don’t just check a playbook — they investigate, correlate signals across your entire stack, and determine what the alert actually means for your specific environment. An authentication failure from a known test account gets automatically dismissed. That same failure from a privileged user at 3am triggers immediate escalation with full context.

Multi-agent systems that work together: Torq’s Multi-Agent System deploys specialized AI Agents that collaborate autonomously. A Case Management Agent handles triage and prioritization. Enrichment Agents gather context from threat intelligence, asset inventories, and user behavior analytics. Investigation Agents perform automated analysis. Response Agents execute containment. All working in concert, without human intervention, at machine speed.

Context that evolves with your environment: Static rules become obsolete the moment threats evolve. Torq Hyperautomation™ continuously adapts to analyst decisions, threat intelligence, and your environment’s behavior patterns. The system gets smarter every day, automatically adjusting prioritization as your threat landscape shifts.

Cloud-native speed and scale: Legacy SOAR platforms can’t keep pace with cloud-speed threats. Torq’s cloud-native architecture processes alerts at machine speed regardless of volume spikes. When your environment generates 50,000 alerts during a campaign, Torq scales instantly — no performance degradation, no missed threats.

Real Results: Organizations Transforming Alert Management

Agoda: End-to-End Phishing Automation

Online travel platform Agoda needed to scale security operations with a lean, distributed team during a major cloud migration.

With Torq, employees report suspicious emails with one click. The platform automatically enriches data, analyzes attachments, classifies threats with AI, and responds to users, all without human intervention.

“Torq completely removes manual intervention for phishing,” says Laksh Gudipaty, Security Incident Response Manager at Agoda. “It’s now end-to-end automated on a 24×7 basis.”

Results: 47% reduction in missed SLOs for cloud security and incident reports generated in 30 minutes instead of 7 hours.

Valvoline: 7 Analyst Hours Saved Daily

Valvoline‘s security team was cut in half during a divestiture. Their legacy SOAR was code-heavy, and only a few people could maintain it.

Torq transformed their phishing workflows — previously consuming up to 12 hours daily — into fully automated processes. An integration their legacy SOAR couldn’t complete after hundreds of hours was running in under a week.

“My team is in love with the product,” says Corey Kaemming, Senior Director of InfoSec at Valvoline. “Sometimes, I have to tell them to stop having so much fun.”

Results: 6-7 analyst hours saved per day and operational ROI within 48 hours.

Global Money Transfer Platform: Day-Long Tasks in 3 Minutes

This financial services company was drowning in manual alert management. Their in-house tool couldn’t scale with alert volumes or integrate with their security stack.

Torq was implemented in days, not the months their previous system required. The vast majority of alerts are now automatically identified, analyzed, and remediated.

Results: 30% time savings across the security team and IAM tasks reduced from a full day to 3 minutes.

Your 90-Day Roadmap to Autonomous Alert Management

Organizations successfully transforming their alert management with Torq follow this proven 90 day approach.

Month 1: Foundation Building

In the first 30 days, the focus is on standing up the platform, connecting your stack, and shipping quick wins. Guided by a dedicated Torq team, your SOC enables SSO and role mapping, lights up core integrations like M365/Defender, Okta/Entra, CrowdStrike, Slack, Jira, and AWS, and launches the first workflows — phishing triage, EDR alert handling, or cloud misconfiguration detection.

Your builders are trained on workflow design, testing, and debugging. By the end of the first month, automations are live, Tier-1 alert noise is already dropping, and analysts are reclaiming hours once lost to swivel-chair triage.

What to Measure:

First workflows deployed and delivering value
Tier-1 analyst workload beginning to decline
Platform familiarity achieved across the builder team
Baseline MTTR and alert volumes documented

Month 2: Process Optimization

The next 30 days focus on scaling and simplifying. A second wave of workflows expands coverage into IAM offboarding, IOC enrichment, login anomaly detection, and user behavior signals. Socrates, Torq’s AI SOC Analyst, is deployed to handle Tier-1 triage, enrichment, and case summaries.

Teams tune thresholds, implement deduplication and correlation rules, and adopt modular subflows and templates to accelerate workflow reuse. Automation KPIs like MTTR, suppression rate, and analyst touches per case are established to measure impact.

What to Measure:

Automation coverage tracking (percentage of Tier-1 alerts handled end-to-end)
Suppression rate (false positives automatically identified and closed)
Builder teams creating workflows independently
Alert fatigue reduced through smarter case thresholds

Month 3: Full Autonomy

By the end of three months, your SOC begins operating as an autonomous system with human-in-the-loop guardrails. Socrates orchestrates the entire case management lifecycle from ingestion through enrichment, correlation, decision, response, and documentation. Analysts only step in for escalated incidents.

Standard operating procedures and runbooks are finalized, intake and closure criteria are standardized, and before-and-after benchmarking is completed to prepare for the first quarterly business review.

What to Measure:

Up to 90% of Tier-1 alerts automated end-to-end
MTTR drops by 60%+ on core use cases
Analyst touches per case approaching zero for Tier-1 incidents
Analysts shift from reactive case handling to proactive oversight and threat hunting
Tool consolidation savings documented (legacy SOAR licenses retired)

The Future of Alert Management Is Here

Cybersecurity alert management has been broken for years. The answer was never more analysts, more tools, or more rules. It was a fundamental shift in how alerts get processed — from human-speed to machine-speed, from manual triage to autonomous resolution, from reactive firefighting to proactive defense.

That shift is happening now. Organizations running AI SOC platforms are achieving what seemed impossible just two years ago: 95%+ Tier 1 automation, 60%+ MTTR reduction, and analysts who actually want to stay in their jobs.

The technology exists. The results are proven. The only question is how long you’ll wait while your competitors make the leap.

Torq is the enterprise-grade autonomous SecOps platform that combines adaptive agentic insights and automation to triage, investigate, and remediate your most critical threats. The platform streamlines every step from alert through fix, working alongside your SecOps staff to transform overwhelming alert volumes into manageable, prioritized action.

The future of security operations is autonomous. The platform is Torq. The timeline is 90 days.

Get the 90-Day Roadmap to see exactly how Torq customers achieve SOC autonomy in three months.

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FAQs

What is alert fatigue in cybersecurity?

Alert fatigue occurs when SOC analysts become desensitized to security alerts due to high volumes and frequent false positives, leading to missed threats and analyst burnout.

How does AI improve alert management?

AI-powered systems use agentic reasoning to automatically classify, prioritize, enrich, and investigate alerts at machine speed, dramatically reducing false positives while accelerating response to genuine threats.

What's the difference between traditional SOAR and AI-powered alert management?

Traditional SOAR relies on static playbooks and rule-based automation. AI-powered platforms use adaptive reasoning that learns from context, evolves with threats, and handles complex scenarios without predefined rules.

How quickly can organizations see ROI from automated alert management?

Leading platforms deliver measurable impact within 2-4 weeks, with most organizations achieving 70%+ false positive reduction and significant MTTI improvements in the first 90 days.

Can small security teams benefit from AI-powered alert management?

Absolutely. AI-powered automation is a force multiplier for lean teams, enabling 2-3 analysts to manage alert volumes that would typically require 10+ people using traditional methods.

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

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

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Contents

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What’s Breaking in the Traditional SOC Model

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

Will AI Replace SOC Analysts? Displaced, Not Replaced

What the AI SOC Org Chart Looks Like in Practice

What’s Stopping CISOs from Redesigning Around AI?

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

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Contents

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TL;DR

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

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

1. API Keys

2. Basic Authentication

3. OAuth 2.0

4. JWT (JSON Web Tokens)

5. Mutual TLS (mTLS)

6. HMAC (Hash-based Message Authentication Code)

7. OpenID Connect

The Hidden Risk: What Happens When API Authentication Fails

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

Building a Secure, Self-Healing Integration Strategy with Torq

Dead Integrations Don’t Send Alerts

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The True Cost of Running a SOC

People Costs

Tooling Costs

Breach Costs

Hidden Costs

Outsourcing Costs

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

The Three Pillars of an Autonomous SOC

1. Hyperautomation

2. AI Agents

3. Enterprise-Grade AI Architecture

What an Agentic SOC Fixes

The Future of SOC Economics

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