AI AI SOC Integrations

How Torq Optimizes Agentic SecOps From Detection Through Resolution with Google SecOps

By Rick Bosworth,
Sr. Director of Product Marketing

April 22, 2026

6 Minute Read

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

The AI SOC is cybersecurity’s fastest-growing category, and for very good reason. Machine-speed threats demand machine-speed responses, and the $82.45 billion market forming around this reality reflects just how urgent that need has become.

The Torq AI SOC Platform delivers agentic insights and the ability to streamline action across the full security stack. Torq is officially listed as a technology partner that can host an integration with Google Security Operations, enabling Torq to cover the complete threat lifecycle from signal to resolution.

The results are concrete. According to Torq analysis, security teams report a 4x increase in alert handling capacity without adding headcount, a 75% reduction in MTTR that turns hours-long response cycles into minutes, and 95% of Tier 1 tickets auto-remediated.

That last number matters most. The repetitive, high-volume triage work that consumes analysts’ time is handled nearly entirely by the integrated platform, freeing your team for the investigations that actually require human judgment.

Detection Meets Autonomous Response

Google Security Operations is an intelligence-driven, AI-powered platform that gives security teams an incredibly powerful foundation: cloud-scale detection, deep analytics, and the visibility to spot threats across even the most complex environments. Pair that with the Torq AI SOC Platform built on a foundation of agentic AI and Hyperautomation, and something powerful happens. Detection doesn’t only surface threats — it triggers an entire response workflow, automatically.

Torq prioritizes the most important detections, contextualizing risk and identifying threat needles buried within the alert haystack. Cases are created and agentic investigations launched automatically, to quickly uncover the necessary containment and remediation actions to be either executed autonomously or with human-on-the-loop authorization. All agentic reasoning and actions are transparent and fully documented. Your team maintains total oversight and control.

With Torq’s integration with Google Security Operations, every one of those steps can happen at machine speed, with full auditability and AI doing the heavy lifting. Your security team is freed from the manual grind to focus on the decisions that require human judgment.

What Torq Delivers with Google Cloud

Torq connects to your entire environment, including Google Unified Security, Security Command Center, and Google Workspace, as well as 400+ tools across cloud infrastructure, identity, endpoints, email, data protection, and IT service management.

Through the integration, Google Security Operations alerts are ingested by the Torq platform, where it creates a case and launches an automated investigation and response workflow without waiting for a human to intervene.

Less noise. Torq pulls detections directly from Google Security Operations via API and immediately applies agentic auto-triage: correlating related events, enriching them with threat intelligence and risk context, and delivering a verdict on every alert. False positives are filtered before they reach your team, leaving analysts with a prioritized view of actual risks rather than a queue of raw alerts to work through manually. Every alert becomes a tracked, enriched, actionable case — not a notification that gets buried in a queue.

Full visibility, shared across every stakeholder. For confirmed issues, Torq’s AI SOC Analyst, Socrates, gets to work automatically. It queries Google Security Operations for related events, mapping context across the environment, assembling timelines, and producing a complete case summary in natural language — ready for analyst review, approval, or autonomous closure. Native case management gives security, cloud engineering, IT operations, and business leadership a single shared view from detection through resolution, with complete visibility into every AI decision and action along the way.

Response that goes all the way to remediation. Torq executes response actions across your entire security stack: blocking users, isolating endpoints, revoking access, and notifying stakeholders. Automated workflows then coordinate remediation across cloud infrastructure, endpoints, identity systems, network, and beyond — without requiring your team to context-switch between tools. Everything is logged where it belongs: in your SIEM. Most solutions stop at analysis. Torq covers the full lifecycle.

Flexible log ingestion and custom parsing. Torq also supports raw log ingestion back into Google Security Operations, with custom parser support for non-standard data sources. If it lives in your environment, it can live in your SIEM.

Built for the AI SOC

When a Google Security Operations alert fires on a compromised credential, Torq doesn’t just run a static playbook. It investigates the user’s recent activity, checks for lateral movement, evaluates policy, notifies the right people, and takes action. All in a single, fully documented flow. The analyst can see a complete picture and a recommended next step, not just an alert number.

Key capabilities that power the solution:

Agentic AI triage, investigation, and response
400+ native integrations
Transparent agentic reasoning and control over agentic action
No-code and agentic workflow building
Human-in-the-loop controls
Immutable audit trails
Cloud-native enterprise architecture

“Google Security Operations is where the world’s best security teams detect threats. Torq is where those threats are further prioritized, investigated, and resolved at speed and scale. This integration and partnership is about building a continuous, AI-augmented response loop that eliminates the manual work between detection and remediation.”

– Rachel Israel, Director of Tech Alliance, Torq

Getting Started with the Integration

Torq’s collaboration with Google Cloud extends beyond Google Security Operations. Torq’s integration with Google Cloud allows security teams to automate workflows across the full Google Cloud environment — including Google Chat notifications, Google Workspace user management, and any custom Google Cloud API action through Torq’s Step Builder.

Setup takes minutes:

Create a Google Cloud service account in IAM & Admin with the appropriate scopes.
Generate a JSON private key and upload it to Torq’s Google Cloud integration.
Enable the APIs for the Google Cloud services you want to automate (Gmail, Google Drive, Google Workspace, etc.).
Connect Google Security Operations as an alert source in Torq.

From there, Torq handles the rest. No playbook scripting. No brittle automation. Just outcomes.

“Torq is the de facto leader of the AI SOC space. While the category is now being treated as emerging, Torq’s position reflects something closer to incumbency — an established platform in a market that is only just catching up to what it represents.” – Forbes

Better Together: What Torq’s Collaboration with Google Cloud Can Help Unlock for Your SOC

Security teams aren’t looking for more dashboards or more alerts. They’re looking for outcomes. Resolved cases. Contained threats. Time back for the work that actually requires human judgment.

The Torq AI SOC Platform on Google Cloud delivers exactly that. Detection happens in Google Security Operations. Response happens in Torq. And the full lifecycle — from signal to resolution — is covered, documented, and auditable from end to end.

That’s the AI SOC. And it’s available right now on Google Cloud Marketplace.

Ready to see what Torq and Google Security Operations look like running together?

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Ready to automate everything?

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

Corey Kaemming, Senior Director of InfoSec

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

Todd Willoughby, Director

“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 Customers

From 24/7 On-Call to Holidays Off: AI SOC Automation Results from Three Security Teams

By John White

April 23, 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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John White is the Field CISO for EMEA at Torq. A respected security executive with more than 20 years of leadership experience, John previously served as CISO at Virgin Atlantic, where he led a multi-year transformation deploying the Torq AI SOC Platform to modernize cyber operations. Prior to that, he built and transformed security functions for global organizations, including ASOS, Liberty Global, AEG Europe, and KPMG.

I spent 20 years as a CISO. I know what it feels like to run a SOC that’s stretched thin and held together by sheer effort — where every alert, regardless of severity, means someone’s pulling out a laptop at midnight or stepping away from their family on a holiday.

So when I sat down with three security leaders at a recent Torq customer panel, I wasn’t interested in the pitch-perfect version of their AI SOC automation journey. I wanted the real one: what broke, what they tried, what actually worked, and what changed for the people doing the work.

What I got were scenes I’ve seen play out a hundred times — lean teams, brittle tools, a breaking point — followed by something I’ve rarely seen: three teams that came out the other side with fundamentally different operations. Not incrementally better. Truly different.

Here are their stories.

Why These Security Teams Hit the Limits of Manual SOC Operations

Every team had a different trigger. The pattern underneath was identical: too few people, too many tools, and manual work that couldn’t scale, no matter how hard the team worked.

Corey Kaemming, CISO at Valvoline, inherited the problem three months into the job: a corporate divestiture that cut his team in half overnight. And the technologies split with it. The SOAR they’d been running was heavily customized — the kind of system that works until it doesn’t, and when it doesn’t, everything goes down. On top of that, their MDR provider was only responding to alerts from two tools. If an alert didn’t come from one of those two tools, it functionally didn’t exist.

Three months in, half the team gone, a brittle SOAR, and blind spots across the stack. That was Corey’s starting position.

Dustin Nowak, Cyber Threat Manager at Kenvue (the parent company of brands like Johnson’s, BAND-AID, and Neutrogena), faced a nearly identical divestiture — but his challenge was even more foundational. His team needed to stand up an internal hybrid SOC from scratch. They needed case management that could support a structured investigative process — something that followed NIST or SANS methodology, not just a ticketing queue. Most platforms they evaluated treated case management as an afterthought.

Matthew Brister, Staff IR and Threat Hunting Engineer at Henry Schein One, had four people covering 24/7 operations. Every alert, every time — Tuesday afternoon or Christmas morning — someone was on a laptop. For a team that small, every hour burned on a low-priority alert was an hour stolen from an investigation that actually mattered.

What AI SOC Automation Delivered in the First 30 Days

The first month on a new platform is where reality either matches the demo or it doesn’t. For these teams, Torq matched — and in some cases exceeded — their expectations.

Matt’s team moved the fastest. They tackled all five of their priority use cases in the first week. The remaining three weeks were spent exploring what else was possible. “I kept calling my team saying, ‘Show me something cool,'” he said.

But the number that doesn’t capture what actually changed is the one Matt told me next. Before Torq, his lean team was on call around the clock, every day and on every holiday. If an alert came in at 2am on Christmas, someone opened a laptop. After deploying Torq’s AI SOC platform, any obviously malicious action is automatically locked down. Last Christmas was the first holiday where alerts came in, but nobody had to leave their family.

I’ve sat in hundreds of vendor sessions. That’s the kind of outcome that sticks with you.

Corey’s team saw the efficiency gains immediately: six to seven hours per day saved in analyst work by removing manual, repetitive tasks. But what mattered just as much was what the platform didn’t require. “If it’s going to take three to four people to manage it, I’m out,” he said. “The time my team spends in Torq isn’t on care and feeding — it’s on building. That’s huge value, especially with a lean team.”

Mitch started where he had to — with a single pane of glass. When you’re operating across so many SOC tools, the first win isn’t automation. It’s being able to see everything in one place. Unified case management gave his team that foundation. From there, they moved into enrichment automation: the meta lookups, the IoC checks, the steps that run the same way for every incident.

Dustin took the most deliberate approach. He didn’t try to automate everything on day one. He started with case management — building the investigative structure first, then layering integrations and automation on top. It was the slowest start on paper. It was also the foundation that let everything else scale.

How to Build the Business Case for AI SOC Automation

Getting the Torq platform approved was only half the fight. Getting the organization to believe in it — and to stop defaulting to the tools they already had — was the real work.

Corey ran a head-to-head evaluation against a competitor. He chose a use case that the competitor couldn’t solve. Torq figured it out in three to four days. That made the technical case. The ROI case came from the six to seven hours per day saved in analyst time. But the political battle was harder: differentiating Torq from everything else already in the stack. Splunk was already there. Azure was already there. Why couldn’t those tools do this?

The answer was in the operational reality. None of those tools could unify the workflow across the full stack without heavy customization and a dedicated team to maintain it. Torq could, and it didn’t require an engineering staff to keep it running.

Dustin’s approach was different, and it’s the one I’d recommend to any CISO trying to make the SOC relevant to leadership. Kenvue makes Tylenol, Band-Aids, and consumer health products. To get leadership’s attention, the security team had to speak the business’s language.

One of their biggest use cases turned out to be digital rights protection — monitoring social media for fake accounts and brand threats. When someone set up fake Facebook accounts, Dustin’s team ingested the threat intelligence, automated monitoring, and told the business exactly what was happening regionally in real time. That took the SOC from cost center to what Dustin calls a Cyber Fusion Center (CFC) — relevant to the business in a way that MTTR metrics alone never could be.

Matt had the smoothest internal path. His boss was hands-on with the SOC and had leadership backing from the start. The team said yes immediately. The only question was how to divide up the work. Later, Matt built a dashboard in Torq to justify expenses across security tools — and it worked so well that teams outside security started asking him to build dashboards for their tools, too.

When AI + Automation Expands Beyond Security Operations

Here’s what surprised me: None of these teams stopped at security operations. Once Torq proved its value in the SOC, adjacent teams began to show up.

One of the themes that came up across the panel was how teams combine different approaches to security operations — and whether automation can scale the function at a fraction of the cost. At Kenvue, the team is already exploring that: rather than outsourcing to an MDR at full price, they’re looking to bring it in-house through the automation they’ve already built.

Corey’s team is advancing identity-focused security after experiencing impersonation attempts. In response, they are developing an identity verification workflow using Torq that relies on contextual validation rather than traditional methods. The approach leverages existing organizational signals to help confirm legitimacy, reducing reliance on static or easily exploited verification techniques.

Matt’s team is leaning into agentic AI and pushing for deeper data retention capabilities. They’ve already built creative workarounds using Torq workflows and dashboards to hold onto investigation data longer — and they want that to go further. It’s a sign of how much operational weight the platform is carrying: teams aren’t just using it for automation, they’re building core SOC infrastructure on top of it.

What These Security Leaders Learned Deploying AI SOC Automation

I asked each panelist what advice they’d give to a CISO or SOC leader considering a similar move.

Corey: Trust your team. Empower them to make decisions. Get governance right before you deploy — especially around AI, data privacy, and PII. Bring legal in early, not after. And once it’s running, market it internally. Don’t gate-keep. When other teams come asking, the answer should be “yeah, I can help — I have a tool for that.”

Matt: Get your foundation right in month one. Alerts aggregated. Use cases defined. If you don’t set the base, everything you build on top of it will be shaky.

Dustin: Make it relevant to the business. If you’re only reporting in SOC metrics, you’re invisible. Translate your impact into language the business understands, by region, by business unit, by brand risk.

The AI SOC Automation Playbook

Different companies, different industries, and different team sizes. The same arc: a breaking point that forced a change, a first month that proved the value, an internal battle that tested whether the platform could survive organizational gravity, and an expansion that nobody planned but everyone benefited from.

The teams that deployed Torq for AI SOC automation didn’t just get faster metrics. They got analysts who stopped dreading on-call rotations. They got SOCs that earned credibility with the business. They got a platform that other teams wanted to use. And in one case, they got their Christmas back.

That’s not a vendor story. That’s an operational one. And it’s the kind of outcome that only happens when the technology actually works the way the demo said it would.

These conversations happened at a recent Torq customer panel. Thank you to Corey, Dustin, and Matt for their time, honesty, and willingness to share what they’ve learned.

Torq surveyed 450 CISOs and security leaders on where AI in the SOC is delivering, where trust is breaking down, and what a true AI SOC actually looks like.

Get the 2026 AI SOC Leadership Report

FAQs

What results can you expect from AI SOC automation in the first 30 days?

Based on three enterprise Torq deployments, teams saw results within the first week to first month: Valvoline saved six to seven analyst hours per day by automating repetitive tasks. Henry Schein One deployed five priority use cases in the first week and eliminated 24/7 on-call requirements for a four-person team. Kenvue built a structured case management foundation. Time-to-value was measured in days, not months.

How do you build a business case for AI SOC automation?

The strongest business cases combine quantified analyst time savings (Valvoline documented six to seven hours saved per day), competitive evaluation against alternatives (Torq solved a use case in three to four days that a competitor couldn’t), and business-relevant framing (Kenvue translated SOC impact into brand risk and regional threat data, which took the SOC from cost center to what they call a cyber fusion center).

Does AI SOC automation require a large team to manage?

No. One of the most consistent findings across all four teams was that the Torq AI SOC platform didn’t require dedicated staff to maintain. Valvoline’s CISO was explicit: “If it’s going to take three to four people to manage it, I’m out.” Teams spent their time building new use cases, not maintaining the platform — which is critical for lean SOCs that can’t afford to trade one operational burden for another.

Can AI SOC automation expand beyond security operations?

Yes — and it did for every team in this panel. At Kenvue, expansion into data privacy and IT incident response is underway. At Valvoline, identity verification workflows are being built for anti-spoofing. The pattern: once the Torq platform proves value in the SOC, adjacent teams discover it on their own.

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 Research

Torq Leads Every Category in the 2026 KuppingerCole Analysts Leadership Compass: Emerging AI SOC

By Rick Bosworth, Sr. Director of Product Marketing

April 20, 2026

9 Minute Read

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

Request a Demo

The security automation market just got its definitive evaluation and its new name.

KuppingerCole Analysts is the global analyst firm that sets the benchmark for cybersecurity technology evaluations. Their Leadership Compass is the gold-standard independent assessment enterprises rely on when making platform decisions, rigorously scoring vendors across product maturity, innovation, and market execution. When KuppingerCole Analysts names a category, the industry follows. When they name a Leader, buying teams listen.

KuppingerCole Analysts’ April 2026 Leadership Compass retired the SOAR category entirely. The report is now called The Emerging AI SOC to reflect the market transformation. In the words of KuppingerCole analyst Matthew Gardiner, “Traditional rule-based workflow and playbook-based SOAR systems have hit an efficiency and implementation brick wall. The cost and expertise required to get significant ROI from traditional SOAR systems is out of reach for average security organizations… AI is showing early signs of changing this calculus and lowering the bar for smart(er) automation.”

We couldn’t agree more. The new frontier is agentic AI, adaptive reasoning, and platforms that augment analysts, not replace them. Transparency and control have been at the heart of our AI SOC strategy for years.

Torq was named a Leader by KuppingerCole Analysts in all four categories for the AI SOC: Overall, Product, Innovation, and Market.

Torq scored highest in both Innovation Leadership and Product Leadership. We are named a Market Leader driven by rapid customer growth, expanding revenues, and increasingly comprehensive capabilities. And was evaluated as an Overall Leader across every dimension that KuppingerCole Analysts measures.

Here’s what the KuppingerCole Analysts Leadership Compass for the Emerging AI SOC found and what it means for the market.

The AI SOC Category Is Official. SOAR Is Not Coming Back.

KuppingerCole Analysts rebranded the report category because the underlying technology for security automation has fundamentally shifted. Newer automation capabilities “increasingly focus on AI-based reasoning, natural language chat interfaces, contextual enrichment, and adaptive decision support” rather than simply executing preconfigured rules and playbooks. Rule-based systems only work when the problem space is stable, and security operations are anything but.

The competition has shifted with the market. Differentiation is no longer about who has the most predefined playbooks or the longest feature list. It’s about usability, flexibility, scalability, and the ability to support heterogeneous, tool-rich environments without excessive engineering or people-intensive overhead. Playbooks and out-of-the-box integrations still matter, but they’re table stakes. The real differentiator is how effectively a platform reduces the human burden while handling the messy, multi-vendor reality of a modern SOC.

This is the shift Torq was built for — and the criteria KuppingerCole Analysts used to separate Leaders from the rest of the field.

The report also names the operational problem driving that shift: agentic AI has the potential to rebalance the false-positive-to-false-negative tradeoff that has defined SOC work for years. With manual triage, alert fatigue is so severe that missed threats become an accepted cost. AI agents don’t experience fatigue. They triage 100% of alerts continuously, shifting sensitivity to catch threats rather than just manage volume.

The buyer data backs this up. Torq’s 2026 survey of 450 security leaders found that 97% are confident AI can handle triage, the highest-volume function in the SOC. But only 35% have deployed it there. The confidence exists. The platforms to act on it haven’t — until now.

Where Torq Outscored in the KuppingerCole Analysts AI SOC Evaluation

KuppingerCole Analysts evaluates vendors across product leadership, innovation, and market execution. Here’s where Torq stands.

Product Leader. Torq scored highest in product leadership. The Product Leaders in this analysis stand out for the breadth, depth, and maturity of their current product capabilities, delivering robust, production-grade platforms that address the full lifecycle of modern SOC operations. This includes detection integration, triage, investigation, automation, case management, governance, and reporting. Torq earned this position with strong architectural foundations, rich functionality, and proven suitability for complex operational environments.

Innovation Leader. As Gardiner states, “Innovation Leaders push the boundaries of how SecOps are designed, automated, and augmented with AI.” Torq scored highest in innovation, with a standalone callout for behavioral detection coverage across users, devices, AI agents, and other non-human identities, as well as threat actor attribution, cloud infrastructure automated response coverage, and extensive AI decision explainability.

Market Leader. Market Leadership weighs the number of customers, their geographic distribution, deployment size, support services, partnership ecosystem, and financial health. As KuppingerCole Analysts makes clear, “Market Leadership requires global reach.” Named a Market Leader, Torq has quickly emerged as a security automation market leader, driven by rapid customer and revenue growth, unique branding, and increasingly comprehensive product capabilities — reshaping expectations for modern security automation by emphasizing simplicity, extensibility, and rapid deployment through low-code automation, lowering the barrier to adoption while supporting complex enterprise use cases.

Overall Leader. Named an Overall Leader, the Torq AI SOC Platform exemplifies a new generation of security automation — excelling at low-code orchestration, rapid automation across security tools, and AI-assisted and agentic workflows. Positioned as a security automation engine within the SOC that is independent of the large security platforms. And in a relatively short time, acquired a significant number of enterprise customers.

Torq’s AI SOC Strengths Documented in the KuppingerCole Analysts Leadership Compass

The report identified eleven specific strengths for Torq. Here are the ones that matter most for buying teams.

Proven Agentic AI

Torq has measurable, documented success with agentic AI among early-adopter customers, including Fortune 500 production security operations.

Our 2026 Torq AI SOC Leadership Report found that 72% of CISOs and security leaders are comfortable with fully autonomous AI for medium-severity incidents and below, but most haven’t deployed it because the platform controls aren’t in place. Not a problem with Torq. Documented customer success with Torq Agentic AI shows that Torq controls work in practice, not just in a demo environment.

“Torq Agentic AI now handles 100% of Carvana’s Tier-1 security alerts and has automated 41 different runbooks within just one month of deployment.”

– Carvana

Deep Integration Layer

KuppingerCole Analysts noted Torq’s extensive support for third-party SIEM, EDR/XDR, ITSM, web gateways, firewalls, threat intelligence sources, IAM/PAM, UEBA, and file sandbox systems as the first in its list of strengths.

300+ pre-built integrations covering a broad spectrum of SOC use cases. Extensive support for public cloud response actions, including enabling/disabling users and starting/stopping instances. Deep integrations with ITSM platforms like ServiceNow, Jira Service Management, BMC Helix, Freshservice, and Ivanti. Broad support for incident communication systems, including email, Slack/Teams, PagerDuty, and SMS. Customization support for both enterprise customers and MDR providers.

This directly addresses the fragmentation problem we see in SOCs: 85% of CISOs want a unified AI SOC platform. You can’t unify what you can’t integrate.

AI-Native Architecture

The Torq platform supports Retrieval Augmented Generation (RAG) for pulling in external data sources, Model-Context Protocol (MCP) for standardized tool connectivity, and agent-to-agent collaboration, enabling specialized AI agents to coordinate across investigation steps without human handoffs. Analysts can describe what they want in plain language — “enrich this alert with threat intel, check if the user has had prior incidents, and isolate the endpoint if confidence is high” — and the platform automatically generates and validates a production-ready workflow.

This is the architectural difference the KuppingerCole Analysts AI SOC evaluation is measuring: whether AI is the foundation of the platform or a feature layer added on top of legacy automation. Torq was built for the former.

Our research found that 92% of security leaders want AI that learns and adapts to attack patterns, which requires an architecture designed for continuous learning from the ground up.

Enterprise-Grade Trust and Governance

Torq provides broad support for role-based, attribute-based, and policy-based access controls, along with strong authentication for administrative users — giving security teams granular control over who can do what within the platform. Global datacenter support enables regional data sovereignty, which is critical for EU customers operating under strict compliance requirements.

And for MSSPs and large enterprises, the platform supports full look-and-feel customization, white-labeling, and tenant isolation — so providers can deliver Torq-powered services under their own brand without exposing the underlying platform to end customers.

In our recent report, 92% of security leaders cited at least one factor that reduces their trust in AI; governance isn’t optional. The number one thing that would build confidence is full transparency into AI decision-making. Torq scored highest in the Leadership Compass for AI decision explainability — the exact capability buyers say they need before they’ll expand AI autonomy.

Financial Stability and Market Momentum

Torq’s recent unicorn-level $1.2 billion valuation is a strength noted by KuppingerCole Analysts because it matters when you’re betting your SOC infrastructure on a vendor’s longevity. Torq has quickly emerged as a market leader, driven by rapid customer growth and expanding revenues, and has acquired a significant number of enterprise customers in a relatively short time.

For buying teams evaluating standalone AI SOC platforms against platform vendors, commercial viability is a real consideration. KuppingerCole Analysts’ inclusion of this as a documented strength signals that Torq has crossed the threshold from promising startup to established market participant.

What This Means for Security Teams

The AI SOC is no longer a concept; it’s a defined market category with a formal evaluation framework, scored vendors, and documented production outcomes. The KuppingerCole Analysts Leadership Compass for AI SOC makes that official. And the data from 450 security leaders in the 2026 Torq AI SOC Leadership Report confirms the urgency: security teams aren’t debating whether AI belongs in the SOC. That adoption argument is over, with 94% already using AI. The conversation is now about accountability and results. Enterprises are seeking an AI SOC platform that actually delivers.

For buying teams, the decision comes down to architecture. Do you extend a legacy system with bolted-on AI capabilities and questionable ongoing investment? Or do you invest in a cloud-native, AI-augmented platform designed for the multi-vendor, multi-signal reality of your security stack?

KuppingerCole Analysts evaluated that exact question across product strength, innovation, and market execution. One platform scored highest in innovation and product leadership, and earned Leader status in every measured category — with production-grade results to back the scores.

Get the KuppingerCole Analysts Report

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

Five Essential Elements of Security for Modern Security Teams in 2026

By Torq

April 15, 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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TL;DR

Modern security failures are caused by tool sprawl, gaps between platforms, and the manual effort required to bridge those gaps.
The five essential elements of security in 2026 are: unified visibility, AI-driven execution at speed, built-in AI governance, structured case management, and continuous measurement.
According to the 2026 AI SOC Leadership Report, 80% of security teams still rely on fragmented point solutions, even though 85% say they’d prefer consolidation.
Structure without speed creates backlogs. The teams winning in 2026 have both.
Security in 2026 is an operational discipline — and the teams winning are the ones investing in AI-driven execution, not just adding more tools.

Why “Security” Looks Different in 2026

Ask most security leaders what keeps them up at night, and they’ll probably mention something about alert volume, talent gaps, or the evolution and speed of attacks. What they’re less likely to say — but should — is this: the biggest security failures aren’t happening because organizations lack tools. They’re happening in the gaps between them.

The modern enterprise security environment is genuinely complex. Hybrid infrastructure, multi-cloud deployments, sprawling SaaS ecosystems, third-party APIs — the attack surface has expanded dramatically, and it’s not shrinking. But the response to that complexity has mostly been to add more tools on top of existing tools. The result? An average SOC runs more than a dozen separate platforms, and most of them don’t natively communicate with each other.

According to the 2026 AI SOC Leadership Report, the average security team is running 7 AI-powered tools alone, and 80% still depend on fragmented point solutions rather than a unified platform. AI is everywhere. Integration is not. That’s the defining security essentials 2026 challenge. Not a shortage of technology but a shortage of execution.

This guide breaks down the five essential elements every modern security program needs to operationalize this year.

1. Unified Visibility Across the Security Stack

Let’s start with the most foundational element, and the one that’s most frequently misunderstood.

Visibility doesn’t just mean having a single pane of glass. And it doesn’t mean consolidating all your data into one SIEM and calling it done. True visibility in 2026 means the ability to see and act on signals across your entire environment — endpoint, identity, cloud, SaaS, network — without those signals living in isolated silos that require manual correlation to be useful.

Here’s why this matters so much right now: modern attacks don’t respect tool boundaries. A threat actor compromises a credential in your identity provider, moves laterally through a cloud environment, and exfiltrates data via a SaaS application. Each of those steps might generate an alert in a different system. If your analysts have to manually connect those dots across three or four separate consoles, the response will be slow — and in security, slow is expensive.

The problem isn’t that organizations lack data. It’s that the data lives in disconnected places, and pulling it together takes time and manual effort that most teams simply don’t have. As Torq’s Field CISO John White writes, the CISO’s job is increasingly about designing human-machine teams that can act on intelligence at the speed the threat environment demands.

Visibility also has to extend across the full security lifecycle. Detection is only one part of it. You need to see what’s happening during investigation, during response, and during post-incident review. If any of those stages is a black box, you can’t improve them.

What operationalizing this actually looks like:

Connecting signals from SIEM tools, EDR tools, identity, cloud security, and SaaS tools into unified workflows that can trigger responses automatically
Ensuring that context follows a case from detection through resolution
Making visibility actionable, not just informational

The Torq AI SOC Platform is built around this premise: it connects to your existing tools and surfaces cross-stack context so your team can see the full picture and respond without toggling between platforms.

2. AI-Driven Execution at Machine Speed — Without Losing Control

Here’s an issue every security leader is navigating right now: the volume and velocity of threats have outpaced human response capacity, but fully autonomous AI makes many teams nervous. Both of those things are true at the same time. And both of them are valid.

Torq Field CISO John White frames it this way in his piece on AI governance in the SOC: the decisions that require human authority are the ones that demand business context — risk appetite, the political environment you’re operating in, the company’s financial situation, and the strategic direction the board is pursuing. That’s the judgment layer. And it’s not an arbitrary line.

The answer isn’t to pick a side. It’s to get smarter about where automation should run fully, where it should support human decision-making, and where humans should always be in the loop.

According to the 2026 AI SOC Leadership Report, 72% of security teams are already comfortable with fully autonomous AI handling incidents of medium severity or lower.

That’s the bulk of SOC volume. The repetitive, high-frequency alerts used to consume hours of analyst time every day. Teams aren’t debating whether to automate that work. They’re figuring out how to do it reliably.

The nuance is in what comes next. Nine in 10 security leaders say they need explainability — the ability to see how AI reached a decision — before they’ll extend autonomy to more complex cases. That’s not AI skepticism. That’s reasonable governance. You wouldn’t sign off on a major business decision without understanding the reasoning behind it.

Security decisions are no different.

The operational takeaway: speed and structure aren’t opposites. The best security automation executes at machine speed on tasks that don’t require human judgment, while surfacing the right context at the right moment for decisions that do. Ad hoc scripts and brittle runbooks can’t do that. AI-driven workflows can.

Torq Hyperautomation™ is designed exactly for this: consistent, auditable execution across your stack, with human oversight built in rather than bolted on. And Torq AI Agents go further — they don’t just plan and reason, they act. They investigate cases, enrich context, and remediate issues across your environment, while your team stays in control of the decisions that matter most.

3. Built-In Governance and Guardrails

Automation without oversight isn’t a security program. It’s a liability.

John White puts it plainly: if AI makes the wrong call and a breach happens, accountability lands with the CISO — not the vendor, not the board. That accountability doesn’t transfer. It sits with you.

Which makes governance not a nice-to-have, but a leadership imperative. And yet it’s genuinely underbuilt in most organizations. Governance in security has traditionally been treated as a compliance function, or something you do before an audit, not something that runs in the background of every security operation, every day. That model doesn’t work when your response workflows are executing hundreds of actions per hour.

The compliance landscape isn’t getting simpler, either. Regulatory requirements continue to expand across industries and geographies. Audit trail requirements are more stringent. Boards want visibility into security operations that most teams aren’t set up to provide. And the consequences of an undocumented, undiscovered automated action taking the wrong step are significant — both operationally and from a liability perspective.

What built-in governance actually means in practice:

Approval workflows for actions that cross a defined risk threshold, so humans are in the loop on high-stakes decisions without becoming bottlenecks on everything
Role-based access controls that define who can modify, trigger, or approve which types of actions
Auditable execution logs that capture not just what happened, but why — what conditions triggered the workflow, what data was used to make the decision, what action was taken, and who reviewed it

This is especially critical as AI agents take on more investigative and response work. The CISO’s role is evolving toward strategic oversight — which means the systems performing operational work need to provide the visibility and accountability that strategic oversight requires.

Governance isn’t a constraint on speed. Done right, it’s what makes speed sustainable.

4. Structured Case and Incident Management

Many organizations don’t have a consistent, structured way to manage security work from detection through resolution. Alerts get triaged. Incidents get worked. But the thread connecting detection to investigation to remediation to post-incident review is loose, manual, and inconsistent.

That’s a problem for a few reasons.

First, it makes it nearly impossible to audit what actually happened during an incident. If the investigation steps aren’t captured in a structured way, reconstructing them after the fact is painful and imprecise — which matters a lot when you’re dealing with regulatory requirements, insurance claims, or executive reporting.

Second, it creates analyst burnout. Without structure, every incident becomes a custom, ad hoc effort. Analysts are reinventing the same processes over and over, context is lost between shifts, and the cognitive load of keeping everything in your head — or in a sprawling Slack thread — is exhausting. Alert fatigue is real, and unstructured case management makes it worse.

Third, it makes improvement nearly impossible. You can’t optimize a process you can’t measure. If you don’t know how long the average investigation takes, where handoffs break down, or which types of incidents consume the most analyst time, you’re flying blind on where to invest.

Torq’s Case Management is built to solve this directly — intelligent case automation and prioritization that gives every security event a predictable lifecycle. Cases are automatically created, enriched, and routed based on incoming signals. AI agents contribute investigation steps and context. Analysts review, decide, and close. The entire thread is captured and auditable.

The principle here is simple: every meaningful security event should follow a structured, repeatable process. Not because process is the goal, but because process is what makes improvement possible.

5. Measurement and Continuous Improvement

If you can’t measure it, you can’t manage it. And if you can’t manage it, you definitely can’t improve it.

This is one of the most consistently underinvested areas in security operations. Most teams have dashboards. Far fewer have metrics that actually tell them whether the program is getting better.

The distinction matters. Alert counts, open ticket totals, and MTTD are popular metrics because they’re easy to measure — not because they’re necessarily the most meaningful. What leaders actually need to understand is whether their security operations are becoming more effective over time: Are we resolving the same types of incidents faster than we were six months ago? Are automated workflows handling a higher percentage of cases without human intervention? Are analysts spending more time on complex investigations and less time on repetitive triage?

According to the 2026 AI SOC Leadership Report, analysts are already spending an average of 8.6 hours per week overseeing AI outputs. That number isn’t inherently good or bad — it depends entirely on whether that oversight is generating better outcomes and shrinking over time as trust in AI execution increases.

Security maturity is not static. Teams that treat their current operating model as the destination rather than a point on a continuum will fall behind. The ones that measure relentlessly, invest in the right areas, and continuously refine their workflows are the ones building programs that scale.

This means building reporting into your workflows from the start, not as an afterthought. It means tracking metrics that reflect execution and outcomes — case closure rates, automation coverage, time to containment. And it means creating a feedback loop where what you learn from incidents drives changes to how you respond to the next ones.

The 5-Point Security Essentials 2026 Checklist

Use this as a quick gut-check on where your program stands:

Do we have connected visibility across all critical security tools, with context that flows between them?
Can we execute response actions consistently and at speed, without relying on manual effort for every step?
Are governance, approvals, and access controls built into our workflows — not reviewed after the fact?
Do we track security incidents in a structured, auditable way from detection through resolution?
Can we measure what’s working, what isn’t, and where to improve — with data, not just instinct?

If the honest answer to any of those is “not really,” that’s where to start.

Security in 2026 is an Operational Discipline

This is the most important reframe for security leaders right now: security success in 2026 is not defined by the number of tools in your stack. It’s defined by how well you can execute across that stack — consistently, quickly, and with the governance and measurement to know it’s working.

The organizations pulling ahead are doing it because they’ve built security programs that can actually operate at the speed and scale the threat environment demands. They’ve invested in AI-driven automation that executes reliably. They’ve structured their case management so nothing falls through the cracks. They’ve built governance in, not bolted it on. And they measure everything.

That’s the autonomous SOC.

Torq is the AI SOC platform purpose-built to help security teams operationalize all five of these security essentials across their existing tools and workflows — without ripping and replacing what’s already working. With Torq Hyperautomation™, AI Agents, and Case Management working together, security teams can close over 95% of security cases autonomously, at machine speed, with full audit-ability.

Ready to see how your program stacks up?

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FAQs

What are the five essential elements of security in 2026?

The five elements are: (1) unified visibility across your security stack, (2) AI-driven execution at machine speed with human oversight where it matters, (3) built-in governance and guardrails, (4) structured case and incident management, and (5) continuous measurement and improvement. Together, these elements define what it looks like to run security operations effectively in an environment defined by complexity, speed, and scale.

Why do security programs fail despite having many tools?

Tool sprawl is one of the biggest and most underacknowledged security risks in enterprise organizations. According to the 2026 AI SOC Leadership Report, 80% of security teams still depend on fragmented point solutions, even as they add more AI-powered tools to the stack. The failures happen in the gaps between tools: in manual correlation, inconsistent response processes, and the lack of a unified workflow connecting detection to resolution.

How can security teams improve operations without replacing their entire stack?

Start by identifying where execution breaks down, not where coverage is lacking. Most teams have adequate detection capability. The breakdowns happen during investigation, triage, and response — where manual steps, inconsistent processes, and tool-switching slow everything down. AI-driven automation and structured case management can layer on top of your existing stack to close those gaps without a full rip-and-replace. Learn more about how the Torq AI SOC Platform works with your existing tools.

What should security leaders actually be measuring in 2026?

Move beyond just alert counts and MTTD. The metrics that matter are the ones that reflect execution and outcomes: automation coverage (what percentage of cases are handled without manual intervention), time to containment, case closure rates by category, and analyst time spent on complex vs. repetitive work.

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

Corey Kaemming, Senior Director of InfoSec

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

Todd Willoughby, Director

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

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AI AI SOC Research

SOC Tool Sprawl: What It’s Really Costing Your Security Operations

By Torq

April 13, 2026

9 Minute Read

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Security operations teams have never had more technology at their disposal… and they’ve never been more overwhelmed by it. The average SOC is now running 7 AI-powered solutions. 10% are managing 10 or more. And across the broader enterprise, organizations deploy an average of 83 security tools from 29 vendors, according to IBM research.

Every one of those SOC tools was added for a reason. Better detection, faster enrichment, smarter alerting. Individually, they deliver value. But collectively, they’ve created a problem the industry is only now beginning to quantify: SOC tool sprawl.

Torq’s 2026 AI SOC Leadership Report — a survey of 450 CISOs and security leaders — puts hard numbers on the cost of that sprawl. 80% of SOC teams rely on disconnected point solutions. 36% cite a “patchwork of multiple tools” as a functional gap. Analysts spend 8.6 hours per week validating AI outputs across those tools. And the teams that can least afford the overhead are absorbing the most of it.

This isn’t a tooling problem; it’s an architecture problem. And it’s getting worse every quarter organizations don’t address it.

What Is SOC Tool Sprawl?

SOC tool sprawl is what happens when security teams continuously add point solutions — each solving a real, specific problem — without a unifying layer to connect them. Over time, the result is an overextended stack where siloed data, overlapping functionalities, and operational inefficiencies compound faster than the tools themselves can deliver value.

The pattern is predictable: A new threat vector emerges. A point solution gets purchased to address it. It works — within its own console. But it doesn’t talk to the SIEM, doesn’t share context with the EDR, and doesn’t feed into case management. So the analyst becomes the bridge, manually pulling data from one tool, correlating it with another, and pasting findings into a third.

Multiply that across seven or more AI tools — each with its own confidence model, alerting format, and severity scoring — and the cost becomes structural. SOC tool sprawl doesn’t just add complexity; it also creates inefficiency. It changes how the SOC operates and not for the better.

The SOC Tool Sprawl Tax: What Fragmentation Actually Costs

The real cost of SOC tool sprawl isn’t measured in licensing fees. It shows up in four places most organizations aren’t tracking.

Oversight hours: Our report found that analysts spend an average of 8.6 hours per week on human oversight of AI-powered outputs. That’s not inherently a problem. AI has taken over the execution layer — processing alerts, enriching data, running playbooks — and analysts have moved into a judgment layer: validating decisions, providing context, and making calls that require institutional knowledge. 9 in 10 security leaders say AI has positively impacted SOC workload, and almost 90% say it’s reduced stress and burnout. The problem is when SOC tool sprawl makes that judgment work inefficient. Disconnected tools produce outputs with different confidence models, formats, and reasoning chains. Instead of spending 8.6 hours on strategic oversight, analysts spend it reconciling conflicting information across siloed dashboards. 37% of security leaders say AI requires too much manual oversight — and that burden scales with the number of tools, not the number of incidents. Consolidate into a single orchestration layer with transparent reasoning, and those 8.6 hours become what they’re supposed to be: high-value, strategic time.
Breach lifecycle: IBM research shows that fragmented stacks take 72 days longer to detect threats and 84 days longer to contain them. When context is scattered across a dozen consoles, the time between “alert fired” and “incident contained” stretches in ways that directly increase breach costs. IBM’s Cost of a Data Breach Report found that organizations using AI extensively cut the breach lifecycle by 80 days and saved $1.9 million on average — but that ROI only materializes when the AI tools are integrated, not fragmented.
Integration maintenance: Data from our AI SOC report shared that 95% of security leaders run multiple tools with overlapping functions, yet fewer than a third have them fully integrated. Every tool added is another API to maintain, another update cycle to manage, another integration that can break when a vendor pushes a change. For SOC teams already stretched thin, integration maintenance becomes a permanent tax on engineering capacity that never appears in the budget.
Skill gaps: The more tools a team runs, the harder it becomes for analysts to be proficient with each one. Suboptimal tool usage — where capabilities aren’t fully leveraged — weakens the overall security posture. The paradox of SOC tool sprawl is that buying more tools can make you less secure, not more.

Why SOC Tool Sprawl Hits Lean Teams the Hardest

The teams with the fewest resources bear the highest fragmentation costs and have the least capacity to address them.

The 2026 AI SOC Leadership Report found that smaller teams — 15 or fewer — are twice as likely to default to legacy automation: 30% compared to 15% for teams of 35 or more. Not because they prefer legacy tools, but because switching costs feel prohibitive when you’re barely keeping up with the queue.

Except the cost of staying put isn’t static. It’s growing. 44% of lean SOC teams say false positives are reducing their trust in AI, compared to 28% of larger teams. With fewer analysts to absorb the noise, fragmentation doesn’t just slow the team down — it actively erodes confidence in the tools themselves. SOC tool sprawl becomes a staffing problem, not because they don’t have enough people, but because their people are spending time managing tools rather than managing threats.

How SOC Tool Sprawl Erodes Trust in AI

The trust gap in AI-powered security operations is one of the most discussed challenges in the industry. 92% of security leaders cite at least one factor that reduces their trust in AI. The conversation usually frames this as an AI problem — the models aren’t good enough, the outputs aren’t reliable, the technology isn’t ready.

Our data tells a different story. The issue isn’t whether AI works. It’s whether the architecture around it lets teams verify that it does.

When AI outputs come from so many different systems with so many different confidence models, analysts have no consistent baseline to calibrate trust against. There’s no single source of truth. Each tool has its own alerting format, its own severity scoring, and its own enrichment logic. An alert that scores high-severity in one tool might not even surface in another. Analysts can’t build trust in AI when the AI itself is fragmented across systems that don’t talk to each other.

This creates a self-reinforcing cycle: more tools generate more outputs that require more validation. More validation means more oversight hours. More oversight hours mean analysts feel less confident in AI — because they’re spending all their time checking it instead of benefiting from it. And when trust stays low, teams add another tool to fill the gap that the last one created. The sprawl feeds itself.

37% of security leaders say AI requires too much manual oversight. That’s not a statement about AI’s capability. It’s a statement about what happens when you deploy AI across seven disconnected systems and ask a human to be the integration layer between them.

How to Fix SOC Tool Sprawl: What 85% of Security Leaders Want

The survey asked security leaders what would fix this. The answer wasn’t “fewer tools.” 85% want a unified AI SOC platform. Not one tool that replaces everything. One platform that connects to everything.

That distinction is critical. Nobody is asking to rip out their SIEM, their EDR, their identity tools, or their cloud security posture management. Those tools exist because they solve real detection and protection problems. What’s missing is the layer that sits across all of them — correlating, enriching, and orchestrating so the SOC operates as one system instead of seven disconnected ones.

More than half say unification alone would resolve their trust issues with AI. The trust problem isn’t the AI. It’s the architecture. Give them a single orchestration layer with consistent context, unified case management, and one place to validate AI decisions — and the trust follows.

This also explains why the lean-team trap is so persistent. The teams running four people and multiple tools aren’t going to do a forklift migration. They can’t afford the downtime, the retraining, or the risk. What they need is a platform that lets them consolidate at their own pace — bringing tools into a single orchestration layer without ripping anything out. Integration over replacement. Unified and flexible, not one or the other.

The organizations that figure this out first won’t just reduce complexity. They’ll turn the 8.6 hours per week that their analysts spend on AI oversight from fragmented busywork into strategic judgment time. They’ll break the cycle where low trust drives more tools, which drives lower trust. And they’ll give lean teams the operational leverage to compete with SOCs several times their size — not by adding headcount, but by eliminating the fragmentation tax that’s consuming the headcount they already have.

The Cost of Ignoring SOC Tool Sprawl

Seven or more AI tools. 8.6 hours a week in oversight. 80% reporting operational complexity. The teams that need help most are the least likely to make a change, and the fragmentation compounds every quarter they wait.

The cost of SOC tool sprawl is measurable in hours lost to validation, trust eroded by inconsistent outputs, and incidents that take longer than they should because context lives in five different tabs. It shows up in analyst burnout, in MTTR that plateaus no matter how many tools you add, and in the growing gap between what AI can do in theory and what teams actually let it do in practice.

What 450 security leaders are asking for isn’t complicated. It’s a platform that connects to everything they already have, gives them a single place to triage, investigate, and respond, and lets their AI operate as a single system rather than a collection of competing ones.

The data says 85% want it. The question is how long they’ll wait.

Read the 2026 AI SOC Leadership Report

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 AI SOC Research Security Automation 101

Mastering SOC Automation in 2026: Beyond the Basics

By Torq

April 8, 2026

7 Minute Read

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TL;DR

94% of security teams already use AI in the SOC, but the average team runs 7 disconnected tools — adoption has outpaced architecture.
The three core problems holding teams back are fragmentation, eroding trust, and oversight that hasn’t scaled with automation.
The gap between confidence and actual AI use is stark: 97% of leaders believe AI can handle triage, but only 35% are using it for that.
Mastering SOC automation in 2026 means moving from tool accumulation to platform unification — with adjustable autonomy that lets teams set the terms.

The AI SOC has arrived.

According to the 2026 AI SOC Leadership Report, 94% of organizations are using AI in the SOC in some capacity. The question in 2026 is no longer whether to adopt AI-driven SOC automation, but rather how to do so. Is the architecture behind that adoption actually working?

For most teams, the honest answer is: not yet. The average SOC runs 7 AI tools. Analysts are spending 8.6 hours a week just overseeing AI systems. And 92% of security leaders say at least one factor is reducing their trust in AI. The tooling is there, but the outcomes aren’t keeping up.

This is the challenge of mastering SOC automation in 2026, and it has less to do with buying more technology than with rethinking how the technology you already have fits together.

The Adoption Ceiling: More AI, Not Better AI

Security operations teams have moved fast on AI. The report found that 79% of organizations have adopted generative AI and large language models inside their SOC, making them the leading category of AI in use. On the surface, that looks like progress.

But adoption type matters. 76% of teams are still running first-generation AI built around high alert volume and rule-based detection — systems designed for a world of known threats, not adaptive ones. 73% rely on AI optimized for precision over speed.

These tools aren’t wrong, but they represent an earlier generation of capability. The teams seeing better outcomes meaningfully are the ones that have moved to agentic AI and AI-native platforms: systems that can reason through context, chain investigative steps together, and take goal-directed action rather than just flagging anomalies for humans to sort.

This is the maturity curve the market is currently on. Adoption was the first phase. Architecture is the next one. The teams that treat those two things as the same problem are the ones still grinding through alert queues despite having more AI than ever.

The Fragmentation Tax: When Analysts Become the Integration Layer

80% of SOC teams rely on disconnected point solutions, and they say that fragmentation creates significant operational complexity. 36% identify it as a functional gap, not just an inconvenience.

The real cost isn’t measured in tool licenses. It’s measured in analyst time. When your SIEM doesn’t talk to your EDR, and your EDR doesn’t talk to your identity provider, the analyst becomes the integration layer — manually pulling context from five different consoles to investigate a single alert. That’s not analysis; that’s data entry. And it’s happening at scale across most SOCs right now.

Smaller teams feel this most acutely. 44% of lean SOC teams say false positives are eroding their trust in AI, compared to 28% of larger teams. With fewer analysts available to absorb the noise, fragmentation doesn’t just slow the team down; it actively erodes confidence in the tools themselves.

What a majority of security leaders say they want, according to the report, isn’t a single monolithic tool that does everything. It’s one platform that connects to everything: a unified layer that pulls context from across the stack, correlates it intelligently, and delivers enriched, actionable cases rather than raw alerts. That distinction matters. AI SOC automation done right isn’t about replacing your entire toolset; it’s about making the tools you have work together instead of against each other.

The Trust-Autonomy Paradox: Confidence Without Action

Here’s the most revealing data point in the report: 97% of security leaders are confident that AI can handle alert triage. Only 35% are actually using it there.

That gap is not a knowledge problem. It’s a control problem.

Most AI SOC tools offer a binary: the AI runs autonomously, or the human runs manually. What’s missing is a dial — the ability to set autonomy levels based on alert severity, confidence threshold, and organizational risk tolerance. A team might be fully comfortable letting AI auto-close low-severity, high-confidence alerts. They might want human review before any containment action on a critical asset. Those are different settings, not different tools.

72% of leaders say they’re only comfortable with AI autonomy for medium-severity alerts and below. That’s not a failure of trust in AI; it’s a reasonable position for any team accountable to a board and a compliance framework. The platforms that unlock greater autonomy over time are the ones that make it adjustable rather than all-or-nothing.

Where human authority sits within AI governance is increasingly a design question, not just a policy one. The teams building the most capable AI SOC operations in 2026 are the ones that have thought carefully about which decisions belong to AI, which belong to humans, and how that line shifts as trust is established.

Reframing Oversight: From Burden to Strategic Function

8.6 hours a week on AI oversight sounds like a problem. But 9 in 10 security leaders say AI is positively impacting their team’s workload. Those two data points can coexist — and understanding why is important.

Oversight in a well-functioning AI SOC is not the same as babysitting brittle playbooks. It’s analysts reviewing AI decisions, tuning confidence thresholds, identifying edge cases, and building the institutional knowledge that makes the system smarter over time. That’s high-value work. It’s a very different job from manually triaging 500 alerts a shift.

The question isn’t how to eliminate oversight. It’s about making oversight strategic. That requires two things: transparent reasoning, so analysts can actually understand what the AI did and why, and adjustable autonomy, so the system gets more latitude as it earns trust. The evolving AI SOC org chart reflects this shift: AI governance.

Teams that architect for this transition now will have a significant operational advantage over those still designing SOC workflows around manual processes.

What the Market Has Already Decided It Wants

The 2026 AI SOC Leadership Report doesn’t just diagnose the problems — it shows a clear picture of what security leaders are asking for. The top-ranked AI SOC capabilities across respondents were:

Continuous learning: #1 ranked capability across all respondents
Explainability: 90% say the ability to understand AI reasoning is critical
Full platform integration: 91% cite this as a core requirement
Unified platform preference: 85% would choose a single integrated AI SOC over multiple point solutions

And perhaps the clearest signal of all: 53% say a fully integrated AI SOC platform would directly resolve their trust concerns. Not more AI. Not better individual tools. Integration and explainability, working together.

The market has clearly described what it wants. The architectural requirements are clear. The capability gaps are documented. The only remaining question is which platforms are actually built to close them and which are still layering AI on top of legacy infrastructure and hoping for different results.

Where the Torq AI SOC Platform Fits

The Torq AI SOC Platform is built around the architecture that the market has described. Specialized AI agents handle triage, investigation, enrichment, and remediation autonomously — connected across your full security stack, not siloed within it. Every action is logged with full reasoning, so oversight is informed rather than reactive. And autonomy is configurable: teams set the terms based on severity, confidence, and risk tolerance, then expand AI authority as trust is established over time.

This isn’t automation bolted onto legacy architecture. It’s AI-native SOC automation designed for the way modern security operations actually work — where the goal isn’t to run more tools, but to make the right decisions faster, with less friction, at a scale no human team can match alone.

The 2026 AI SOC Leadership Report makes one thing clear: the teams that master SOC automation this year won’t be the ones with the most AI. They’ll be the ones who built the right architecture around it.

Ready to get the full picture on the AI SOC from 450 CISOs and security leaders?

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FAQs

If AI adoption is so high, why aren't SOC outcomes improving?

Because adoption has outpaced architecture. Most teams are running 7 disconnected AI tools, and 80% rely on fragmented point solutions. When tools don’t talk to each other, analysts end up as the integration layer — manually pulling context across consoles instead of doing real analysis.

Why aren't more teams using AI for alert triage?

It’s a control problem, not a confidence problem. 97% of leaders believe AI can handle triage, but only 35% are using it there. Most tools offer a binary — fully autonomous or fully manual — when what teams actually need is adjustable autonomy based on alert severity, confidence, and risk tolerance.

What would most improve trust in AI SOC tools?

Explainability and integration. 90% say understanding how AI reaches its decisions is critical, and 53% say a fully integrated platform would directly resolve their trust concerns. The ask isn’t more AI — it’s AI that shows its work, connected across the full stack.

What does mastering SOC automation actually look like in 2026?

It means moving from tool accumulation to platform unification — with agentic AI that can reason through context and take goal-directed action, adjustable autonomy that expands as trust is earned, and oversight that’s strategic rather than reactive.

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

How AI SOC Operations Are Reshaping Security Teams in 2026

By Bob Boyle

April 1, 2026

10 Minute Read

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I recently sat between two people who think about the AI SOC operations from completely different angles — and spent 50 minutes watching them land in the same place.

Leonid Belkind builds the technology. He co-founded Torq, serves as CTO, and spends his days translating between the market, our customers, and the engineers who build the product. John White spent 20 years on the operational side, most recently as CISO at Virgin Atlantic, where he deployed Torq before crossing over to become our Field CISO. When Leonid talks about what agentic AI can do, John talks about what happened when he actually turned it on with half the headcount he needed.

What I expected was a technology discussion. What I got was a conversation about fear, trust, speed, and why the next six to nine months might be the most important window security leaders have ever faced.

Their thesis: the window to deploy agentic AI in the SOC before machine-speed attacks become the norm is roughly six to nine months. The teams that start now — even on a small scale — will be the ones that thrive. The teams that wait will be the ones that get hit.

Here’s the full recording if you want the unfiltered version. But these are the moments that stuck with me.

The Threat Landscape Has Shifted. AI SOC Operations Haven’t Caught Up.

The conversation started where every SOC conversation starts right now: attackers are moving faster than defenders, and the gap is widening.

Leonid brought up VoidLink, a malware framework that compressed months of attack development into days. But the point wasn’t VoidLink specifically. It was what VoidLink represents. Malicious actors don’t sit through vendor evaluations. They don’t need compliance sign-off or procurement cycles. They grab what’s available and move. Tools that required state-sponsored resources a few years ago are accessible to anyone now.

“The phrase ‘bringing a knife to a gunfight’ hasn’t come from nowhere,” Leonid said. “This thing is happening. If you’re not there, you’re just so ill-equipped to face the challenges it poses.”

That set the tone for everything that followed. Because if the threat landscape has fundamentally shifted — and both of them believe it has — then every stage of AI SOC operations needs to shift with it.

“We certainly can’t use traditional methods as CISOs to address a new risk. That’s the definition of insanity: trying to do the same thing to get a different outcome.”

– John White, Field CISO at Torq

His read: VoidLink isn’t an outlier. It’s just the start.

Triage: The Easiest Win and the Most Overdue

When we moved into the threat lifecycle, Leonid made the case that triage is the most obvious place to start and the place where delay is least defensible.

His reasoning was straightforward. Triage sits at the top of the funnel, facing the highest volume of incoming signals. Detection systems often lack context. Waiting for perfect fidelity means being too late. And the humans doing this work? They’re not great at it. Not because they lack skill, but because the job demands consistency and speed at a scale humans physically can’t sustain.

“Bob, you’re wonderful,” he told me, “but if I give you 1,000 assignments at the same second, no matter how wonderful you are, that’s not your best quality.” Fair point.

Agentic AI doesn’t get decision fatigue. It doesn’t take breaks. It handles non-uniform data and drives toward outcomes without someone having to write a playbook for every scenario. In Leonid’s view, triage was overdue for automation before agentic AI even existed. Now there’s genuinely no excuse.

John brought the human angle. The first thing he sees when AI handles triage is happier staff. “From a CISO’s perspective [when AI for triage is deployed], when you look out at your team, they don’t seem overwhelmed. They’ve got much more time to apply a quality approach.” He emphasized that analysts aren’t unhappy because they dislike security; they’re unhappy because they’re not doing security work. They’re drowning in noise instead of solving problems.

The shift from reactive to proactive is only possible when analysts aren’t buried. “There’s nothing worse than an overwhelmed team trying their best but still not being able to achieve the outcomes they want.”

The takeaway: If you’re not automating triage yet, this is where to start. The risk is low, the ROI is immediate, and the analyst experience improvement alone justifies the investment.

Investigation: The Glass Ceiling Has Broken

Investigation is where the conversation really got interesting and where both speakers argued the market has underestimated how far agentic AI has come.

Leonid drew a parallel to software engineering. A year ago, copilots suggested code. Now tools like Cursor refactor entire applications. A similar leap has happened in security investigation.

“You as a human should be the copilot,” he said. “The copilot in a real flight is the person supposed to be fresh, up for it, there for escalation scenarios.” AI handles the evidence gathering, enrichment, correlation, and even inference — drawing conclusions, making risk scores, assembling timelines. The analyst steps in for judgment, not grunt work.

He shared a compelling example. Torq’s Director of Strategy — a former head of security operations at a regulated enterprise — tested an investigation exercise he used to give Tier 2 analyst candidates. Human analysts typically took half a day across multiple tools to produce findings with full evidence and timelines. An autonomous AI investigation, crunching the same hundreds of thousands of logs, completed it in under 6 minutes, producing more detailed findings than humans typically produce. Same data, same exercise, apples to apples. Leonid called it “an Archimedes ‘eureka’ moment.”

John focused on what pre-built cases mean operationally. When an analyst receives a case that’s already enriched and contextualized, two things happen: they move faster and with less bias. “In the SOC, having done the role for a long time, you start to build up preconceived ideas of what things look like. The advantage of having AI do that for you is that it’s unbiased.”

He tied it back to his exposure window framework — the time during which attackers operate. “If you can reduce or even remove that exposure window, you’re going to mitigate the threat pretty quickly. You’ve got one answer, one thing you can trust, a definitive way forward, and then you can move into action.”

The takeaway: Investigation is no longer a “human-only” phase. The teams treating it that way are operating with a capability gap that widens every month. Agentic AI doesn’t replace analyst judgment; it gives analysts something worth judging, in minutes instead of hours.

Response: Where AI SOC Operations Get Uncomfortable — and Where They Matter Most

The response phase was the most charged part of the conversation, and the part that makes or breaks the entire AI SOC argument. Because if you speed up triage and investigation but leave response at human speed, your AI SOC operations haven’t closed the loop.

Leonid didn’t mince words: “Many founders start their pitch by saying, ‘Put it in detect-only mode, and then as you gain confidence…’ But as a founder of a security operations company, if you haven’t responded, at best you haven’t done much.”

His argument: leaving containment actions — quarantining endpoints, blocking network traffic, suspending identities — to human speed during active exploitation means deeper organizational exposure. The barrier isn’t technological. It’s psychological. And it cuts both ways: “Are humans 100% trustworthy? They don’t have lapses in judgment? They don’t accidentally push the wrong button?”

John balanced this with practical reality. CISOs are comfortable with automated triage and investigation. Response is where they hesitate and that hesitation is risk-based, not irrational. The answer isn’t to leap blindly. It’s to start small.

At Virgin Atlantic, John never had abundant resources. The operation was 24/7/365, safety-first. He couldn’t afford human lag. So when deploying Torq in his SOC, he started with a handful of use cases, built trust with the team, and expanded from there. “Within the first four or five use cases, starting small, I was still saving 40 hours a week within the team. That’s a whole analyst’s working week.”

His advice: “Start small, build the trust, and then take AI through the tiers. The more you speculate, the more you accumulate.”

The takeaway: Automated response is where the value compounds but it requires earned trust, not blind faith. Start with low-risk containment actions, prove the guardrails work, and expand. The teams that never start are the ones carrying the most risk.

The SOC That Learns Over Time and the Teams That Restructure Around It

The final section went over the future of the SOC as an organization. Leonid went deep on how AI agents actually learn: semantic knowledge (facts about your environment), procedural knowledge (how things get done), and episodic knowledge (memories of what worked and what didn’t). Each maps to a specific AI technique — from in-context learning for environmental awareness, to reflective prompt evolution for refining procedures, to methods like LoRA for deeper model adaptation. The key insight: most AI learning in security operations happens without retraining the model.

John took the strategic view. Looking back at 2025’s high-profile attacks, detection wasn’t the failure — the gap between detection and action was. AI attackers set an intent and let the model figure out the how, making them unpredictable in ways that static defenses can’t match.

His vision for the AI SOC in 2026 goes beyond technology.

“AI doesn’t just change technology. It’s going to change the way security teams work — how we structure teams, the roles we assign, the execution we give up to AI so we can concentrate on designing outcomes and judging performance.”

– John White, Field CISO at Torq

He introduced the concept of the agentic workforce — taking existing analyst roles (a vulnerability management analyst, for example), mapping the tools and processes they use, and gathering them into an agentic persona. Not replacing the human. Redefining what the human does.

“CISOs should be expecting constant and consistent delivery. That’s what AI brings. You don’t have to wait for someone to turn up to work.”

One moment that stuck: a Torq customer told John he “got his Christmas back” because automation changed the team’s shift patterns. Escalations still come to humans out of hours but the first phases run at machine speed regardless of who’s on shift.

The takeaway: The AI SOC doesn’t just change your technology. It changes your org chart, your shift patterns, your hiring profile, and what “analyst” means. The teams thinking about this now will adapt. The teams that aren’t will be restructuring reactively after the next major incident.

The AI SOC Operations Playbook: The Window Is Closing

John closed with urgency. “Don’t fear AI. Embrace AI. At the moment, there is still the opportunity to get ahead of the curve, but that window is closing. I’d say we have maybe 6 to 9 months before machine-speed attacks really start becoming commonplace. Those who have adopted an agentic approach will thrive. Those that haven’t — they’re going to be the companies that get hit.”

Leonid’s closing was equally direct. Responsible adoption is possible. The guardrails exist. The industry learnings are sufficient. The only remaining question is whether you act on it.

Here’s the practical path both speakers laid out for transforming AI SOC operations:

Start with triage. Lowest risk, highest volume, most immediate ROI. Get analysts out of the noise.
Expand into investigation. Let AI build the case. Let analysts make the call. Compress the exposure window from hours to minutes.
Earn your way into response. Start with low-risk containment actions. Build trust. Expand the scope as confidence grows. Don’t skip this step.
Think beyond technology. Start designing agentic roles. Map existing analyst workflows to agent personas. The org structure that works in 2026 isn’t the one you have today.

“[With AI in the SOC], we can’t wait for perfect,” John said. “It’s going to be ever-evolving. The most important step is just to get on the journey.”

Watch the Full Conversation

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

Corey Kaemming, Senior Director of InfoSec

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

Todd Willoughby, Director

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

Phillip Tarrant, SOC Technical Manager

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

Gai Hanochi, VP Business Technologies

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

Dina Mathers, CISO

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

Yossi Yeshua, CISO

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AI AI SOC

The CISO’s Role Is Rapidly Changing

By John White

April 1, 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 that, he built and transformed security functions for global organizations, including ASOS, Liberty Global, AEG Europe, and KPMG.

AI isn’t just reshaping the threat landscape or how we defend against attacks; it’s redefining what leadership in security looks like. The CISO of the near future is less a chief technologist and more a strategic architect of business outcomes, designing human-machine teams that reimagine the target operating model in response to both risk and opportunity.

I want to dwell on that last word for a moment. Opportunity. We talk endlessly about risk in this industry, and for good reason. But we don’t talk nearly enough about the opportunity sitting right in front of us. For the first time in my career, CISOs have an enabler that can take a strategic vision from concept to operations, end-to-end, faster and more securely than ever before. That’s not a risk to manage. That’s an extraordinary moment to seize.

This piece is about what that means in practice for CISOs — for the role, for the skills we need to develop, and for the mindset we need to let go of. Some of it I’ve learned from watching the industry shift in real time. Some of it I’ve learned the hard way in the trenches. And some of it I’ve only realized after stepping out of an operational role and gaining an outside perspective as what I call “a recovering CISO.

What Does “Strategic Architect” Actually Mean?

There have been lots of technology waves in security — on-prem to cloud, SaaS, zero trust. Each one changed how we worked. But the AI wave is different in kind, not just degree. Quantum will have its own impact, but AI does something quantum doesn’t: it builds things for you. That’s a fundamentally different proposition for a CISO.

Historically, you put together your strategy — risk reduction targets, maturity gains — and executed it over a steady two- or three-year change program. You needed armies of people with specific skill sets. The gap between strategic intent and operational reality was measured in months, sometimes years.

Agentic AI is closing that gap.

With the right AI tooling, CISOs can articulate intent in natural language and have autonomous systems build, deploy, and iterate the operational response. Auto-triage events. Enrich and prioritize cases. Investigate and resolve incidents. What once took months now takes days or hours. And the kicker: you no longer need to depend on large teams of skilled resources to deliver it.

The day-to-day changes fundamentally. It’s no longer about managing activity. It’s about leading agentically — articulating intent, shaping outcomes, and building an organization capable of autonomous, agile execution.

Gone are the days of long, rigid three-year plans. The model is shifting: agree on an outcome, execute over a short sprint, come back to senior leadership with what you’ve built, review together, iterate, and go again. It’s a product lifecycle, not a security program. CISOs are becoming more product-focused, more like marketers, constantly selling a vision and delivering it in pieces.

The greatest skill a CISO can develop right now is the ability to articulate intent clearly and pivot fast. Everything else follows from that.

Two Starting Points, One Destination

I’ve worked on both sides of the Atlantic, and the regional differences in how CISOs are approaching this shift are real:

U.S. CISOs have typically had greater freedom to experiment — with higher risk tolerance, faster technology adoption, and earlier moves toward automation-first models. They try things, swap them out if they don’t stick, and move on. Less governance bureaucracy, more speed.
In EMEA, the starting point has been different. Regulation, data protection, and supervisory scrutiny drive a more cautious, governance-first mindset. CISOs there prioritize control and defensibility before innovation. Investments are more measured. The instinct is to get it right the first time and maximize the return on every dollar spent.

Neither approach is better. They’re different responses to different environments.

But AI is forcing convergence. U.S. leaders are realizing that agentic security without strong governance doesn’t scale safely. EMEA CISOs are recognizing that manual, people-heavy models can’t meet regulatory expectations at speed or scale. Automation is no longer optional; it’s becoming a prerequisite for compliance, resilience, and cost control.

The result is a shared destination from different starting points: security organizations that are outcome-driven, automated by default, and governed by design. The U.S. needs to think harder about governance. EMEA needs to shift from resilience-first to bolder, more innovative moves. Both are on the same journey.

The Skills Nobody Trained Us For

If I were mentoring someone who wants to be a CISO in five years, here’s what I’d tell them. And almost none of it maps to traditional career development.

First of all, don’t become a CISO. I’m joking. Mostly.

Agentic and AI systems literacy is non-negotiable. You need to be genuinely literate in the agentic world, not just aware of it. Keep up with emerging technologies, understand how things are being built, and know the movers and shakers. If you don’t understand how agentic systems work, you can’t re-architect a target operating model around them. You need enough depth to be an intelligent buyer, governor, and architect, even if you’re not building.

Product ownership mentality over technical depth. Think like a product owner, not a program manager. Shorter cycles, continuous iteration, outcome-based delivery. Think unified platform, not individual tools in silos. You can’t have silos of people and silos of tools and expect it to scale. The security organization of the future is a platform that integrates your existing stack while automating tasks that would otherwise require human intervention — which is exactly what the 2026 AI SOC Leadership Report found that 85% of today’s security leaders want: a unified, end-to-end AI SOC platform.

The ability to articulate intent and translate it into business outcomes. This surprises people the most. You no longer need deep technical knowledge to be an effective CISO. What you absolutely need is the ability to define what success looks like, communicate it in terms the board understands, and evangelize it across the organization. The modern CISO is more of a marketer than an engineer. You need a vision, and you need to keep selling it as you deliver it piece by piece.

Governance of autonomous workforces. As we create machine identities with real authority — for containment decisions, incident resolution, and workflow execution — we need governance models for them. How do hybrid human-machine teams operate? Who’s accountable when the machine gets it wrong? These are questions we were never trained for, and we need to start answering them now.

What I Had to Unlearn

I describe myself as a “recovering CISO.” That’s not a punchline; it’s an honest acknowledgment of what stepping away from 20-plus years of operational readiness actually feels like.

As CISOs, we like to keep a very tight grip on things. If we’ve got a grip, we can control it. Control means protection. That instinct gets deeply wired in. The phone rings at 3am and you’re already running through the response before you’re fully awake. Working weekends becomes normal. Getting pulled into every significant incident, every escalation? That’s just the job.

That constant readiness is hard to shake off. Even now, I catch myself with the operational muscle memory — the reflex to want to be in the room, the discomfort of not knowing exactly what’s happening on the front line. That’s why I call it ‘recovering’. I’m still pulling away.

But the distance has given me something valuable: the headspace to think about what security leadership actually means when you’re not drowning in operational noise. And what I see clearly now is that the tight operational grip, as much as it felt like protection, is also what holds CISOs back.

With autonomous and agentic delivery, we need to get comfortable releasing that grip and letting machine-led execution take its place. That’s not losing control. It’s reallocating where human judgment adds the most value. The machine handles execution. Humans handle intent, governance, and contextual judgment that AI can’t replicate.

CISOs still in the role will need to make the same mindset shift without the luxury of stepping back to reflect. The ones who do it well will thrive. The ones who stay stuck in their ways will be in survival mode.

The Pivot That Changes Everything

Ultimately, everything comes down to one fundamental shift — from controls to outcomes.

Think about how we’ve historically measured success. Risk scores. Maturity assessments. Compliance certifications. Patch percentages. These are measures of activity and operational hygiene. They’re not useless, but they’re no longer sufficient.

There’s a new target operating model built on three distinct layers:

Outcomes: What the organization is trying to achieve, in business terms
Execution: Where automated and agentic capabilities deliver at scale, at machine speed
Judgment: Where human oversight, context, and accountability are applied where they genuinely matter

When you design this model properly, the things CISOs have always cared about become byproducts. Risk reduces, compliance follows, maturity improves. Not as the sole focus, but as the natural consequence of building something that actually works at the speed the threat landscape demands.

We need to rethink what success looks like. Not the next rung up the maturity ladder. Not the next compliance certification. But have we equipped the organization with a platform that can address future threats faster than before? Are we agile enough to adapt when the landscape shifts again… which it will?

Maintaining the norm is not an option. No one is going to thank you for a clean compliance scorecard if you’ve been hit by a machine-speed attack and couldn’t respond because you hadn’t built a machine-speed defense.

The CISO role is changing. Not incrementally but fundamentally. The question isn’t whether it will change. It’s whether you’ll change with it.

Want the data behind the shift? 450 security leaders weighed in.

Download the 2026 AI SOC Leadership Report

Read the rest of John’s blog series about AI in the SOC:

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 Research

The 2026 AI SOC Leadership Report: What 450 Security Leaders Told Us

By Ofer Smadari, CEO & Co-Founder

March 30, 2026

4 Minute Read

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When we started building Torq four years ago, we had a thesis: the SOC was broken, and automation — real automation, not another tool bolted onto the stack — was the way to fix it. AI has since changed the game entirely. But has it streamlined the SOC, or introduced new complexity?

We wanted to find out. We partnered with Sapio Research to survey more than 450 CISOs and SOC leaders across four countries.

The short answer: AI is everywhere. It’s delivering real value. And it’s creating a new set of problems that nobody planned for.

AI Works. The Way It’s Deployed Doesn’t.

I’ll start with the good news, because there is plenty of it. 90% of security leaders say AI has positively impacted SOC workload. 85% say it’s reduced stress and burnout. 83% agree their AI tools deliver on vendor promises. That’s not a market that’s disappointed with AI. That’s a market that’s seen what it can do.

But underneath those numbers, a more complicated picture is emerging. The average SOC is running 7 AI-powered tools. 80% still rely on fragmented point solutions rather than a unified platform. And 92% of leaders cite at least one factor actively reducing their trust in AI.

This is the paradox we keep hearing in every customer and prospect conversation: AI is working, but the way it’s been deployed — tool by tool, vendor by vendor — is creating the same complexity it was supposed to eliminate.

5 Findings from 450 Security Leaders

We organized the findings around five themes that surfaced consistently across geographies, company sizes, and seniority levels.

1. AI Is Everywhere in the SOC, But Unified Nowhere

Teams are running 7 tools with AI on average, but 80% depend on disconnected point solutions. 85% say they’d prefer consolidation. The tools have multiplied. The integration between them hasn’t. This is the finding that hit closest to home for me; it’s the exact problem we set out to solve when we founded Torq.

2. AI Is Carrying the Load; Analysts Are Making the Calls

72% of teams are comfortable with fully autonomous AI on medium-severity incidents and below — the alerts that make up the bulk of SOC volume. Analysts aren’t being replaced. They’re being freed up for the work that actually requires human judgment.

But to push autonomy further, 9 in 10 say they need to see how AI reaches its decisions before they trust it. I hear this constantly from CISOs: “I’d let AI do more if I could see why it’s doing what it’s doing.”

3. The Analyst Role Is Evolving

Analysts spend an average of 8.6 hours per week overseeing AI outputs. That sounds like a problem… until you see that 9 in 10 say AI has positively impacted their workload. Those hours aren’t busywork. They represent a role shift from execution to judgment. This is the future of the SOC analyst: not replaced by AI, but elevated by it. AI handles the processing; analysts make the calls that matter.

4. Trust Is the Limiting Factor on AI Expansion

92% of security leaders cite at least one barrier to trusting AI in the SOC — from data privacy to black-box decision-making. And the #1 thing that would change that? Transparency. 46% say the ability to see how AI reaches its conclusions would be the single biggest confidence booster.

Not more features. Not more AI. Just show AI that shows its work. We took this to heart early at Torq; explainability isn’t a feature we added. It’s how we built the platform.

5. The Market Knows What It Wants

85% of security leaders would prefer a unified AI SOC platform over managing multiple point solutions. 92% say AI must continuously learn and adapt to evolving attack patterns. The desired end state is remarkably consistent across every seniority level, company size, and geography: unified, explainable, and adaptive. This data validates the architectural bet the entire industry needs to make.

What This Means for the Security Industry

97% of CISOs and security leaders are confident AI can handle triage. Only 35% are actually using it there. That gap keeps me up at night — not because teams lack ambition, but because their tools aren’t giving them a way to act on it. Teams won’t extend AI into high-stakes functions unless they can set autonomy thresholds, see how decisions are made, and adjust as confidence grows.

The organizations that close this gap first will be the ones that unlock what AI in the SOC was always supposed to deliver.

That’s what we’re building. This report shows why it matters.

Get the Report

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 Torq Delivers with Google Cloud

Built for the AI SOC

Getting Started with the Integration

Better Together: What Torq’s Collaboration with Google Cloud Can Help Unlock for Your SOC

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