SOAR vs. AI SOC: What’s the Difference and Why You Should Move

If you’ve been in security operations for more than a few years, you’ve lived through the automation hype cycle at least twice. First, SIEM was going to solve everything. Then SOAR picked up what SIEM left open. Now AI SOC platforms deliver on the promise SOAR set out to keep.

Each wave solved real problems. SOAR gave teams a way to connect their tools and standardize response, and it also brought maintenance demands that grow over time: static playbooks that need updates when APIs change, custom scripting that only two people on the team understand, implementations that take 12-18 months to show ROI, and a coverage ceiling near 30-40% of alert volume, no matter how many engineering hours go in.

GigaOm recognized this shift when it renamed its SOAR Radar the SecOps Automation Radar in 2025, because the category itself has evolved. The Torq AI SOC Platform combines agentic reasoning with deterministic Hyperautomation execution to handle the scenarios that static playbooks never reached. GigaOm has named Torq a Leader and Outperformer in that report for three consecutive years, citing its Hyperautomation capabilities.

This guide answers the questions security leaders ask most: what SOAR and an AI SOC are, what legacy SOAR costs, how the two compare, what ROI to expect, and what happens to your existing playbooks.

What Is SOAR?

SOAR (Security Orchestration, Automation, and Response) is a category of security tools that connects a SOC’s tools and runs predefined response playbooks across them. It emerged around 2015 to solve a specific problem: SOC analysts were drowning in manual, repetitive tasks across disconnected tools.

SOAR does three things. It orchestrates actions across your security stack (e.g., firing an API call to your EDR, updating a ServiceNow ticket, or sending an email notification). It runs predefined response playbooks (e.g., for a phishing alert: extract IOCs, check reputation, quarantine the email). And it collects and organizes investigation data.

That model worked well while the threat landscape moved slowly enough for playbooks to keep up. Machine-speed attacks now call for more flexibility.

Every playbook needs someone to build, test, and maintain it, usually a security engineer with scripting skills your team wants to keep. When a vendor API changes, the playbook needs an update. When a new threat type does not match an existing playbook, the alert waits in the queue until a human picks it up. Because SOAR platforms are code-heavy and take real effort to scale, most organizations automate a fraction of their processes and handle the rest by hand.

GigaOm’s SecOps Automation Radar highlights the complexity, management overhead, and cost that legacy SOAR carries at scale. The SANS 2024 SOC Survey found that automation challenges had become the top barrier to effective SOC operations, ranking above staffing. That gives the next generation of security automation plenty of room to deliver.

What Is an AI SOC?

An AI SOC is an operating model: a fundamentally different way of structuring how your SOC detects, investigates, and responds to threats. A true AI SOC model puts AI at the center of the work and builds the structure, accountability, and controls around it.

What Does an AI SOC Include?

Four capabilities define a complete AI SOC.

• Complete threat lifecycle coverage. The SOC owns every action surrounding a threat, so the work continues after detection and the “this is real!” verdict. An AI SOC accelerates mean time to detection and investigation, plus mean time to respond (MTTR).
• Agentic operations. AI plans, reasons, and executes end-to-end security tasks, such as separating real threats from false positives, extracting key attack details across disparate systems, and coordinating case management autonomously. In successful deployments, AI agents fully remediate alerts on their own.
• Automation modernization. Teams move from playbook-heavy systems to platforms built for AI-speed automation creation, better reuse, and stronger governance.
• More consistent execution. The SOC shifts from “people clicking buttons” to processes that run consistently, with humans approving sensitive actions.

What Are the Three Principles of an AI SOC?

Three principles turn those capabilities into daily practice.

1. Agents drive execution. In a legacy SOC, execution depends on whoever is on shift and what they remember to do. In an AI SOC, every alert passes through AI agents that reason through the situation, select the right tools to query, gather evidence, and execute response actions within guardrails. Torq Auto Triage starts the process by filtering noise and prioritizing real threats. Torq HyperAgents® are specialized, customizable AI agents built for investigation and response, and Torq Socrates™, Torq’s agentic SOC orchestrator, coordinates them to manage cases end to end. The agents document an immutable system of record for what happened and why each decision was made. Analysts supervise execution, step in on escalations, and refine the logic over time.
2. Cases centralize accountability. In most SOCs today, accountability scatters across ticketing systems, Slack threads, email chains, and analyst memory, so seeing the full picture of an incident means assembling it from five different tools. In an AI SOC, the case is the single source of truth: the platform creates it automatically from correlated alerts, enriches it with evidence from across the stack, prioritizes it by business impact, and tracks it from detection through resolution. The case logs every agent action, AI decision, and human intervention in one place. When leadership asks “what happened and how did we respond?” the answer lives in the case.
3. Governance keeps automation trustworthy. Automation without governance accelerates risk, so an AI SOC builds governance into the operating model: approval gates for high-impact actions, immutable audit trails for every decision, scope boundaries that limit what agents can touch, and regular validation cycles where the team reviews AI-closed cases for accuracy. That architecture makes autonomy safe enough to trust at scale and explainable enough to defend to auditors, insurers, and the board.

The shift from SOAR to an AI SOC is a move from “we have some automation” to “AI-driven automation is how we operate,” with the structure, accountability, and controls to make it sustainable.

How Long Does AI SOC Implementation Take Compared to SOAR?

AI SOC platforms go live in days to weeks, while legacy SOAR implementations take 12-18 months to show meaningful ROI. That gap is the largest time-to-value difference in enterprise security tooling today.

Legacy implementations take time for understandable reasons: custom playbook development for each use case, integration work for every tool, scripting that specialized engineers maintain, and long QA cycles because a broken playbook can interrupt production response. AI SOC platforms remove those dependencies. AI agents investigate without predefined playbooks, native integrations ship with the platform, and any analyst can build and modify automations through natural language or a no-code builder.

Timelines from Torq customers:

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

The average enterprise SOAR implementation takes longer than the average enterprise AI SOC deployment needs to deliver measurable ROI. For security leaders building a business case, that timeline matters: every month spent rebuilding or maintaining playbooks is a month of risk and capacity the organization cannot recover.

What Are the Hidden Costs of Legacy SOAR?

Licensing is the visible line item in a SOC automation budget, and the larger costs sit underneath it: engineering headcount, integration upkeep, professional services, and the risk carried by alerts nobody investigates. Each of these costs also marks an opportunity to recover budget with AI agents.

How Much Do SOAR Engineering and Maintenance Cost?

Legacy SOAR typically needs dedicated security engineering headcount, often $400,000-$600,000 or more per year in loaded cost, plus integration and maintenance work that can reach 40+ hours per week at scale. Every vendor API update starts a maintenance cycle, every new tool needs integration work, and every novel threat type needs a new playbook that takes weeks to build and test. AI agents lighten that load because they investigate without predefined playbooks.

What Do SOAR Integrations and Professional Services Add?

Integration work and professional services add cost at the start of a deployment and again each time the stack changes. Custom scripting for each tool, QA cycles, and playbook development for each use case all draw on specialized skills, which stretches implementations to 12-18 months before ROI appears. The Torq AI SOC Platform shortens that path with 300+ native integrations that ship with the platform, so teams spend their budget on outcomes instead of connector builds.

What Does the Coverage Gap Cost?

Alerts that go uninvestigated carry risk that rarely shows up on a budget line. Your team has spent years building automation that covers about a third of your alerts, and the rest wait in the queue or go uninvestigated. SACR’s 2025 AI SOC Market Landscape research, based on a survey of 300+ CISOs, found that 40% of alerts are never investigated, and 90% of those that are investigated turn out to be false positives. Agentic triage with Torq Auto Triage filters out that noise and prioritizes real threats, averaging 42 seconds per triage with 94% accuracy out of the box (as of May 2026).

What Does Talent Dependency Cost?

The talent math adds to the picture. The engineers who build playbooks are the same engineers every company in your industry wants to hire, and when one leaves, the tribal knowledge encoded in the automation leaves with them. AI agents change that equation: any analyst can build and modify automations in natural language, so institutional knowledge stays in the platform.

SOAR’s deterministic model suited an era of slower, more predictable attack patterns. Attackers now use AI and move at machine speed, so response needs to keep that pace.

How Does an AI SOC Compare to Traditional SOAR?

An AI SOC extends traditional SOAR across every dimension a modern SOC measures: coverage, speed, maintenance effort, scalability, and accessibility. The table below puts the two side by side.

Legacy SOAR excels at deterministic playbook execution for known scenarios. Torq Hyperautomation runs those same scenarios up to 10x faster, and AI agents handle everything else.

Which Threats Does an AI SOC Handle Better Than SOAR?

An AI SOC extends coverage to every threat category, because the limiting factor in playbook-based automation is the playbook, not the threat type. When a playbook exists, SOAR executes it well, and AI agents pick up everything else. The gap is widest in five categories.

Novel and Zero-Day Attacks

By definition, no playbook exists when a threat is new, so those alerts land in the manual queue. AI agents reason through unfamiliar patterns by correlating signals, querying enrichment sources, and building a hypothesis without a pre-built playbook.

Malware

Static playbooks match known IOCs and behaviors, and polymorphic malware mutates to slip past that matching. An AI SOC, using agentic triage from Torq Auto Triage, evaluates behavior and context rather than signature, flagging threats that a rule-based system can miss.

Multi-Stage and Multi-Vector Attacks

Modern campaigns span email, identity, endpoint, and cloud, sometimes over weeks. Playbooks typically run per alert and per tool, which makes the connecting thread hard to see. An AI SOC correlates across the full attack surface and builds a single case that shows the whole campaign.

Identity-Based Attacks

Account compromise, session hijacking, and OAuth abuse call for contextual reasoning about user behavior, device posture, and access patterns. Playbooks handle discrete signals well, and AI agents add the judgment call: “Is this really the user doing this?” They assemble the context automatically.

Cloud Misconfigurations and Supply Chain Threats

These threats require reasoning across tools that did not exist when most SOAR platforms were built. Native AI SOC integrations cover AWS, Azure, GCP, CrowdStrike, Microsoft Defender, and 300+ more out of the box.

The common thread: anywhere a human analyst would say, “I’d need to look at this in context,” an AI SOC can help by bringing that context to every alert.

What Benefits Does an AI SOC Deliver?

An AI SOC delivers broader coverage, faster adaptation, wider team access, quicker time to value, and elastic scale. For organizations evaluating security automation in 2026, it solves the problems that playbook-based automation left open and adds capabilities that were out of reach before.

Broader Coverage

An AI SOC, with agentic triage from Torq Auto Triage, investigates 100% of the alerts that hit your queue, compared with the 30-40% that have matching playbooks. That moves a team from automating tasks to automating outcomes.

Adaptability to New Threats

Novel attack techniques, evolving TTPs, and multi-stage campaigns do not wait for someone to write a playbook. Agentic AI reasons through unfamiliar scenarios by correlating signals, enriching context, and making policy-aware decisions. Torq’s context and memory capabilities help agents learn from every case and improve over time.

Access for Your Whole Team

Torq’s Agentic Builder and natural language interface let any analyst build, modify, and trigger automations. Your team gains independence from the two senior engineers who understand the scripts behind your playbooks.

Time to Value Measured in Days

Valvoline went live on top-priority use cases within a week, and an integration that had been stalled for months under its legacy SOAR arrived in days. The team saved six to seven hours of analyst time every day from the start. Legacy SOAR implementations typically take 12-18 months to show meaningful ROI, so an AI SOC gives you those 12-18 months of risk reduction back.

Scale Without Degradation

Serial queues in legacy SOAR platforms can slow response when alert volume surges, which is when automation matters most. The cloud-native architecture of the Torq AI SOC Platform processes millions of daily security automations without bottlenecks because Torq built it for elastic scale from the start.

What ROI Can Organizations Expect From an AI SOC?

Security automation ROI from an AI SOC arrives in days to weeks. Torq customers commonly achieve a 60x increase in SOC throughput and velocity and a 90%+ reduction in mean time to respond. The sections below cover how quickly returns show up and which metrics prove them.

How Quickly Can an AI SOC Pay for Itself?

Valvoline saw ROI inside 48 hours and recovered six to seven analyst hours per day, while legacy SOAR typically needs 12-18 months to show meaningful ROI. A faster start compounds, because every month of earlier coverage shrinks the backlog and the risk that comes with it. A former CISO’s perspective on measuring AI SOC ROI beyond hours saved adds a useful framework for building the business case.

What Metrics Should Organizations Track?

Six metrics show whether an AI SOC delivers, and each one maps to a cost or risk discussed above.

• Percentage of alerts investigated
• Alerts missing SLA, driven toward zero
• Mean time to investigate (MTTI) and mean time to respond (MTTR)
• Time to operationalize a new use case
• Engineering hours spent on automation maintenance
• Analyst hours recovered for higher-value work

Baseline each metric before deployment, then review it at 30, 60, and 90 days to see the trend.

Real-World Examples: How Teams Moved From SOAR to an AI SOC

Five Torq customers show what the move looks like in practice, from enterprise security teams to a managed services provider with 200+ customers. Each example follows the same arc: a legacy automation foundation, a fast move to agentic AI, and measurable results.

Valvoline: ROI Inside 48 Hours

Valvoline’s legacy SOAR had an integration that stayed blocked for months. After moving to the Torq AI SOC Platform, the team went live on top-priority use cases within a week and delivered that stalled integration in days. The result: ROI inside 48 hours and six to seven analyst hours saved per day.

RSM: 200+ Managed Customers in Three Weeks

RSM serves a large base of managed customers, so migration speed matters. The RSM team migrated 200+ managed customers off legacy SOAR in three weeks. Todd Willoughby, Director at RSM, credits Torq with extraordinary efficiency for RSM and its customers. MSSPs and MDRs like RSM deliver more value per customer when an AI SOC handles the repetitive work, and Torq for MSSPs and MDRs is built for that model.

Deepwatch: Years of Automations Recreated in Weeks

Deepwatch standardized its global security infrastructure on Torq. The Deepwatch team recreated years of legacy SOAR automations in weeks, then started building new capabilities beyond what its previous platform covered.

Lennar Corp.: Phishing Response From Hours to Minutes

Lennar replaced its legacy SOAR platform with Torq and cut phishing response from hours to minutes. Phishing is a high-volume, repeatable use case, which makes it a strong starting point for AI agents.

Carvana: Every Tier 1 Alert Handled by Agentic AI

Carvana’s CISO, Dina Mathers, reports that Torq agentic AI now handles 100% of Carvana’s Tier 1 security alerts. Read Carvana’s story to learn more.

Exploring how these results apply to your sector? See how the Torq AI SOC Platform supports financial services and healthcare security teams.

What Happens to Existing SOAR Playbooks During Migration?

Your existing playbooks keep working: proven automations run up to 10x faster on Torq Hyperautomation, and AI agents add the investigation and response capabilities that playbooks never covered. This is the question that keeps teams on legacy SOAR longer than they need to stay, and migration to Torq builds on everything you have already created.

Can Existing Playbooks Move to an AI SOC?

Yes. Torq Hyperautomation, the engine behind the Torq AI SOC Platform, executes your proven automations up to 10x faster than they ran on legacy SOAR, and your integrations stay intact. On top of that foundation, Torq’s Multi-Agent System, coordinated by Socrates, handles the agentic investigation, autonomous triage, and adaptive response that playbooks never covered. Torq Retrospect carries years of SOC case history into Torq in under an hour, so your past moves with you.

What Does a SOAR Migration Look Like?

Deepwatch, RSM, and Lennar each built on their existing automations, and none started from scratch. All of them gained more from Torq in weeks than years of legacy SOAR had delivered.

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

Protecting an existing investment means getting more from it. Moving your automations to an AI SOC platform puts that investment to work at greater speed and scale.

When Should You Move From SOAR to an AI SOC?

The right time to move is when your automation no longer keeps pace with your alert volume, your team, or your threat landscape. Answer these five questions candidly to see where your SOC stands today.

1. What percentage of your alerts does your team investigate? If the answer is under 80%, you have a coverage gap that playbooks alone cannot close. An AI SOC investigates every alert, while playbook-based automation covers the scenarios someone built a playbook for.
2. How many full-time engineers maintain your automation? If dedicated security engineers spend their time keeping playbooks running, that capacity could go to higher-value security work. Modern platforms reduce engineering dependency to a minimum.
3. How long does it take to operationalize a new use case? If the answer is weeks or months, your automation is trailing your threat landscape. Torq customers operationalize new automations in minutes using natural language or the no-code builder.
4. What happens when an alert does not match an existing playbook? If it waits in the queue, your automation gap grows with every new attack technique. AI agents investigate novel scenarios without waiting for someone to write the logic.
5. How does your platform perform during alert volume spikes? If response time slows when you need it most, your architecture has a structural limit that more playbooks will not lift.

If two or more of these answers point to a gap, it is time to evaluate an AI SOC.

SOAR Promised Automation. The AI SOC Delivers It.

SOAR was an important step. It proved that security operations benefit from automation, and it showed what the next generation needs: more adaptability, less maintenance, and coverage that matches the threat landscape.

An AI SOC, powered by agentic AI and Hyperautomation, delivers on what SOAR set out to achieve: every alert investigated, every response executed fast, every action auditable, and analysts focused on work that calls for human judgment. That means coverage of all alerts, compared with 30-40% from playbooks alone.

Carvana, Valvoline, Deepwatch, RSM, and Kenvue have already made the move. They built on a strong automation foundation and took it to the next level.

Ready to see what an AI SOC can do for your team?

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