TL;DR

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

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

That era is long dead.

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

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

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

Why Legacy SOAR Can’t Be Fixed With More Playbooks

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

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threat types, updated tool integrations, and evolving attacker techniques mean playbooks are perpetually incomplete or outdated. 

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

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

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

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

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

What the Best SOAR Replacement Actually Looks Like

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

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

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

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

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

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

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

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

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

Six Things the Right SOAR Replacement Delivers for Your SOC

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

Here’s what that looks like for your team.

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

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

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

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

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

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

This is What Torq Was Built For

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

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

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

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

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

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

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

Your SOAR Had Its Run. See What Comes Next.

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

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

Ready to make the move?

FAQs

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

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

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

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

What is SOAR?

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

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

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

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

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

What is an AI SOC?

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

An AI SOC must include:

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

Three principles define it:

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

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

How Long Does AI SOC Implementation Take Compared to SOAR?

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

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

Timelines from Torq customers:

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

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

SOAR vs AI SOC: Key Differences

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

Which Threats Does AI SOC Handle Better Than SOAR?

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

The gap is widest in five categories:

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

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

The Case Against Keeping SOAR

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

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

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

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

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

Why AI SOC Is the Clear Path Forward

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

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

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

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

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

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

What Happens to Existing SOAR Playbooks During Migration?

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

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

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

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

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

When Should You Move From SOAR to AI SOC?

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

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

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

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

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

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

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

SOAR Promised Automation. AI SOC Delivers It.

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

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

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

Your SOAR had its run. See what comes next. 

FAQs