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

This article was originally published on Security Info Watch. 

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

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

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

The True Cost of Running a SOC

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

People Costs

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

Tooling Costs

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

Breach Costs

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

Hidden Costs

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

Outsourcing Costs

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

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

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

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

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

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

The Three Pillars of an Autonomous SOC

1. Hyperautomation

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

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

2. AI Agents

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

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

3. Enterprise-Grade AI Architecture

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

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

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

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

– Francis Odum, Software Analyst Cyber Research

What an Agentic SOC Fixes

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

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

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

– Dina Mathers, Carvana CISO

The Future of SOC Economics

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

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

Go autonomous in less than 90 days with Torq.