How AI is Redefining SOC Architecture 

If you’ve been in cybersecurity longer than five minutes, you know one thing: legacy SOC architecture isn’t just showing its age — it’s creaking under the weight of today’s threats. 

Cybersecurity analyst Francis Odum nailed it when presenting at Torq’s SKO 2025: “Legacy SOAR assumed everything starts in the SIEM. Now, teams connect automation directly to EDR, email, and identity systems.”.

This antiquated SOC architecture model, where every alert and log file is funneled into a Security Information and Event Management (SIEM) solution for analysis, is too slow, too rigid, and creates too many bottlenecks to support today’s exploding security event and data pipeline. Modern SOCs need speed, scalability, and a level of intelligence that legacy architecture simply cannot provide. They need a new approach that is purpose-built for the AI era. 

What is AI SOC Architecture?

AI SOC architecture is not just about adding AI to the stack — it’s about re-architecting the stack around AI. The traditional SOC model relies on aggregating data into a centralized point of analysis before taking action. In contrast, the AI SOC places agentic, AI-powered Hyperautomation at the center of operations — integrating directly with data lakes, security tools, and workflows to create a unified, AI-native control plane. This architecture ensures a single source of AI truth, distributed evenly across the entire security stack.

Shifting the SOC Foundation

“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

For years, the SOC has been centered around the SIEM. Disparate security vendor solutions would feed hundreds of thousands of logs, events, and alerts into the SIEM for security analysts to manually parse through, correlate, and eventually return to the respective point solution(s) to begin the remediation process. This model created a lot of friction, leading to several chronic problems, including: 

• Process debt: This process would cause what we in the biz call “swivel chair syndrome,” as it often isn’t as simple as a single straight line from detection to SIEM to remediation. Instead, the lengthy investigation had analysts swiveling back and forth between the SIEM and security tools several times before reaching a conclusion hours later. 
• Central bottlenecks: While a centralized approach to security event management once seemed favorable, SIEM solutions were not designed for the volume of data produced by the multi-cloud environments that organizations have built — let alone the deployment of AI to help alleviate the manual filtering of that data. This creates a massive data bottleneck and, worse, a single point of failure for the SOC to rely upon. 
• Reactive, delayed response: In addition to scalability concerns, this is also a largely reactive approach, requiring analysts to use the SIEM to begin the manual investigation process long after an incident occurs. This slows down critical SOC reporting metrics like Mean-Time-To-Detection (MTTD) and Mean-Time-To-Response (MTTR). Legacy SOAR solutions attempted to solve this problem but did not promise faster orchestration or response times due to limited and inflexible automation playbooks. 

Between sifting through an overwhelming amount of logs in a centralized SIEM solution and battling the manual efforts of legacy SOAR automation, security analysts find themselves drowning in disconnected alerts and burning out at an alarming rate. 

An AI SOC architecture flips this on its head, shifting the SIEM further left in the security event lifecycle, particularly as many organizations continue to adopt a multi-SIEM strategy to offset increasing storage costs from legacy SIEM vendors. 

Gartner’s recent Reference Architecture Brief: SIEM-Centric Security Operations report points out that as the industry largely shifts away from legacy SOAR solutions, it is seeing more advanced capabilities come from platforms centered around AI SOC Analysts, which produce stronger outcomes for analyst augmentation and security automation. 

What Does AI-Native SOC Architecture Look Like? 

In the same report, Gartner breaks down the Security Operations Center architecture into two distinct components: Security Operations Tools (e.g., SIEM and Detection-as-Code solutions) and SOC Actions (e.g., manual triage, investigation, threat hunting, and response via the SOC Team). Gartner calls out SecOps Workflow Automation, which consists of third-party automation and AI SOC analysts, bridging the gap between these two pillars of the SOC. 

This is the heart of the AI-native SOC Architecture — a foundation of agentic AI and Hyperautomation built for the modern cloud-first SOC environment and designed for simplicity, extensibility, and scale.

Agentic AI

Agentic AI sits at the core of the AI SOC architecture. Rather than burdening human analysts with manually piecing together thousands of logs and events, an AI-native SOC leverages a multi-agent system (MAS) to handle up to 90% of Tier-1 security analysts’ tasks. These specialized AI agents have a deep understanding of the SOC environment, allowing them to plan incident response, make complex decisions, and take remediation actions autonomously. 

Hyperautomation

Hyperautomation is the engine that drives autonomous response and the glue that connects agentic AI with the rest of the SOC solutions to bridge the gap between Security Operations Tools and SOC actions. With limitless no-code or AI-generated integrations, the Hyperautomation engine is the delivery system allowing agentic AI to take action, automating anything from simple alert triage to complex, multi-step incident responses. 

Enterprise-Grade Security Architecture

Unlike monolithic legacy SIEM and SOAR solutions, an AI-native SOC architecture is built for cloud-first scalability and flexibility. Underpinned by an extensible security architecture, horizontal and elastic scalability allows the SOC to dynamically process and prioritize hundreds of thousands of events from various data sources, ensuring the most critical information is surfaced without interruption.

Torq’s AI SOC Architecture

Torq is built for this moment. It’s not about retrofitting AI into a legacy architecture — Torq is an enterprise-ready, AI-native platform purpose-built from the ground up to solve existential SOC challenges like alert fatigue, tech sprawl, and analyst burnout. 

Torq’s AI SOC architecture begins with the ability to integrate with any solution across the entire security stack and beyond — whether it’s EDR, IAM, email phishing, threat intelligence, collaboration and communication tools, and more. 

This direct integration enables agentic AI to not only take autonomous remediation actions across Tier-1 and Tier-2 security use cases but also allows AI agents to retrieve and enrich data directly from the source, regardless of what data may be missing (or difficult to find manually) from SIEM logs. As the modern SOC scales to produce tens of thousands of alerts per day, Torq’s AI-SOC architecture can seamlessly handle massive alert volumes without creating single-point bottlenecks. 

HyperSOC™ 

Torq HyperSOC, the AI-powered autonomous SOC solution, was also explicitly designed to support AI deployment across the modern SOC. While legacy SOAR solutions have bolted-on workarounds to handle case management once an analyst has manually pulled the relevant data from a SIEM tool, Torq HyperSOC is comprised of intelligent case management and Socrates, the agentic AI SOC Analyst, embedded directly in each security case. Socrates summarizes key findings, suggests next steps, and analyzes case runbooks for autonomous remediation. 

The Multi-Agent System 

Socrates coordinates Torq’s multi-agent system, a team of AI Agents that can autonomously handle the vast majority of Tier-1 and Tier-2 use cases, reduce human analysts’ workload by over 95% from initial investigation to final remediation, and enable SOC teams to tackle up to 5x more security cases in a single day without adding headcount.

Model Context Protocol

To help Torq’s system of AI agents communicate reliably across a limitless amount of integrated security tools and other AI solutions deployed in the SOC, Torq’s AI SOC architecture also natively supports Model Context Protocol (MCP), an open protocol designed to standardize how applications provide context to AI Agents to retrieve contextual information from applications and systems. 

Human-on-the-Loop AI Guardrails

Finally, this entire AI architecture is designed with the appropriate AI guardrails that provide the explainability, audibility, and control organizations require. These guardrails ensure there is always a human on the loop to avoid AI hallucinations and so SOC teams remain in control of critical decisions.

From AI-Enabled to AI-Architected

Legacy SOC architecture isn’t just outdated — it’s actively holding organizations back. True AI-native SOC architecture, like Torq HyperSOC, breaks through these barriers. It offers immediate, measurable outcomes, dramatically improving analyst effectiveness, reducing costs, and transforming security postures from reactive to proactive.

In Francis Odum’s words: “The market is ready for next-gen, AI-powered solutions. These aren’t future-state ideas; they’re delivering real-world results right now.”

The future of cybersecurity isn’t just AI-enabled; it’s AI-architected. 

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