What is an AI SOC Platform? How to Choose the Right One for Your Security Operations

AI SOC platforms apply artificial intelligence across security operations to automate detection, investigation, and response. But the category is broad, and the architecture behind each platform determines what it can do for your SOC—and where it will fall short.

Most AI SOC platforms address only one piece of the threat lifecycle. Choosing a platform that can't scale with your operations means ripping and replacing later. This guide breaks down the four major platform architectures, their limitations, and what to prioritize when evaluating AI SOC vendors.

Key Takeaways

• AI SOC platforms fall into four architectural categories: agentic AI, hyper automation, detection-focused, and workflow automation. Each comes with structural limitations that affect long-term SOC scalability.

• Architecture determines outcomes. A platform that coordinates multi-agent AI systems across the full TDIR lifecycle delivers fundamentally different results than a SOAR tool with an AI layer added on.

• ReliaQuest GreyMatter is the only platform that unifies agentic AI, multi-agent orchestration, and 250+ bi-directional tool integrations across detection, investigation, containment, and response—with customers achieving MTTC of 5 minutes or less.

• With market penetration at just 1–5%—signaling early-stage adoption with significant first-mover advantage.

• Point solutions and automation-first platforms create technical debt. Choosing a platform that covers only triage or only orchestration means bolting on additional tools later to fill the gaps.

Four Types of AI SOC Platform Architecture

The architecture behind an AI SOC platform shapes what it can do, where it falls short, and whether it can grow with your operations. Here are the four dominant approaches and the structural limitations security leaders should understand before buying:

Agentic AI Platforms

Agentic AI platforms deploy autonomous AI agents that detect, investigate, and respond to threats without following static playbooks. A central orchestrator agent coordinates specialized agents—each handling a distinct task like enrichment, correlation, or containment—and adapts workflows in real time based on context.

This is the only architecture that covers the full TDIR lifecycle through coordinated AI decision-making. ReliaQuest GreyMatter's multi-agent system is trained on decades of real-world SOC data, integrates with 250+ tools bi-directionally, and delivers mean times to contain 5 minutes or less. GreyMatter's Agentic Teammates extend AI further into threat hunting, detection engineering, and IT health monitoring.

Other vendors in this space apply agentic AI to narrower slices of the workflow, creating a critical distinction: not all agentic AI platforms cover the same scope.

Vendors in this category: ReliaQuest GreyMatter, 7AI, Dropzone AI, Prophet Security

Hyperautomation Platforms

Hyperautomation platforms extend traditional security orchestration automation and response (SOAR) by adding AI-assisted triage and no/low-code workflow builders. They're designed to orchestrate multi-step response actions across tools at scale.

Structural limitations:

• Playbook dependency. Hyperautomation still relies on human-designed workflows. Every automated action requires someone to build, test, and maintain the underlying playbook. As threat complexity grows, playbook maintenance becomes its own operational burden.

• Reactive by design. These platforms execute predefined steps — they don't reason about novel threats or dynamically re-prioritize based on emerging context. When an alert doesn't match an existing workflow, it falls through to a human.

• No native investigation. Hyperautomation platforms orchestrate response actions, but they don't investigate alerts or make triage decisions autonomously. You still need separate tooling or manual analyst effort for the investigation step.

Vendors in this category: Torq, Swimlane

Detection-Focused Platforms

Detection-focused platforms run AI-assisted threat detection across multiple data sources, like SIEMs, data lakes, and point tools, without requiring data migration. They strengthen the detection layer of the SOC but stop there.

Structural limitations:

• Detection only. These platforms identify threats but don't investigate, contain, or respond to them. Every detection still requires analyst triage and manual escalation through other tools.

• No response orchestration. Detecting a threat faster has limited value if containment still takes hours. Without built-in response capabilities, there leaves a gap between detection and action.

• Incomplete lifecycle coverage. Organizations adopting a detection-focused platform will need to layer additional tools for investigation and response. This adds integration complexity and tool sprawl.

Vendors in this category: Anvilogic

Workflow Automation Platforms

Workflow automation platforms provide no-code builders for designing custom security workflows. They're flexible and accessible, with strong community libraries and broad API connectivity.

Structural limitations:

• No AI reasoning. Workflow automation platforms execute the steps you define — they don't triage alerts, reason about threats, or make autonomous decisions. There is no native AI investigation, detection, or prioritization.

• Manual design required. Every workflow must be designed, tested, and maintained by your team. The platform automates execution, but the intelligence behind every decision still comes from a human.

• Ceiling on complexity. Simple, repeatable processes work well. Multi-step, context-dependent investigations that require judgment across data sources quickly exceed what rule-based automation can handle.

Vendors in this category: Tines

AI SOC Platform Comparison

1. ReliaQuest GreyMatter

• Key Strength: Covers detection through response with coordinated agentic AI; MTTC ≤5 min; Agentic Teammates for hunting and detection engineering.

• Key Limitation: Best suited for enterprise-scale SOCs; not optimized for small or mid-sized organizations.

2. 7AI

• Key Strength: Dynamic agent creation; end-to-end autonomous operations.

• Key Limitation: Newer platform; less proven at scale in complex multi-SIEM environments [SME VERIFY]

3. Dropzone AI

• Key Strength: Fast deployment; replicates elite analyst techniques

• Key Limitation: Triage-focused — lacks response orchestration and containment

4. Prophet Security

• Key Strength: Learns from analyst feedback; strong investigation narratives

• Key Limitation: Investigation-centric — limited containment and response actions

5. Torq

• Key Strength: Powerful no/low-code workflow builder; strong SOAR replacement

• Key Limitation: Automation-first — requires extensive workflow design to realize value

6. Swimlane

• Key Strength: Turbine excels at data curation and case management; SOAR leader

• Key Limitation: SOAR foundation with AI layered on; playbook-dependent

7. Anvilogic

• Key Strength: Runs detections across Snowflake, Databricks, Splunk without migration

• Key Limitation: Detection layer only. Focused on SIEM replacement and limited triage response

8. Tines

• Key Strength: Flexible no-code builder; free Community Edition; large template library

• Key Limitation: General-purpose automation — no native AI detection. Automated triage/investigation is limited in scope.

How to Evaluate an AI SOC Platform

The AI SOC platform market is crowded and the terminology is inconsistent. Vendors across all four architecture types call themselves "AI-powered," "autonomous," and "agentic." Yet the underlying capabilities differ dramatically. Cut through the marketing with these evaluation criteria:

1. TDIR Lifecycle Coverage

Does the platform cover detection, investigation, containment, and response—or just one step? Platforms that address only triage or only response create gaps that require additional tooling, additional integrations, and additional analyst effort to bridge.

2. AI Autonomy Level

Does the platform act autonomously, or does it advise and wait for a human? There's a significant operational difference between a platform that autonomously contains a threat in minutes and one that surfaces a recommendation an analyst must review, approve, and execute manually.

3. Integration Depth

How many tools does the platform integrate with, and are those integrations bi-directional? A platform that reads telemetry from your SIEM but can't push containment actions to your EDR or identity tools leaves execution gaps. Look for platforms that work with your existing stack, not ones that require rip-and-replace.

4. Transparency and Explainability

Can analysts see why the AI made a decision? Auditable decision trails are essential for trust, compliance, and continuous improvement. Platforms that operate as black boxes erode analyst confidence and complicate post-incident reviews.

5. Adaptability

Does the platform learn from your environment? Static models trained on generic data degrade over time. Platforms grounded in your organization's telemetry, alert history, and analyst feedback improve with every investigation — reducing false positives and surfacing higher-fidelity incidents.

6. Scalability Without Technical Debt

Will this platform still meet your needs in two years? Point solutions that solve today's triage problem may create tomorrow's integration problem. Choosing a platform architecture that covers the full lifecycle from day one avoids the cost and complexity of bolting on additional tools later.

For a deeper evaluation framework, see the right questions to ask when evaluating AI SOC vendors and 6 entry points for bringing AI into your SOC.

FAQ

What is an AI SOC platform? An AI SOC platform is a security operations solution that uses artificial intelligence — including machine learning, generative AI, and agentic AI—to automate core SOC functions like alert triage, threat investigation, and incident response. Platforms vary significantly in architecture, autonomy level, and how much of the TDIR lifecycle they cover.

What's the difference between an AI SOC platform and a SOAR platform? SOAR platforms automate response through predefined playbooks that teams must build and maintain. AI SOC platforms with agentic AI architecture use autonomous agents that reason about threats, adapt to new data, and act based on context—not static rules. Some vendors bridge the gap by adding AI to a SOAR foundation, but the underlying playbook dependency remains.

Do AI SOC platforms replace existing security tools? No. The strongest AI SOC platforms integrate with your existing SIEM, EDR, identity, and cloud tools rather than replacing them. ReliaQuest GreyMatter connects to 250+ tools bi-directionally, operating as an orchestration and intelligence layer on top of your current stack.

What are the risks of choosing a narrow AI SOC platform? Platforms that cover only triage, only detection, or only workflow automation create gaps across the threat lifecycle. Filling those gaps requires additional tools, additional integrations, and additional analyst effort—compounding complexity over time. A full-lifecycle platform avoids this technical debt from the start.

Is agentic AI in the SOC proven or still experimental? Agentic AI is production-ready for leading platforms. Early adopters gain a measurable operational advantage if they choose the platform carefully.

What Are The Next Steps?

The AI SOC platform you choose is an architecture decision with long-term consequences. Workflow automation, hyperautomation, and detection-focused platforms each address a narrow slice of the threat lifecycle—each with structural limitations that create operational gaps as your SOC scales. Full-lifecycle agentic AI, coordinated through multi-agent orchestration with deep bi-directional integration, is the only architecture that eliminates those gaps from the start.

Start here:

• Get the evaluation framework for AI SOC vendors

• Explore GreyMatter's agentic AI architecture

• Learn how AI agents operate inside the SOC