Not All AI Is Built for the Enterprise SOC
AI in security is everywhere. Not all of it works at scale.
Startup AI vs. GreyMatter Agentic AI for the Enterprise SOC
See what separates AI tools designed for demos from GreyMatter—an agentic system already working in production for enterprise SOCs.
Startup AI |
| |
|---|---|---|
Operational Experience |
Built on less than 3 years of operational experience. |
Built on 15+ years of expertise across 1,300+ environments. |
Data Privacy |
Requires your data to train and improve its models. |
Doesn't train on your data; environment context applied in real time. |
Agentic AI |
Single-task bots limited to one function like triage or enrichment. |
Six AI personas that coordinate across every core SOC function. |
Platform Capabilities |
Limited native security capabilities beyond the core AI tool. |
Full platform with native SOAR, CAASM, dark web, and deep intel. |
AI Governance |
Self-reported accuracy with no structured testing or validation lifecycle. |
Six-phase AI testing and validation lifecycle with RAG-grounded responses. |
Technology Agnostic Architecture |
Locked to a single LLM provider and its limitations. |
Model-agnostic architecture that selects the best model per task. |
Enterprise Scale |
Unproven at enterprise scale with limited production history. |
74M alerts investigated annually across 250+ technologies. |
Pricing??? Something someone help lol |
Priced per alert investigated, which caps coverage and creates visibility gaps. |
Priced based on endpoint count. Unlimited alert investigations, direct connections, or data volume provide maximum visibility. |
GreyMatter Agentic AI 
5 Questions Every AI Vendor Should Be Able to Answer
These are the questions that separate production-ready AI from vendor claims. Here's how GreyMatter answers each one.
Does your AI train on customer data to improve?
__Our answer: __No. GreyMatter's AI agents are not trained on individual customer data. They are informed by over 15 years of security operations expertise and refined through the patterns, threat intelligence, and operational knowledge gained from protecting more than 1,300 customer environments. Your environment-specific context is applied at inference time through Retrieval-Augmented Generation (RAG), not through model training.
How do you prevent your AI from hallucinating?
Our answer: GreyMatter mitigates hallucination risk through RAG, which grounds every AI response in live and historical security data, combined with a six-phase AI testing and validation lifecycle, including expert validation, golden dataset testing, LLM-as-judge evaluation, and human expert oversight.
What happens when the AI gets something wrong mid-investigation?
Our answer: GreyMatter's guardrails automatically escalate alerts to human review if a response fails specific criteria three times. Analysts can also flag inaccuracies directly in the platform, which triggers manual review and feeds back into the AI's continuous improvement cycle.
Can you show independent, third-party validation of your AI claims?
Our answer: Forrester's Total Economic Impact study found GreyMatter delivers 224% ROI over three years. Gartner recognizes ReliaQuest GreyMatter as an AI SOC platform, noting that ReliaQuest "leveraged decades of security operations insights to train generative and agentic AI models." ReliaQuest is continuously evaluated by leading analyst firms as the AI SOC market evolves—these aren't one-time assessments.
How fast can your AI detect and contain threats?
Our answer: GreyMatter’s data pipeline tool, Transit, delivers sub-5-second mean time to detect (MTTD) by identifying threats in data before it reaches your SIEM. Once detected, GreyMatter investigates threats with a 33-minute mean time to investigate (MTTI) and contains them in under 5 minutes.