AI and Data Trust FAQ

No, GreyMatter's AI agents are not trained on individual customer data. They are informed by over a decade of security operations expertise and refined through the patterns, threat intelligence, and operational insights gained from protecting more than 1,300 customer environments today. Your environment-specific context is applied at inference time through Retrieval-Augmented Generation (RAG), not through model training.

No, customer data is separated and not shared between customers. GreyMatter's multi-tenant architecture processes your telemetry within your environment.

GreyMatter is model agnostic. It selects the most effective AI model for each task based on use case, data type, and performance requirements. Rather than locking into a single LLM, the platform evaluates outputs across multiple models and shifts to better-performing configurations when needs change. This multi-model approach is reinforced by RAG, which grounds AI outputs in real-time and historical ReliaQuest data to reduce hallucinations.

Any AI system can produce inaccurate outputs. However, GreyMatter mitigates hallucination risk through Retrieval-Augmented Generation (RAG), which grounds every AI response in live and historical security data, combined with a six-phase AI testing and validation lifecycle: expert validation during design and build, crowdsourced QA through real-time customer feedback, daily statistical sampling by human experts, golden dataset testing using a library of validated use cases, LLM-as-judge secondary AI evaluation, and built-in safety guardrails including hallucination checks and validation logic.

GreyMatter's agentic AI makes decisions by correlating telemetry across your connected tools, applying threat intelligence, and using environmental and historical context to prioritize risk and determine the appropriate response. Every decision is transparent inside the platform—your team can inspect the data analyzed, the reasoning applied, and the actions taken for each AI-driven investigation.

GreyMatter uses a just-in-time data stitching model—it queries telemetry from your existing security tools (SIEMs, EDRs, cloud platforms, identity solutions) at the point of investigation rather than ingesting and storing it long-term. Your data stays in your environment, is normalized through the Universal Translator, enriched with external threat intelligence, and is never exposed to public AI models.

No. GreyMatter is an AI-native security operations platform, so AI is fundamental to how it operates. Attacker breakout times continue to shrink, and the speed and accuracy AI delivers is what keeps your organization ahead of the accelerating threat landscape. Your team does have granular control through GreyMatter to define which actions run autonomously and which require human approval, but removing AI from the equation isn't the intent.

GreyMatter's agentic AI correlates and filters alerts before they reach your analysts using agentic memory, previously triggered events in your environment, and guidance built from experts who have analyzed real enterprise security data for 15+ years to distinguish false positive from true positive behaviors. This enables GreyMatter to autonomously triage, investigate, and resolve routine Tier 1 and Tier 2 alerts with 99.4% accuracy and threat containment in minutes.

No, GreyMatter multiplies the expertise and impact of your current team—automating the reactive, repetitive work (triage, investigation, containment) that consumes analyst time so they operate proactively on threat hunting, detection engineering, and strategic security initiatives.