Does ReliaQuest train its AI models on my data?

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.

Does ReliaQuest share my security data with other customers?

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

What AI models does GreyMatter use under the hood?

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.

Can GreyMatter's AI hallucinate? What safeguards prevent that?

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.

How does GreyMatter's AI make decisions, and can I audit them?

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.

What data does GreyMatter collect from my environment?

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.

Can I turn off AI in GreyMatter?

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.

How does GreyMatter reduce false positives using AI?

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.

Does GreyMatter replace SOC analysts or augment them?

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.

AI Security, Testing & Governance FAQ

1. How does ReliaQuest measure AI accuracy?

GreyMatter currently operates at 99.4% prediction accuracy, measured continuously through the six-phase AI testing and validation lifecycle. This includes golden dataset testing against a library of validated use cases with known outcomes, daily statistical sampling reviewed by human experts, and crowdsourced QA from real-time customer feedback.

2. Does GreyMatter host its own AI models or use public models?

GreyMatter runs models within ReliaQuest's own secure infrastructure. Your data is never sent to public, consumer-facing AI endpoints, and the platform's model-agnostic architecture means ReliaQuest can select and swap models based on performance without exposing customer data to external services.

3. How does ReliaQuest secure its AI infrastructure?

GreyMatter's AI infrastructure is covered by the same controls validated in ReliaQuest's SOC 2 Type II attestation, including access controls, encryption, monitoring, and incident response. AI-specific protections include model isolation, input validation, output filtering, and the six-phase validation lifecycle that tests for adversarial inputs and unexpected behaviors before any model update reaches customer environments.

4. How does ReliaQuest address bias in its AI models?

GreyMatter's models are trained on security operations data (machine telemetry, threat patterns, and investigation outcomes) rather than data about people, which limits the types of bias risk common in other AI applications. The six-phase validation lifecycle includes expert review and golden dataset testing designed to catch systematic detection errors or blind spots, and GreyMatter's multi-model approach allows ReliaQuest to swap underperforming models when accuracy degrades.

5. Can I direct or tailor GreyMatter's AI to my environment?

Yes. While you cannot retrain the underlying models, you tailor GreyMatter's behavior through reference lists, agentic memories, and ongoing feedback on AI analysis. These shape how the AI prioritizes risks, filters alerts, and responds within your specific environment, so the platform becomes more effective for your context over time without modifying the core model.

6. Does GreyMatter offer MCP (Model Context Protocol) integration?

Intended use cases is to allow customers to connect their in house AI tools (Copilot, Claude Enterprise, etc to communicate with GreyMatter so they can orchestrate agentic AI workflows using the data that GreyMatter has and the data their AI has on their end. Something might be pulling incident data from GreyMatter and feeding it to Claude to build a custom report, or pulling in IT Ticket data into GreyMatter for GreyMatter's Agentic AI to use as context during an investigation. The oppotunities here as they say "are limitless"

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