GreyMatter Universal Translator: Field-Level Normalization Across Every Source


A patented data mapping engine that automatically normalizes every field from any connected technology to OCSF—enabling detection, investigation, and response across your entire stack from one place, without centralizing data first.

The Architecture Behind Unified Operations

The Universal Translator maps every individual field from any connected technology to OCSF automatically at integration via an AI-powered Advanced Data Mapping (ADM) engine. The result: a normalized telemetry layer spanning endpoint, cloud, identity, network, OT, and SaaS.

How Advanced Data Mapping Works

When a new technology connects to GreyMatter, the Universal Translator:

01
Ingests the raw schema

From the connected technology—including vendor-specific field names, formats, and structures.

02
Maps each individual field

To its OCSF equivalent automatically, preserving the source-specific detail needed for accurate detection across disparate tools.

03
Maintains the mapping continuously

As source schemas change, without requiring manual intervention or re-mapping.

Example
Parent Process Detection

A detection looking for suspicious child processes spawned by a browser needs the parent process filename. Three tools express this differently:

Source ToolNative Field NameOCSF Mapping
CrowdStrike FalconParentBaseFileNameprocess.parent_process.file.name
Microsoft DefenderInitiatingProcessFileNameprocess.parent_process.file.name
SentinelOneparentProcessNameprocess.parent_process.file.name

One detection rule—"alert when process.parent_process.file.name matches a browser AND process.file.name matches a known LOLBin"—fires across all three EDRs simultaneously. No tool-specific detection logic. No per-vendor rule maintenance.

One Query, Every Tool

ADM powers GreyMatter Query Language (GMQL)—a query syntax that abstracts security activities into a single expression. The analyst queries for what happened; the Universal Translator resolves which event codes, fields, and filter values constitute that activity in each connected tool.

Example
Hunting for Lateral Movement

An analyst investigating lateral movement from a compromised workstation writes:

activity = "Lateral Movement" AND src_endpoint.hostname = "WORKSTATION-14" AND time > last 24h

The Universal Translator knows what lateral movement looks like in each connected technology and translates the abstracted query into tool-native specifics:

ToolWhat the Query Becomes
Microsoft DefenderDeviceLogonEvents where LogonType == "RemoteInteractive" OR DeviceProcessEvents matching WMI/PsExec/DCOM parent-child execution patterns originating from WORKSTATION-14.
CrowdStrike FalconLateralMovement-tagged detections + ProcessRollup2 events with remote execution lineage traced to WORKSTATION-14.
Windows Event Logs (via SIEM)Event IDs 4624 (Type 3, Type 10 logons), 4648 (explicit credential use), 7045 (remote service installation) where source host = WORKSTATION-14.

All three execute simultaneously. Results return to one console, normalized to the same schema.

If you add a new tool to the environment, ADM maps that tool's lateral movement indicators to the same abstracted activity. Existing queries cover it immediately with zero rework.

Natural language works identically—an analyst types "show me lateral movement from WORKSTATION-14 in the last 24 hours," GreyMatter converts it to GMQL, and the Universal Translator resolves it into tool-native queries across every connected source.

Data Stitching: Full Attack Chains from Fragmented Telemetry

Because every source maps to the same OCSF fields, the Universal Translator can automatically stitch related events across tools into a single narrative—revealing attack chains that no individual tool would surface alone.

Example
Credential Theft → Lateral Movement → Exfiltration
SequenceSource ToolWhat It SeesShared OCSF Fields
1. Credential accessCrowdStrikeLSASS memory access by suspicious processprocess.file.name, endpoint.hostname, user.name
2. New authenticationAzure ADSuccessful login from same user.name, unusual locationuser.name, src_endpoint.ip, status_id
3. Lateral executionMicrosoft DefenderWMI process spawn on target hostuser.name, endpoint.hostname, process.parent_process.file.name
4. Data stagingCloud CASBBulk file download to local staging folderuser.name, file.name, src_endpoint.ip

Each tool sees its fragment. The Universal Translator stitches them via shared normalized fields—user.name and src_endpoint.ip thread the entire process together into one correlated attack chain.

What Field-Level Normalization Makes Possible

CapabilityHow It Works
Cross-Stack DetectionDetection logic written once applies across every connected technology simultaneously.
Detection at Source and In TransitRuns at the source technology and on data in motion via Transit. 5-second mean time to detect.
One Query, Every ToolGMQL converts one query into each connected tool's native syntax.
Natural Language to Native QueriesAnalyst questions convert to GMQL, then to native queries across every connected source.
Automatic Data StitchingNormalized fields stitch events across tools to surface full attack chains automatically.
One-to-Many Response ActionsBan a hash, block a URL, isolate a host—across every connected tool from one action.

Most platforms normalize to top-level event categories—"authentication event," "process creation"—without resolving individual fields. That abstraction can't produce precise cross-source detections. For the few vendors that have adopted OCSF, the mapping is limited to their own stack or performed manually.

In Production: Global Enterprise Onboards 12 Technologies in Under 30 Days

A global financial services firm—4 SIEM instances, 3 EDR platforms, multiple cloud environments post-acquisition. With the Universal Translator:

All 12 source technologies normalized to OCSF automatically upon connection.
Detection coverage unified across the full environment within the first 30 days.
Analysts stopped context-switching between tool-native consoles.
Two subsequent technology swaps (SIEM migration, EDR consolidation) required zero detection re-engineering.
6 months <30 days

Time to full operational coverage post-acquisition dropped from 6 months to under 30 days.

The Foundation for Agentic Defense

The Universal Translator is the normalization layer every other GreyMatter capability builds on. Detection at source runs correlation logic against normalized data at the integrated technology itself—data never moves. Detection in transit normalizes raw telemetry to OCSF as it enters the Transit pipeline, where multi-event correlation catches attacks in motion at 5-second mean time to detect. Detection at storage queries already-normalized data wherever it lives using the same logic.

See the Universal Translator in Action