AiVRIC
Security Intelligence Platform
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AIVRIC PLATFORM FABRIC

Shared Data Layer

One schema, one index, one truth — enabling every AiVRIC capability to query, correlate, and act on the same verified security and compliance data.

The AiVRIC Fabric Shared Data Layer is the normalized backbone that eliminates data silos across every suite.

Without a shared data layer, security teams maintain separate stores for CSPM findings, GRC evidence, AI evaluation results, and incident data. The Fabric collapses these into one normalized index — so queries, correlations, and investigations work across all of them simultaneously.

Unified Ingestion

Continuously collect findings, evidence, and telemetry from every AiVRIC suite and third-party integration into one pipeline.

Evidence Normalization

Apply a consistent schema to security findings, compliance controls, AI signals, and risk records for cross-suite querying.

Cross-Suite Querying

Run a single query that spans CSPM posture, GRC evidence, AI risk scores, and offense findings simultaneously.

Real-Time Sync

Changes in any suite propagate to the shared layer within seconds, keeping dashboards and agents operating on current data.

How it works
1
Collect

Findings, evidence, and telemetry stream into the Fabric ingestion pipeline from every connected suite and integration.

2
Normalize & Tag

Each record is mapped to the Fabric schema, tagged by policy, control framework, and identity context.

3
Index & Serve

Normalized records are indexed for low-latency retrieval by GenAI Chat, dashboards, agents, and export workflows.

Outcomes you can expect

  • Eliminate data silos that cause inconsistent findings across security and compliance tools.
  • Accelerate cross-suite investigations from hours to seconds with unified querying.
  • Ensure every evidence package, dashboard, and AI response draws from the same verified source.

Ready to see Shared Data Layer in action?

Join a live platform walkthrough and see the Fabric at work across your environment.

Request a Live Demo
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