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GatorAId Overview

GatorAId is eXate's intelligent data classification engine. It analyses source data, identifies sensitive information, and automatically generates the corresponding Manifest, mapping your data structures to eXate Attributes.

By automating data discovery and classification, GatorAId significantly reduces the effort required to onboard new datasets. Instead of manually inspecting every table, column, or API field, users can review and refine automatically generated classifications before applying Policies and Privacy Enhancing Technologies (PETs).

GatorAId supports databases, files, and API payloads, helping organisations establish consistent data classification and protection across their data estate.

Key Capabilities

  • Automatically discovers and classifies sensitive data.
  • Generates Manifests ready for use by the eXate platform.
  • Uses eXate's built-in library of common data classifications.
  • Supports review and refinement of automatically generated classifications.
  • Accelerates onboarding of new applications, databases, and APIs.
  • Reduces manual effort and improves classification consistency across large datasets.
  • Enables faster deployment of Policies and Privacy Enhancing Technologies (PETs).

Classification Workflow

A typical GatorAId workflow consists of the following stages:

  1. Connect to the source data.
  2. Analyse the structure and contents of the source.
  3. Identify sensitive fields and classify them as eXate Attributes.
  4. Generate a Manifest describing the dataset.
  5. Review and refine the generated classifications where required.
  6. Publish the Manifest for use by Datagator, APIgator, GatorSet, and other eXate products.

Managing Classifications

Once a data source has been analysed, GatorAId generates a proposed Manifest containing the detected Attributes.

Before publishing the Manifest, users can:

  • Review detected classifications.
  • Modify or correct individual Attributes.
  • Add additional classifications where required.
  • Remove incorrect classifications.
  • Re-run classification after changes to the source data.

This review process allows organisations to combine automated classification with domain knowledge, ensuring Manifests accurately reflect their data before protection policies are applied.

Best Practices

  • Review generated classifications before publishing a Manifest.
  • Re-run classification after significant schema changes.
  • Extend the default classification library with organisation-specific Attributes where appropriate.
  • Reuse generated Manifests across multiple eXate products to ensure consistent protection.
  • Periodically review classifications as applications and data models evolve.

See also