Data Classification
Overview
Data classification is used to identify and categorize data flowing through AI systems based on sensitivity and risk.
It enables Peridot to enforce policies and control how data is used across models and integrations.
Why Data Classification Matters
AI systems often process sensitive information, including:
Customer data
Internal documents
Financial or operational data
Without classification:
Policies cannot be applied effectively
Sensitive data may be exposed
Governance becomes inconsistent
How Classification Works
Peridot classifies data using:
Content analysis
Metadata
Source systems
User context
Each request is evaluated and tagged with a classification level.
Classification Levels
Examples:
Public
Internal
Sensitive
Restricted
These levels can be customized per organization.
How Classification Is Used
Classification informs:
Policy enforcement
Routing decisions
Incident detection
Access control
Example
A request containing sensitive customer data:
Classified as “Sensitive”
Blocked from external models
Routed to approved system
Logged and monitored
In Production
Classification happens in real time
Results are used across all policies
All decisions are logged and auditable
Best Practices
Define clear classification levels
Continuously refine detection rules
Combine classification with policies
Next Steps
Apply classification in policies