# Trainable classifiers are used in data loss prevention and auto-labeling policies
Trainable classifiers are machine learning-based classifiers that recognize content by meaning and context rather than fixed patterns.
Source: https://maester.dev/docs/tests/ZTA.35036
## Overview
Trainable classifiers are machine learning-based classifiers that recognize content by meaning and context rather than fixed patterns. Unlike sensitive information types that match predefined formats, trainable classifiers can identify unstructured content like strategic plans, financial reports, or HR documents. Using trainable classifiers in auto-labeling policies, and data loss prevention (DLP) rules, extends protection to sensitive business content that pattern-based rules can't reliably capture.
## Remediation
- [Learn about trainable classifiers](https://learn.microsoft.com/purview/classifier-learn-about?wt.mc_id=zerotrustrecommendations_automation_content_cnl_csasci)
- [Get started with trainable classifiers](https://learn.microsoft.com/purview/trainable-classifiers-get-started-with?wt.mc_id=zerotrustrecommendations_automation_content_cnl_csasci)
## Run it
The check runs as part of the whole assessment, with the ZeroTrustAssessment module installed:
```powershell
Install-Module ZeroTrustAssessment -Scope CurrentUser
Connect-Maester
Invoke-Maester -Pack ZTA -SkipBuiltIn
```
Leave out `-SkipBuiltIn` to run Maester's own checks in the same run.
About this check
| | |
| --- | --- |
| Severity | Medium |
| Effort to fix | High |
| Impact on users | Medium |
| Pillar | Data, AI |
| Category | Advanced Classification |
| Product | [Purview](/docs/tests/products/purview) |
| Feature | Data classification |
| Area | Data protection |
| Licence | EXCHANGE_S_ENTERPRISE |
| Tenant | Workforce, External |
Product, feature, area and ATT&CK are Maester's: the assessment does not record them, so Maester keeps them in the pack's `taxonomy.json`.