> For the complete documentation index, see [llms.txt](https://docs.modelmetry.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.modelmetry.com/evaluators/google.dlp-pii-detector.v1.md).

# DLP PII Detector (Google)

This evaluator utilizes Google's Cloud Data Loss Prevention (DLP) API to identify and classify potentially sensitive information within text data, ensuring compliance and safety across user interactions.

### Configuration <a href="#configuration" id="configuration"></a>

| Option                | Description                                                                      | Type       | Default                                                   | Required | Constraints                                                  |
| --------------------- | -------------------------------------------------------------------------------- | ---------- | --------------------------------------------------------- | -------- | ------------------------------------------------------------ |
| **InfoTypes**         | Types of information to detect, based on Google Cloud DLP documentation          | `[]string` | `["PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD_NUMBER"]` | `true`   | MinItems1                                                    |
| **MinimumLikelihood** | The minimum likelihood required for an information type to be considered a match | `string`   | `LIKELY`                                                  | `true`   | EnumVERY\_LIKELY, LIKELY, POSSIBLE, UNLIKELY, VERY\_UNLIKELY |

### Additional Information <a href="#additional-information" id="additional-information"></a>

The Google Cloud DLP PII Detector is a powerful tool for scanning text for personally identifiable information (PII), helping to maintain privacy and meet regulatory compliance requirements. For more detailed information on configuring and using this evaluator, refer to [Google's DLP documentation](https://cloud.google.com/sensitive-data-protection/docs/libraries).

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