Text classification
Given the following text input:
Please update my records with the following information: Email address: foo@example.com National Provider Identifier: 1245319599 Driver's license: AC333991
The output is a list of findings, organized into the following categories:
-   InfoType
-   Likelihood
-  Offset(Where in the string the potentialInfoTypewas found)
Example output is shown in the table below.
| InfoType | Likelihood | Offset | 
|---|---|---|
| US_HEALTHCARE_NPI | VERY_LIKELY | 122 | 
| EMAIL_ADDRESS | LIKELY | 72 | 
| US_DRIVERS_LICENSE_NUMBER | LIKELY | 155 | 
| CANADA_BC_PHN | VERY_UNLIKELY | 122 | 
| UK_TAXPAYER_REFERENCE | VERY_UNLIKELY | 122 | 
| CANADA_PASSPORT | VERY_UNLIKELY | 155 | 
Automatic text redaction
Automatic redaction produces an output with sensitive data matches removed instead of giving you a list of findings.
Example automation redaction input:
Please update my records with the following information: Email address: foo@example.com National Provider Identifier: 1245319599 Driver's license: AC333991
Example output using a placeholder of "***":
Please update my records with the following information: Email address: *** National Provider Identifier: *** Driver's license: ***
Resources
- For more information about using Sensitive Data Protection to redact text, see Redacting Sensitive Data From Text Content .
- For more information about using Sensitive Data Protection to de-identify sensitive data in text content—which includes "masking" sensitive data, replacing sensitive data with a "token" string, and encrypting and replacing sensitive data using a randomly generated or pre-determined key—see De-identifying sensitive data in text content .

