Getting Started
  • Protecto Overview
    • Introduction
      • Quickstart Guide
      • Protecto Vault
        • What is a token?
        • Token customization
        • Authentication
        • Tokenization APIs
          • Masking
            • Mask with token
            • Mask with format and token
            • Identify and mask (Auto-detect)
          • Unmasking
          • What happens if an API fails?
        • Asynchronous API's
        • Bulk data
      • Add new data source
        • Snowflake
          • Create and grant access to Protecto
          • Add Snowflake to Protecto
        • Salesforce
          • Create connected app and user
            • Steps to create connected app
            • Steps to create Protecto user
          • Add Salesforce to Protecto
        • Azure SQL
          • Connect using AD Application credentials
          • Connect using database user credentials
        • Databricks
          • Add Service principal (Azure AD Application) to Databricks
          • Steps to create Azure Databricks Cluster
          • Steps to create Databricks python notebook and schedule job
        • Redshift
          • Create and grant access to Protecto user
          • Add Redshift to Protecto
      • Protecto FAQ's
        • 1. What are the steps after we sign up for a Protecto account?
        • 2. Can I sign up for a free account? How long is the trial period?
        • 3. What is Protecto license key? How can I get a new license key?
        • 4. How do I extend the trial period?
        • 5. What is the Protecto pricing model?
        • 6. How do I cancel my account?
        • 7. How do I unsubscribe / opt-out from emails?
      • Compliance User Guide
        • Risk Identification: Key Definitions
        • Understanding Risks
          • Find assets with severe breach risk
          • Filter assets by breach risk level
          • Find assets with other privacy risks
        • Understanding Usage
          • Find the data assets that were accessed
          • Find the data assets that are not used
        • Add Tags & Classification
          • Add tags globally
          • Classify tags to the categories
          • Add tags with category to the data assets
          • Remove tags with category from the data assets
        • Governance
          • Find all data assets
          • Add/delete purposes
          • Assign data owner for a data asset
          • Add/delete consent, data subject type and location for a data asset
          • Add/update retention time for a data asset
          • Add/update minor data for a data asset
        • Generate Compliance Reports
          • ROPA (Records of Processing Activities)
          • DPIA (Data Protection Impact Assessment)
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  1. Protecto Overview
  2. Introduction
  3. Protecto Vault
  4. Tokenization APIs
  5. Masking

Identify and mask (Auto-detect)

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Last updated 1 year ago

This method automatically identifies and masks personal/sensitive data within specific sentences.

Note: To send a request with the authentication token, include the following in headers:

Headers: {"Authorization": "Bearer <AUTH_TOKEN>"}

Endpoint:

https://protecto.ai/api/vault/mask

Request Method: PUT

Request Payload: eg :

{"mask": [{ "value": “George Williams lives in Washington”}]}

Parameters:

· value (string): Sensitive data value that needs to be masked.

Response:

{"data": [{"value": " George Williams lives in Washington“, "token_value”: “243 3466 lives in eweww”}]}

Response Parameters:

· token_value (string): Generated token replacing the sensitive data.

Advantage:

This approach is particularly useful when users may not be aware of all the sensitive data present in a given text or when dealing with large volumes of data where manual identification is impractical. Auto-detect masking offers the following advantages:

  1. Effortless Sensitive Data Identification: Users do not need to manually identify and specify sensitive data elements within the text. The system automatically detects patterns and formats indicative of sensitive information, such as names, phone numbers, credit card numbers, and more.

  2. Comprehensive Data Protection: Auto-detect masking ensures that all instances of sensitive data within a dataset are masked, providing a comprehensive approach to data security. This is especially valuable for scenarios where users may not be aware of all the potential data types that need protection

  3. Reduced Human Error: Automated detection reduces the risk of human errors that can occur during manual identification and specification of sensitive data. It enhances accuracy and consistency in data protection processes.

  4. Time and Resource Savings: Auto-detect masking saves time and effort, particularly when dealing with large amounts of data. Users do not need to spend time identifying each instance of sensitive data, allowing them to focus on other tasks.