Exploring Data Privacy

  • Release version: Washingtondc
  • Updated February 1, 2024
  • 2 minutes to read
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    Summary of Exploring Data Privacy

    The Data Privacy feature enables ServiceNow customers to classify sensitive data, remove personally identifiable information (PII) from user data in production instances, and anonymize data in non-production environments. This capability is crucial for developers who need to work with safe, non-sensitive data while ensuring their implementations function correctly.

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    Key Features

    • Data Classification: Identify and classify sensitive data using pre-defined criteria, enabling effective handling based on sensitivity levels. Administrators can label and group data within their instance.
    • User Data Anonymization: Administrators can choose to anonymize data for all users or specific subsets, replacing sensitive information with randomized values while preserving data structure.
    • Data Privacy Options: Two methods are available for data privacy: the Classic method, which involves data classification followed by anonymization jobs, and the Store App, which allows for classification and anonymization directly within the app.

    Key Outcomes

    By utilizing Data Privacy, customers can ensure that only classified data is anonymized, thereby protecting sensitive information. It's important to note that unstructured data, such as comments and attachments, are not anonymized. Additionally, administrators should be aware that integrations with single sign-on (SSO) systems may resynchronize user information, which could affect the permanence of data de-identification.

    Use Data Privacy to classify sensitive data and to remove personally identifiable information (PII) from user data in a production instance and anonymize data in non-production instances. Once anonymized, the user data is no longer considered regulated private information.

    Developers must work with data on non-production instances to ensure that their implementations are working as expected. While importing data from your production instance is a useful way to simulate production, it presents a security risk. Administrators can use data privacy to provide developers with data that does not contain private information to work safely in a non-production environment.

    Data classification

    Identify and classify your sensitive data according to pre-defined criteria determined by the level of sensitivity of the data types in your instance. Data sensitivity levels help determine how each type of classified data should be handled. There are several pre-defined classes provided with base level data privacy. Use the classification section of Data Privacy to label and group data within your instance. Add classes, view data class structure and classify data. Group data by type, using pre-defined or user-defined data classifications.

    User data anonymization

    As an administrator, you define whether to anonymize all information for all users or for a subset of users. When anonymized, data for the selected user records is replaced with randomized values or values you define. When replacing values, the data structure can be preserved using various techniques.

    Data privacy options

    • Data privacy (Classic): First use the data classification app to group your data by type, using pre-defined or user-defined data classifications. Then create data privacy techniques and jobs to anonymize PII.
    • Data privacy (Store App): Classify and anonymize your data all from within the data privacy app.

    Considerations

    • Only classified data can be anonymized. For information on data classes and classification, see Data classification (Classic) or Data classification Store App.
    • PII in logs and other auditing data are not anonymized.
    • Only structured data can be anonymized. Unstructured data, such as Journal fields, comments, attachments, and other fields where partial text may represent PII is not anonymized. See Supported field types for anonymization for more information.
    • Integrations with single sign-on (SSO) systems may resynchronize user information from their source of truth systems. There is no mechanism in place to ensure the permanency of the de-identification of sys_user data. For information on user administration and sys_users see User Administration.