Default NER data patterns
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Summary of Default NER data patterns
Named Entity Recognition (NER) based discovery in ServiceNow enables customers to identify sensitive data that does not adhere to fixed patterns, such as names, organizations, nationalities, and political affiliations. This capability supports enhanced data privacy by detecting and managing diverse data types beyond traditional pattern matching.
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To use NER data patterns, customers must have the latest GenAI Controller (sn.generative.ai) installed with admin privileges, and an additional $0 SKU must be signed to enable this feature.
Key Features
- Data Discovery Jobs: NER patterns can be utilized in Data Discovery policies to detect sensitive information.
- Anonymization Jobs: When creating Data Anonymization Policies, selecting the Data Pattern Anonymization technique allows anonymizing NER data within classified columns. Adding these patterns to Active Data Patterns is mandatory.
- Real-Time Anonymization: Entries containing NER data patterns can be anonymized in real time by including these patterns in Active Data Patterns.
- Masking for Now Assist: NER patterns support masking sensitive data during Data Privacy configurations for Now Assist.
NER Data Pattern Types and Examples
ServiceNow provides predefined NER data patterns for various entity types that aid in discovering and managing sensitive data:
- Address: Partial or full street-level locations excluding city, state, country, or zip code. Example: "135 Roslea Rd"
- City: Names of cities or towns worldwide. Example: "Hayward"
- Country: Sovereign nations or territories. Example: "USA"
- Date & Time: Absolute or relative dates and times smaller than a day. Example: "22-07-1992"
- Job Position: Specific organizational roles. Example: "senior software engineer"
- Location: Political or geographic locations like mountains or regions. Example: "Himalayas"
- Nationality, Religious or Political groups (NRP): Personal affiliations. Example: "American Indian"
- Organization: Names of organizations. Example: "Abraham & Lincoln co."
- Person: Full personal names including first, middle, or last names. Example: "Abraham Lincoln"
- Salary: Numeric values representing earnings with currency symbols. Example: "$500"
- State: States, provinces, or regions globally. Example: "CA"
These patterns improve the accuracy of sensitive data detection and facilitate compliance with data privacy requirements.
Use Named Entity Recognition (NER) based discovery to help discover sensitive data that does not follow fixed patterns.
Several Data Privacy capabilities support using Named Entity Recognition (NER) model data patterns to discover data such as names, organizations, nationalities, and political affiliations. Data patterns with the type Model
use this feature (see Configure Data Discovery patterns for more details).
Warning:
This feature requires an additional $0 SKU to be signed by the customer in order to be enabled. Also, customers must have the latest version of the GenAI Controller
sn.generative.ai
installed on their instance (which requires the admin role).NER data patterns can be used for:
- Running Data Discovery jobs using Data Discovery policies.
- Running Anonymization jobs using Data Anonymization Policies. Note:To anonymize any NER data pattern within text in a classified column, you need to select the Data Pattern Anonymization technique when creating the anonymization policy. Then, ensure that each NER data pattern is added to Active Data Patterns.
- Real time anonymization of entries containing NER data patterns. Note:This capability requires adding the NER data pattern to Active Data Patterns.
- Masking NER data patterns when configuring Data Privacy for Now Assist.
| Name | Description | Named Entity Recognition | Keywords | Examples |
|---|---|---|---|---|
| Address | A full or partial location identifier, including street names, unit / plot numbers, but excludes city, state, country and zip code. | ADDRESS |
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| City | The name of a city or town from regions and countries around the world. | CITY |
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| Country | The name of a sovereign nation or territory. | COUNTRY |
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| Date & Time | Absolute or relative dates or periods or times smaller than a day. | DATE_TIME |
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| Job position | A specific role or set of responsibilities within an organization, designated to be filled by an employee. | JOB_POSITION |
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| Location | Name of politically or geographically defined location (cities, provinces, countries, international regions, bodies of water, mountains | LOCATION |
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| Nationality, religious or political groups (NRPs) | A person's nationality, religious or political group. | NRP |
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| Organization | Name of organization. | ORGANIZATION |
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| Person | A full person name, which can include first names, middle names or initials, and last names. | PERSON | Fred Luddy, Abel Tuter, Abraham Lincoln |
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| Salary | A numeric value representing an individual's earnings, often accompanied by currency symbols. | SALARY |
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| State | States, Provinces, Prefectures and regions around the world. | STATE |
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