---
sourceDocument: Australia Platform security
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/platform-security

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Platform security

ft:clusterId :

    - psec

bundleId :

    - psec

workflow :

    - Platform


---

# Default NER data patterns

# Default NER data patterns {#ariaid-title1}

Release version: Australia  
Updated November 5, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Default NER data patterns

Named Entity Recognition (NER) based discovery in ServiceNow enables identification of sensitive data that lacks fixed patterns, such as personal names, organizations, nationalities, and political affiliations.
This capability integrates with Data Privacy features to enhance discovery, anonymization, real-time masking, and protection of sensitive information using model-based data patterns.
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Activation requires a signed $0 SKU agreement and the latest GenAI Controller (sn.generative.ai) installed with admin privileges.

## Key Features

* **Data Discovery**: NER patterns can be applied in Data Discovery jobs via Data Discovery policies to find sensitive information.
* **Anonymization** : Supports anonymizing detected NER data patterns through Data Anonymization policies. To anonymize NER data inside text columns, use the **Data Pattern Anonymization** technique and add NER patterns to Active Data Patterns.
* **Real-time Anonymization and Masking**: Enables real-time protection by adding NER patterns to Active Data Patterns for anonymization or masking, including integration with ServiceNow Otto Data Privacy configurations.

## Key Outcomes

With NER data patterns, ServiceNow customers can:

* Discover complex, non-fixed pattern sensitive data accurately.
* Apply consistent anonymization policies to protect sensitive entities within structured and unstructured data.
* Enhance data privacy compliance through real-time masking and anonymization of recognized sensitive content.

## NER Data Pattern Types and Examples

ServiceNow supports multiple NER data types, each with specific matching criteria and examples to guide pattern recognition:

* **Address:** Partial or full location identifiers excluding city, state, country, zip code (e.g., "135 Roslea Rd").
* **City:** Names of cities or towns worldwide (e.g., "Hayward").
* **Country:** Names of sovereign nations or territories (e.g., "USA").
* **Date \& Time:** Absolute or relative dates and times smaller than a day (e.g., "22-07-1992").
* **Job Position:** Specific organizational roles (e.g., "senior software engineer").
* **Location:** Geopolitical or physical features like mountains or regions (e.g., "Himalayas").
* **Nationality, Religious or Political Groups (NRP):** Personal affiliations (e.g., "American Indian").
* **Organization:** Names of organizations or companies (e.g., "Abraham \& Lincoln co.").
* **Person:** Full personal names including first, middle, last names (e.g., "Abraham Lincoln").
* **Salary:** Numeric earnings with currency symbols (e.g., "$500").
* **State:** Names or codes of states, provinces, or regions (e.g., "CA").

Each data pattern includes examples of matching and non-matching values to assist in configuring accurate discovery and anonymization policies.  
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](https://www.servicenow.com/docs/hlv0Eq~iNgpBeuOvfRRy6Q "Active data patterns are only used for certain product capabilities.") 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](https://www.servicenow.com/docs/pXfDdeQdsZ0JCsVLkSjKhA "Use Data Discovery policies to scan specific tables and enable column based jobs.").
* Running Anonymization jobs using [Data Anonymization Policies](https://www.servicenow.com/docs/zw4nkOkoTPTsW3MGAQ8exA "Anonymization provides a way to easily transform data so that it is unidentifiable and more compliant with data privacy regulations.").  
  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](https://www.servicenow.com/docs/Y4kk8LmY9wniN9G4~3y5zw "Active data patterns are only used for certain product capabilities.").
* [Real time anonymization](https://www.servicenow.com/docs/JRxU_5YdUqmKnXYUrpQBVQ "Use the real time anonymization(RTA) policy to anonymize data entries in real time.") of entries containing NER data patterns.  
  Note:  
  This capability requires adding the NER data pattern to [Active Data Patterns](https://www.servicenow.com/docs/Y4kk8LmY9wniN9G4~3y5zw "Active data patterns are only used for certain product capabilities.").
* Masking NER data patterns when configuring [Data Privacy for ServiceNow Otto](https://www.servicenow.com/docs/FnoQmnfy1r_1DvqmTDcNiw "Set up and configure how to discover and anonymize sensitive data from generative AI prompts.").
{#default-ner-data-patterns__ul_vph_1r5_bjc}
{#default-ner-data-patterns__table_wfd_1ll_gxb__entry__5}

| 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 |   | Matching : * 135 Roslea Rd Hayward * \[135, Roslea Rd\] {#default-ner-data-patterns__ul_itk_y2v_m3c} Non matching : * New York, NY * Apt. 11 {#default-ner-data-patterns__ul_jtk_y2v_m3c} |
| City | The name of a city or town from regions and countries around the world. | CITY |   | Matching : * Hayward * Cleburne {#default-ner-data-patterns__ul_xcs_z2v_m3c} Non matching : * Switzerland * 87591 {#default-ner-data-patterns__ul_ycs_z2v_m3c} |
| Country | The name of a sovereign nation or territory. | COUNTRY |   | Matching : * USA * India {#default-ner-data-patterns__ul_zln_p3m_m3c} Non matching : * U-S-A * U.S.A. {#default-ner-data-patterns__ul_amn_p3m_m3c} |
| Date \& Time | Absolute or relative dates or periods or times smaller than a day. | DATE_TIME |   | Matching : * 22-07-1992 * 22/07/1992 * 07/22/1992 * 07-22-1992 * 07 12 1992 {#default-ner-data-patterns__ul_t4w_z2v_m3c} Non matching :   55 II IOO5 |
| Job position | A specific role or set of responsibilities within an organization, designated to be filled by an employee. | JOB_POSITION |   | Matching : * senior software engineer * Director * CSR * Lecturer {#default-ner-data-patterns__ul_tzc_r3m_m3c} Non matching :   sr software engineer |
| Location | Name of politically or geographically defined location (cities, provinces, countries, international regions, bodies of water, mountains | LOCATION |   | Matching : * Himalayas * Great Lakes * Mount Rainier {#default-ner-data-patterns__ul_fhb_1fv_m3c} Non matching : * Bay Of Bengal * The south {#default-ner-data-patterns__ul_ghb_1fv_m3c} |
| Nationality, religious or political groups (NRPs) | A person's nationality, religious or political group. | NRP |   | Matching : * American * Indian * Indo-american {#default-ner-data-patterns__ul_wlg_1fv_m3c} Non matching : * Bald * Handsome {#default-ner-data-patterns__ul_xlg_1fv_m3c} |
| Organization | Name of organization. | ORGANIZATION |   | Matching :   Abraham \& Lincoln co. Non matching :   Now india co |
| Person | A full person name, which can include first names, middle names or initials, and last names. | PERSON | Fred Luddy, Abel Tuter, Abraham Lincoln | Matching : * Fred Luddy * Abel Tuter * Abraham Lincoln {#default-ner-data-patterns__ul_k5p_1fv_m3c} Non matching : * Fred * Toyota {#default-ner-data-patterns__ul_l5p_1fv_m3c} |
| Salary | A numeric value representing an individual's earnings, often accompanied by currency symbols. | SALARY |   | Matching :   my salary is $500, my salary is ₹500, my pay is 1.234,56 € Non matching :   40/hour |
| State | States, Provinces, Prefectures and regions around the world. | STATE |   | Matching : * CA * IN {#default-ner-data-patterns__ul_izs_1fv_m3c} Non matching : * Australia * Pacific Northwest {#default-ner-data-patterns__ul_jzs_1fv_m3c} |
[ ]

{#default-ner-data-patterns__table_wfd_1ll_gxb}