Key Features

• Data Discovery: Use NER data patterns in Data Discovery jobs governed by Data Discovery policies.
• Data Anonymization: Anonymize sensitive information detected via NER patterns by selecting the Data Pattern Anonymization technique in anonymization policies and activating the corresponding patterns under Active Data Patterns.
• Real-time Anonymization: Enable real-time anonymization of entries containing NER data patterns by adding those patterns to Active Data Patterns.
• Data Privacy Masking: Mask NER data patterns when configuring Data Privacy for Now Assist.

Practical Data Patterns Included

The system includes predefined NER data patterns to identify various sensitive entity types, including but not limited to:

• Address: Partial or full street-level addresses excluding city, state, country, and zip code.
• City: Names of towns or cities worldwide.
• Country: Sovereign nations or territories.
• Date & Time: Absolute or relative dates and times smaller than a day.
• Job Position: Specific organizational roles or job titles.
• Location: Politically or geographically defined locations such as mountains, regions, or bodies of water.
• Nationality, Religious, or Political Groups (NRP): Person’s nationality or group affiliations.
• Organization: Names of companies or institutions.
• Person: Full personal names including first, middle, and last names.
• Salary: Numeric earnings values often with currency symbols.
• State: States, provinces, or similar regions worldwide.

Why This Matters

By using NER data patterns, ServiceNow customers can enhance their sensitive data discovery and anonymization efforts beyond traditional fixed pattern matching. This is critical for complying with data privacy regulations and protecting sensitive information effectively across their enterprise data landscape.

Expectations for Use

To leverage these NER patterns, customers must enable the feature via SKU and ensure the GenAI Controller is installed. They can then incorporate NER patterns into discovery, anonymization, masking, and real-time anonymization workflows to improve data privacy management across their environments.