Data masking is the best way to protect personal data, while ensuring its functionality. Learn about the most common data masking examples and use cases.
Table of Contents
What is Data Masking?
Top Data Masking Use Cases
5 Common Data Masking Examples
A Business Entity Approach Supports All Data Masking Examples
What is Data Masking?
Governments, companies, and health organizations all store personal information about their citizens, customer, employees, or patients. Privacy regulations mandate the protection of this data to reduce the risk of a data breach, and ensure that people’s private information remains private.
Data masking, also known as data anonymization, protects sensitive data by replacing personally identifiable information (PII) with fictional, yet statistically equivalent, data.
Production, analytics, and security teams alike favor data masking tools, because properly masked data cannot be identified or reverse-engineered. However, it can still be operationalized to support a variety of use cases. Today, rapid and secure data integration is a business necessity, making data masking software an invaluable asset.
In this article, we’ll cover key data masking use cases and techniques, and explain when to use each using real data masking examples.
Top Data Masking Use Cases
Many business units require access to data that might contain sensitive information, including DevOps, QA, customer success, sales, and more. With data masking, data consumers throughout the organization can access secure, functional data while eliminating bottlenecks.
Here are the most common examples of data masking use cases:
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Test data management
Software and application testing teams require realistic, complete, clean, compliant, and reliable data for test data management. Data masking allows testing teams to use their test data management tools without exposing sensitive production data to security risk. -
Data security
Although masked data looks realistic, it’s useless to a hacker in the event of a data breach. By masking data, data security teams ensure that real people or events cannot be identified – and masked data can’t be used for fraudulent transactions. -
Data governance
Many organizations consider data masking part of their data governance tools, to control who can access different types of data. Different types of data masking enable you to define access controls in different ways. For example, static data masking anonymizes data at rest, while dynamic data masking allows you to mask data in-flight, as well as set more detailed restrictions, such as defining permissions based on role or environment. -
Data compliance
Data masking software makes it easier to comply with ever-expanding data protection laws, such as PCI/DSS, HIPAA, GDPR, CPRA/CCPA, and LGPD, by preventing non-sanctioned use of real customer data. This is a major driver for enterprises, for whom compliance poses a significant challenge and cost. -
Customer 360
A 360-degree view of the customer (also called a single customer view) provides a complete, unified picture of the customer – one that integrates the interaction, transaction, and master data for each customer. Data masking protects customer data and supports compliance, while enabling on-demand access for business users.
5 Common Data Masking Examples
Here’s a list of common real-world scenarios in which organizations use data masking to keep sensitive data secure, as well as the most appropriate data masking techniques for each.
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Customer data masking: Masking personal customer information in a Customer Relationship Management (CRM) system
Almost all medium- to large-size companies today use a CRM to store and manage customer data, including names, phone numbers, email addresses, employment history, and more. Protecting customers’ privacy (as well as active and inactive leads) requires companies to take appropriate measures to ensure this data is not accessible to unauthorized users. Data masking is an effective method for anonymizing CRM data while maintaining data reporting and BI (business intelligence) functionality. Shuffling, data aging, and data pseudonymization are all effective data masking methods for this data masking example.
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Employee data masking: Masking personnel information in Human Capital Management (HCM) systems
Most large companies manage employee data in an HCM system. By applying data masking to an HCM, organizations can protect the sensitive information it contains, such as names, addresses, phone numbers, salary information, health insurance status, and more. Specific data masking techniques, such as data pseudonymization or shuffling, could keep sensitive employee information secure while ensuring that the data remains usable for legitimate purposes, by relevant data consumers.
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Financial data masking: Masking investment data in a stock brokerage
Financial firms use various systems to store and manage investment portfolios for their clients. System databases would contain a variety of sensitive financial information pertaining to customers’ investments, including account numbers, account balances, transaction histories, names, Social Security Numbers, addresses, and more. The firm could use data masking to protect the data, such as by replacing sensitive information with dummy values. Anonymized data would still be usable for authorized data consumers, while upholding data security standards and complying with regulations such as the Gramm-Leach-Bliley Act (GLBA).
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IP address data masking: Masking host or network interfaces and locations in log files
Companies that use log files to track the activities of users on its application, website, or network may choose to mask the IP addresses in the log files. Encrypted lookup substitution, redaction, or shuffling are all data masking techniques organizations could use to obscure real IP addresses. In this data masking example, the organization could still use masked IP addresses for testing or analytics purposes, while ensuring compliance with user privacy laws, such as GDPR.
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Medical data masking: Masking patient information in an Electric Health Record (EHR)
Hospital and health system databases, such as EHR systems, store and manage a wide range of personal information about patients, including names, address, phone numbers, medical histories, and more. To protect patients’ privacy and ensure compliance with relevant regulations (such as HIPAA), hospitals can mask EHR data using shuffling or data aging techniques to restrict access to unauthorized parties. In this data masking example, patient data can still be used for analysis and reporting, but would not expose the hospital or patient to risk.
A Business Entity Approach Supports All Data Masking Examples
No matter what the use case or specific data masking example, a business entity approach to data masking challenges offers unparalleled security and compliance assurance. Entity-based data masking aggregates, cleanses, and provisions data from disparate systems by business entity (like a customer, vendor, or order).
By masking the data for a specific business entity as a singular unit, relational integrity of the anonymized data is maintained – assuring it’s always consistent, and complete. The data for each business entity is managed in its own, encrypted. The PII in the Micro-Database is masked on the fly, based on predefined business rules.
Entity-based data masking can perform dynamic or static, structured or unstructured data masking, while maintaining referential integrity across all databases and systems. It also enables a broad range of data masking techniques, ensuring you can mask and protect data in the most effective and appropriate method in any given scenario.
