What Is Data Masking?

Data masking is a data security technique used to protect sensitive information by replacing it with anonymized yet realistic-looking data. This approach helps prevent unauthorized access to confidential data while still enabling its use in software testing, analytics, and development environments.

There are several methods of data masking, including static data masking, in which a copy of the dataset is altered, and dynamic data masking, which masks data in real time during access. Sensitive fields such as names, credit card numbers, or social security numbers are replaced with fictitious but structurally valid substitutes that retain format and usability.
Data masking plays a critical role in maintaining compliance with privacy regulations such as GDPR, HIPAA, and PCI DSS, making it an essential practice for organizations handling personally identifiable information (PII) or financial data.

Understanding data masking

When companies collect consumer information, privacy regulations require them to protect sensitive data, usually by masking it. Organizations create fictitious customer data by modifying real data using various techniques that replace the original numbers and letters with substitutes. 

These substitutes utilize a similar structure as the original dataset, resulting in a secure replacement. Without access to the original dataset, hackers and cybercriminals cannot reverse engineer confidential information.

Consider the following examples:

• Masking a credit card with a random string of numbers
• Shuffling dates
• Using “John Doe” as a placeholder for a customer’s name

Importance of data masking

Protecting sensitive information from unauthorized access is crucial due to the increasing prevalence of cyberthreats. In industries including healthcare, finance, and retail, where information is collected at a high rate, data obfuscation is crucial. Masking data helps strengthen the security of consumer information, such as credit card numbers, dates of birth, and social security numbers. 

Cybersecurity experts are alerting companies and consumers to the dangers of data breaches. Privacy regulations are tightening to increase fines for organizations that fail to protect the sensitive information they collect. These regulations include the following: 

• Health Insurance Portability and Accountability Act (HIPAA)
• California Consumer Privacy Act (CCPA)
• General Data Protection Regulation (GDPR)
• Payment Card Industry Data Security Standard (PCI DSS)

These standards play a crucial role in preventing unauthorized users from accessing information and in overall data protection. However, they also create obstacles for companies that analyze data to use it effectively. 

Types of data masking

Companies can use several types of data masking to secure sensitive data.

Static data masking

Static data masking (SDM) is commonly used to create anonymized data sets for testing and training purposes. This type is used for data that does not change frequently, such as social security numbers. SDM involves applying a fixed set of rules to information before storing or sharing it. These rules are predefined and ensure consistent masking across touchpoints. The values generated mirror the original data and produce identical analytical results.

Dynamic data masking

Dynamic data masking (DDM) is primarily used to implement data security based on roles such as medical record handling or customer support. When users request data, DDM transforms, blocks access, or obscures sensitive information in real time based on user permissions.

For example, a hospital call center operator handles a question from a customer. This operator can only view the sensitive fields they have privileges to view, such as birth date, social security number, and credit card number.

Deterministic vs. non-deterministic masking

Deterministic data masking is akin to a cryptogram, where the same output value is replaced with the same input value. For example, if the number 24 is replaced with 36 in one instance, any subsequent 24 will be masked with 36. This type of data masking guarantees consistent pseudonymization across data sets, and is crucial for maintaining referential integrity and enabling accurate testing and development, as the relationships within the data are preserved.

Conversely, non-deterministic masking is randomly generated. Sensitive data is replaced with unpredictable values each time it’s processed. For example, if 24 is replaced with 36 in one instance, it will be masked with a different number next time. Non-deterministic masking is used when randomized data anonymization is crucial, because it increases security. The randomization of information makes it harder for cybercriminals to reverse engineer the original data.

How data masking works

Data masking is a complex process that utilizes several techniques and masking tools for effectiveness.

Data masking process

The data masking process can be broken down into simple steps:

1. Discovery: Security and business experts collaborate to produce a comprehensive data record across their organization. Once this record is created, they determine the types of information and the sensitivity level of each data type.
2. Survey and analysis: Some datatypes may require a separate masking technique based on the determined sensitivity level. A company’s information security director determines the appropriate data management method.

Tools and techniques used in data masking

Organizations have access to numerous tools and software solutions to mask their data. Informatica, IBM, Guardium, and Microsoft SQL Server are the most popular, each offering unique benefits and preferred masking techniques. Some of the most common include:

• Encryption-based masking: This type utilizes cryptographic algorithms to encrypt sensitive data into an unreadable format. Authorized users employ decryption keys to access the original data. While this technique offers a higher level of security, it can affect data analysis.
• Shuffling: Shuffling randomly reorders values within a column using a secure formula. The results look identical but don’t reveal personal information. This technique preserves the relationships within the masked values for accurate analysis.
• Nulling out: Also known as blanking, this technique replaces sensitive data with blank spaces or null values, effectively removing it from the dataset. This redaction solution is ideal when you want to retain the structure of the data field but need to conceal specific information.
• Tokenization: This technique replaces complex data with a token or value that is randomly generated. The original data is stored separately, while the token is used in analysis and processing. This type of data masking minimizes the risk of sensitive information being exposed while maintaining its referential integrity.

EDB Postgres^® AI includes data redaction and masking features to limit sensitive data exposure, protecting critical information by obfuscating data in real-time — like displaying a credit card number as xxxx-xxxx-xxxx-1234 or a Social Security number as xxx-xx-123. 

Examples of data masking in action

Organizations mask data every day to protect it. These common use cases include the work of financial firms and hospitals. Financial firms use databases to store sensitive financial data relating to clients’ investments, such as account balances, social security numbers, account numbers, and addresses. These companies can anonymize this sensitive data by replacing it with dummy values. The fake data adheres to security standards and regulations while allowing authorized users to access it. 

Hospitals and health systems use electronic health record (EHR) systems to store and manage a wide range of patient information, such as medical histories, names, phone numbers, and identification numbers. Health systems must comply with HIPAA regulations while maintaining data integrity for research purposes. Shuffling, tokenization, and user restrictions are standard masking techniques for hospitals, because the integrity of the information is preserved while exposure to potential lawsuits is also reduced.

Benefits of data masking

Data masking is a basic form of data protection that provides peace of mind to consumers while offering several benefits to organizations, such as:

Enhanced data security

Data masking improves data security by reducing the risk of data breaches. If cyberattackers breach a database, sensitive information is concealed and unrecognizable, rendering it useless. It also protects against insider threats, such as those posed by negligent or malicious employees. If anyone attempts unauthorized access to sensitive data, data masking helps limit the damage by making the data unreadable. 

Regulatory compliance

Data masking enables organizations to comply with data privacy regulations, such as CCPA and GDPR. Masking anonymizes personal data and prevents its exposure, which is essential in hospitals where patient health information and history are confidential and protected by law.

Safe testing and development

In some instances, research and development companies need to utilize produced data. Data masking replaces real data with authentic-looking fakes. These fakes maintain the integrity of the original data, without exposing sensitive information. Organizations can share this replacement data with third-party developers and testing environments, enabling safe collaboration and data sharing while maintaining data security.

Data masking vs. data encryption

Data masking and data encryption are both data security techniques that can be used to address regulatory compliance. However, they are distinct data privacy solutions. 

At its core, encryption is a type of data masking used in data transfers and storage. Encryption can be applied to data where the information doesn’t need to be utilized in real time. It uses a sophisticated algorithm to encode original data into unreadable ciphertext, and then it reverts this text into a readable format via a decryption key. Encryption protects financial account numbers, payment card information (PCI), and personally identifiable information (PII).

Challenges of data masking

As with any security solution, there are a few challenges associated with data masking. These barriers include:

Masking complexity

Finding the ideal data masking method is essential to protect information while keeping it usable. If you choose an incorrect method, data may be exposed or formatted incorrectly, which produces inaccurate results. Masking involves applying a specific algorithm to the data, which might not align with systems that use different structures, formats, or standards. This discrepancy may affect an organization’s ability to exchange or integrate data or use it for discrete purposes.

Balancing masking with data usability

Research and analytics depend on preserving the original attributes and frequency of any associated data categories to ensure accuracy. This can be challenging in masking processes like tokenization or randomization.

Evaluate your data protection strategies

Data masking is a critical element of organizational compliance with privacy regulations. However, organizations collect a growing volume of structured and unstructured production data, making masking increasingly complex. Additionally, regulations and requirements change. To remain compliant, you must regularly monitor and audit your masked data.

Masking the data you collect allows you to use it securely while protecting sensitive consumer data from hacks and breaches. If your organization collects and handles customer financial or identification information, it’s time to evaluate your data protection strategies. Many organizations rely on third-party data engineering tools for data masking. However, EDB allows organizations to perform data masking at the database level, enhancing convenience and flexibility. If you’re looking for a flexible and secure data masking solution to ensure you’re in compliance and protected from risk, contact EDB today to learn more.