5 Data Masking Techniques and Why You Need Them

In today’s data-driven world, safeguarding sensitive information is more critical than ever. Data masking plays a vital role in protecting this data, especially when it needs to be shared or used in less secure environments. In this blog, we’ll explore five essential data masking techniques—substitution, shuffling, encryption, and more—explaining how each method works and why it’s important. Whether you’re aiming to meet compliance requirements or simply enhance data security, understanding these techniques will help you choose the right approach for your organization’s needs.

What Is Data Masking?

Data masking is a method of creating structurally similar but non-realistic versions of sensitive data. Masked data is useful for many purposes, including software testing, user training, and machine learning datasets. The intent of data masking processes is to protect the real data while providing a functional alternative when the real data is not needed.

Many organizations have robust data security controls to protect their production data but much less stringent controls when data is used for non-production purposes. This can create major security and compliance risks, especially when data is used by third parties outside of the organization’s control. Data masking can alleviate these concerns, ensuring that whenever data is transferred outside production environments, it is masked to prevent compromise.

An important principle of data masking is that the data format remains the same—only the values change. Data can be modified in a variety of ways, including encryption, character shuffling, or dictionary substitution. The objective is to ensure that unauthorized parties who obtain the masked data will have no way to reconstruct the original data.

Why Is Data Masking Important?

Data masking is critical to data security because it can limit the impact of a data breach. Consider a database table with sensitive financial data and less sensitive customer data. Sales personnel should have access to the customer data but not the financial data. By masking the financial data columns, an attacker cannot access the financial data if they compromise a salesperson’s account.

For the same reason, data masking can protect against insider threats. If a malicious employee attempts unauthorized access to data using their account, the actual data will be masked, limiting the damage they can do.

Compliance Requirements for Data Masking

Many data security regulations explicitly require data masking for their definition of protected data. As per the regulations, organizations have to ensure and also prove during audits that data is never exposed and will be protected from unauthorised access from within the organization and in case of an external attack. Some of the top regulations include:

GDPR (General Data Protection Regulation): A regulation enforced by the European Union, GDPR mandates that organizations safeguard personal data through appropriate technical and organizational measures. This includes employing data masking techniques to secure sensitive information.

HIPAA (Health Insurance Portability and Accountability Act): A U.S. law that mandates healthcare organizations to protect sensitive patient information. Data masking is one of the required measures to ensure the confidentiality of patient data.

PCI DSS (Payment Card Industry Data Security Standard): A set of security standards for organizations handling credit card transactions, PCI DSS requires the implementation of robust security measures, such as data masking, to protect credit card information.

Related content: Read our guide to data privacy

Real-World Applications of Data Masking

There are a number of industries and sectors where data masking is widely used. In most cases, companies that operate in these sectors are subject to regulatory mandates to protect specific types of data. So examples where data masking is used include:

Healthcare: Hospitals and healthcare providers use data masking to protect patient information, such as social security numbers and medical records, while allowing data to be used for research and testing.

Financial Services: Banks and financial institutions mask sensitive customer information like credit card numbers and account details to comply with regulations like PCI DSS while using the data in development and testing environments.

Retail: E-commerce companies mask customer data, such as payment information and addresses, to prevent exposure during data analysis and processing.

Telecommunications: Telecom companies use data masking to protect subscriber information when data is shared with third-party service providers for analytics or operational purposes.

Government Agencies: Government entities mask citizen data, including tax records and identification numbers, when sharing information across departments or with external contractors.

Types of Data Masking

While the purpose of masking data is to ensure security and authorised access, there are several ways in which masking can be implemented within applications. This could depend on regulatory and business needs, and sometimes also on the capabilities of the application from where data is accessed.

1. Static Data Masking

Static data masking is most commonly used for production database backups. It adjusts data so that it can be used for development, testing, and training without divulging sensitive information. It works as follows:

• Create a “golden copy” of the production database and move it to a secure location.
• Delete and mask sensitive data columns.
• Save a copy of the masked database to the insecure development, testing, or training environment.

2. Dynamic Data Masking (On-the-Fly Data Masking)

Unlike static methods, dynamic data masking does not require copying the database to a new environment to create masked data. Instead, data is kept in the original database, and a dynamic mechanism masks the relevant data depending on the authorization of the current user account. This ensures only authorized personnel can access sensitive data. Data masking is supported by all major commercial databases and can also be implemented via reverse proxy.

Dynamic data masking is suitable for organizations that continuously deploy software or have databases that are integrated with many other systems, making it impractical to perform static data masking.

Related content: Read our guide to data masking in Oracle (coming soon)

3. Deterministic Data Masking

Deterministic data masking maps original values to masked values, ensuring that data is always replaced consistently in all tables. This can be important to retain data integrity. For example, if the data contains names, the name “John” will always be replaced with “Samuel” in all relevant tables.

4. Statistical Data Obfuscation

Production data often includes numerical data and can be masked through statistical techniques. For example, data can be aggregated using summation, averages, or means, or the data can be described using histograms without sharing the underlying data values.

5. Unstructured Data Masking

Sensitive data is not limited to database tables. Scanned documents and image files, such as identity documents, insurance claims, and financial documents, can also contain sensitive data. Unstructured data masking relies on optical character recognition (OCR) and ensures that regions in an image containing sensitive information are blurred or replaced with alternative data.

Pathlock Data Sheet

Policy-Based Data Masking

Pathlock’s dynamic masking capability provides customers with fine-grained control over which sensitive data fields customers can mask for any specified user in the context of any situation.

Download the Data Sheet

Data Masking Techniques

There are multiple ways in which data can be masked from a user. This could depend on the type of data that needs to be masked and the level of security needed. Here are some notable data masking techniques.

Data Encryption

Data encryption is one of the most effective and widely used data masking techniques. Encryption algorithms convert raw data into an unreadable format, which users can only view using a secret decryption key. No one can read the data without the decryption key.

Encryption is suitable for data in action that must have the ability to revert to its original form. Encrypted data is only secure if access to the decryption key is limited to authenticated users. If a key is compromised, an unauthorized user could decrypt the sensitive data and view it in its raw form. Secure key management is thus essential.

Data Scrambling

Data scrambling is a simple masking method that jumbles the data into a random, unrecognizable string of characters. While this technique is easy to implement, it only works with certain data types. It is not the most secure data masking approach, making it unsuitable for many sensitive use cases.

For example, an employee ID might be a number—687514. Once scrambled, the ID will contain the same numbers in a different order—716854. An unauthorized user could easily guess the original code by playing around with the order.

Data Substitution

Substitution involves replacing the original data with different values. It is a particularly effective masking technique that preserves the data’s original qualities without exposing its real content. However, substitution only works with specific data types like lists of items in a certain category (e.g., a file containing user names). It is also a more complex data masking method to implement.

Data Shuffling

The shuffling technique is a form of data substitution that retains the original data but rearranges the order. For example, a randomized table of user names might have real names in different columns. The shuffled data looks real but doesn’t reveal the true information about the items it lists. The drawback of this approach is bad actors can easily reverse engineer the shuffled data if they understand the algorithm.

Data Pseudonymization

The term pseudonymization, coined by the EU’s GDPR, refers to various ways to protect personal information, including encryption, shuffling, and hashing.

The pseudonymization process prevents unwanted individuals from identifying individuals based on their data. This includes eliminating direct information about a person’s identity and any unknown indicators that a hacker could use to identify an individual. It is important to protect pseudonymized data by storing the encryption keys and any secrets to recover the original data securely and separately.

Data Nullification

Nullification applies null values to columns of data, preventing unauthorized users from seeing the real data. It’s easy to implement, but it can impact the data integrity—this is usually a problem in development and test environments.

Date/Number Variance

The variance technique helps obscure sensitive information about financial and other transactions, such as the dates of financial activities. For instance, date/number variance can mask salary tables by showing the salaries from lowest to highest. It can guarantee data integrity by applying a small variance (i.e., 5%) to all the salaries in the table.

Challenges in Implementing Data Masking

Not all data is the same. One needs to keep several factors in mind when implementing data masking. Being aware of these challenges can help choose the right solution for your specific data security requirements. Some of there challenges are:

1. Format Preservation: Ensuring that the masked data maintains the original format is critical. The data masking solution must accurately recognize and preserve the structure of various data types, such as ID numbers, telephone numbers, and email addresses. Any deviation from the original format can lead to errors in downstream processes.

2. Maintaining Referential Integrity: A significant challenge in data masking is ensuring consistent masking of sensitive data across multiple databases. For instance, a specific social security number should be masked identically in every database where it appears. Failing to maintain referential integrity can disrupt the functionality of enterprise systems, particularly in lower environments where testing occurs.

3. Ensuring Semantic Integrity: The masked data must still make sense within its context. For example, if a date of birth is altered during masking, corresponding fields like “Senior Citizen” status must also be updated accordingly. This ensures that business logic remains intact and the data continues to serve its intended purpose even after masking.

5. Gender Preservation: When masking names, it is important to maintain the correct gender association. If names are altered randomly without considering gender, the gender distribution in the dataset could be skewed, leading to inaccurate analysis and reporting.

5. Balancing Security and Usability: Striking the right balance between data security and usability is a major challenge. Overly aggressive masking can render data unusable, hindering operations and analysis. Conversely, insufficient masking can expose sensitive information, increasing the risk of unauthorized access. Achieving an optimal balance is key to effective data masking.

Dynamic Data Masking with Pathlock

The Dynamic Access Controls (DAC) product from Pathlock is built on an Attribute-Based Access Control (ABAC) security model. This enables a customizable and scalable, policy-based approach to data security, governance, and access control. Since the module’s dynamic data masking capabilities are governed by these easily configured ABAC policies, you can ensure that sensitive SAP data and transactions will be obfuscated without fail in scenarios where user access or actions indicate risk as defined in your organization’s custom policies. 

The module’s centralized ABAC policy administration capabilities ensure that you can easily define and apply granular, dynamic access control policies without the need for redundant policy administration efforts on a per-role basis. With an intuitive user interface, customizing the out-of-the-box policies or creating your own is as easy as selecting filters to apply and requires no technical expertise for configuration.

Ultimately, the DAC module provides a least-privilege security approach that goes beyond traditional access controls, allowing organizations to ensure data security while still allowing employees to perform their necessary duties on a need-to-know basis.

Get in touch with us for a demo and see for yourself how Pathlock can improve data security and reduce compliance risk with a fully dynamic data masking solution.