Data masking techniques and best practices

What is data masking?

Last updated on April 24, 2024 

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Data masking techniques and best practices ensure your data can’t be identified or reverse-engineered while retaining relational integrity and usability.


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Data masking: A must for any enterprise

With the proliferation of personal data – collected by enterprises across all industries – the need for protecting individual privacy is paramount. One way to protect Personally Identifiable Information (PII) is by masking data (i.e., consistently changing names, or including only the last 4 digits of a credit card or Social Security Number.

This reference guide explores today’s data masking techniques, the challenges they pose for enterprises, and a novel approach, based on business entities, that addresses these challenges in the most comprehensive manner.


What is data masking?

Data masking is a method for protecting personal or sensitive data that creates a version of the data that can’t be identified or reverse-engineered while retaining referential integrity, and usability.

The most common types of data that need masking are:

  • Personally Identifiable Information (PII) such as names, passport, social security, and telephone numbers

  • Protected Health Information (PHI) about an individual’s health status, care, or payment data

  • Protected financial data, as mandated by the Payment Card Industry Data Security Standard (PCI-DSS) and the US Federal Trade Commission (FTC) acts and safeguards

  • Test data, associated with the Software Development Life Cycle (SDLC)

Masked data is generally used in non-production environments – such as software development, data science, and testing – that don’t require the original production data.

Simply defined, data masking combines the processes and tools for making sensitive data unrecognizable to – yet functional for – authorized users.


Data masking vs encryption and tokenization

Data anonymization refers to a variety of processes that transform data into another form, in order to secure and protect it. The 3 most common data anonymization methods are data masking, data encryption, and data tokenization. While data masking is irreversible, encryption and tokenization are both reversible in the sense that the original values can be derived from the obscured data. Here’s a brief explanation of the 3 methods:

Data masking

Data masking tools substitute realistic, but fake, data for the original values, to ensure data privacy. Development, support, data science, business intelligence, testing, and training teams use masked data in order to make use of a dataset without exposing real data to any risk. There are many techniques for masking data, such as data scrambling, data blinding, or data shuffling, which will be explained in greater detail later on. The process of permanently removing all PII from sensitive data is also known as data sanitization. There is no algorithm to recover the original values of masked data.

Data encryption

While data encryption is very secure, data teams can’t analyze or work with encrypted data. The more complex the encryption algorithm, the safer the data will be from unauthorized access. In a data masking vs encryption comparison, encryption is ideal for storing or transferring sensitive data securely.

Data tokenization

Data tokenization, which substitutes a sensitive data element with random characters (tokens), is a reversible process. The tokens can be mapped back to the original data, with the mappings stored in a secure “data vault”.

In a data masking vs tokenization comparison, tokenization supports operations like processing a credit card payment without revealing the credit card number. The real data never leaves the organization, and can’t be seen or decrypted by a third-party processor.


Data masking-1

Data tokenization supports the Payment Card Industry’s Data Security.

The fact that data masking is not reversible makes it more secure, and less costly, than tokenization.

Data masking software maintains referential context and relational integrity across systems and databases, which is critical in software testing and data analysis.

In the case of anonymized data, integrity means that the dataset maintains its validity and consistency, despite undergoing data de-identification. For example, a real credit card number can be replaced by any 16-digit value that is validated by the “CheckSum” function. Once anonymized, the same (new) value must be used consistently across all systems.

In short, masked data is usable in its anonymized form, and once the data is masked, its original value can never be recovered.

There are 2 major differences between data masking and encryption/tokenization:

  1. Masked data is usable in its anonymized form.

  2. Once data is masked, the original value can’t be recovered.


Why data masking?

Data masking solutions are important to enterprises because they enable them to:

  • Achieve compliance with privacy laws, like CPRA, GDPR and HIPAA, by reducing the risk of exposing personal or sensitive data, as one aspect of the total compliance picture.

  • Protect data in testing environments from cyber-attacks, while preserving its usability and consistency.

  • Reduce the risk of data sharing, e.g., in the case of cloud migrations, or when integrating with third-party apps.

While data masking tools are now needed more than ever before, to effectively safeguard sensitive data and to address the following challenges:

Regulatory compliance

Highly regulated industries, like financial services and healthcare, already operate under strict privacy regulations. Besides adhering to regional standards, such as Europe’s GDPR, California’s CPRA, or Brazil’s LGPD, companies in these fields must also comply with the Payment Card Industry Data Security Standard (PCI DSS), and the Health Insurance Portability and Accountability Act (HIPAA).

Insider threats

Many employees and third-party contractors access enterprise systems on a regular basis, for example for software testing or analytics purposes. Production systems are particularly vulnerable, because sensitive information is often used in development, testing, and other pre-production environments. With insider threats rising 47% since 2018, according to the Ponemon Institute report, protecting sensitive data costs companies an average of $200,000 per year.

External threats

In 2020, personal data was compromised in 58% of the data breaches, states a Verizon report. The study further indicates that in 72% of the cases, the victims were large enterprises. With the vast volume, variety and velocity of enterprise data, it is no wonder that breaches proliferate. Taking measures to protect sensitive data in non-production environments will significantly reduce the risk.

Data governance

Your data masking tool should be secured with Role-Based Access Control (RBAC). While static data masking obscures a single dataset, dynamic data masking provides more granular controls. With dynamic data masking, permissions can be granted or denied at many different levels. Only those with the appropriate access rights can access the real data. Others will see only the parts that they are allowed to see. You should also be able to apply different masking policies to different users.


Data masking is highly customizable. Data teams can choose which data fields get masked, and how to select and format each substitute value. For example, every Social Security Number (SSN) has the format xxx-xx-xxxx, where “x” is a number from 0 to 9. They can substitute the first five digits with the letter x, or all 9 numbers with other random numbers, according to their needs.


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  • Market description, including dynamic and static data masking techniques
  • Critical capabilities, such as PII discovery, rule management, operations, and reporting
  • Data masking vendors, broken down by categor
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Types of data masking

Over time, many types of data masking have evolved to provide more sophistication, flexibility and security, including:

Static data masking

Non-production environments, such as those used for analytics, testing, training, and development purposes, often source data from production systems. In such cases, sensitive data is protected with static data masking, a one-way transformation ensuring that the masking process cannot be undone. When it comes to testing and analytics, repeatability is a key concept because using the same input data delivers the same results. This requires the masked data values to persist, over time, and through multiple extractions.

For software testing, static data masking is usually employed on a copy of a production database. Advanced masking tools make data look real enough to enable software development and testing, without exposing the original values.

Dynamic data masking

Dynamic data masking is used to protect, obscure, or block access to, sensitive data. While prevalent in production systems, it is also used when testers or data scientists require real data. Dynamic data masking is performed in real time, in response to a data request. When the data is located in multiple source systems, masking consistency is difficult, especially when dealing with disparate environments, and a wide variety of technologies. Dynamic data masking protects sensitive data on demand.

Dynamic data masking automatically streams data from a production environment, to avoid storing the masked data in a separate database. As a rule, it’s used for role-based security for applications – such as handling customer queries, or processing sensitive data, like health records – and in read-only scenarios, so that the masked data doesn’t get written back to the production system.

This technique is frequently used in customer service applications to ensure that support personnel can access the data they need to assist customers while masking sensitive information like credit card numbers or personal identifiers – to maintain privacy and compliance with data protection regulations.

On-the-fly data masking

When analytics or test data is extracted from production systems, staging sites are often used to integrate, cleanse, and transform the data, before masking it. The masked data is then delivered to the analytics or testing environment. This multi-stage process is slow, cumbersome, and risky due to the possible exposure of private data.

On-the-fly data masking is performed on data as it moves from one environment to another, such as from production, to development or test. It’s ideal for enterprises engaging in continuous software development and large-scale data integrations. A subset of the masked data is generally delivered to authorized users upon request, because keeping a backup of all the masked data is inefficient and impractical.

Statistical data masking

Statistical data masking ensures that any masked data retains the same statistical characteristics and patterns as the real-world data it represents – such as the distribution, mean, and standard deviation. When production data is statistically masked, unauthorized users have great difficulty extracting any information of value afterwards.

Test data masking

Software applications require extensive testing before they can be released into production. Test data management tools that provision production data for testing must mask the test data to protect sensitive information. For example, in a legacy modernization program, the modernized software components must undergo continuous testing, making test data masking a key component in the testing process. Masking data with referential context and relational integrity – from production systems, to the test environments – is critical.

Unstructured data masking

When it comes to protecting data privacy, regulations do not differentiate between structured and unstructured data. Scanned documents and image files, such as insurance claims, bank checks, and medical records, contain sensitive data stored as images. Many different formats (e.g., pdf, png, csv, email, and Office docs) are used daily by enterprises in their regular interactions with individuals. With the potential for so much sensitive data to be exposed in unstructured files, the need for unstructured data masking is obvious.


data masking of unstructured data
Masking of unstructured data is particularly important in
the financial services industry due to strict regulations.


Data masking techniques

There are several core data masking techniques associated with data masking, as indicated in the following table:

Technique How it works Notes
Data anonymization Permanently replaces PII with fake, but realistic, data Protects data privacy and supports testing / analytics
Pseudonymization Swaps PII with random values while securely storing the original data when needed Applies to unstructured as well as structured data
Encrypted lookup substitution Creates a lookup table with alternative values for PII Prevents data breaches by encrypting the table
Redaction Replaces a field containing PII with generic values, completely or partially Useful when PII isn’t required or when dynamic data masking is employed
Shuffling Randomly inserts other masked data instead of redacting Scrambles the real data in a dataset across multiple records
Date aging Conceals confidential dates by applying random date transformations Requires assurance that the new dates are consistent with the rest of the data
Nulling out Protects PII by applying a null value to a data column Prevents unauthorized viewing


Data masking methods
Data masking techniques are applied to create compliant,
realistic data for software testing and analytics.


Data masking challenges

Not only must the altered data retain the basic characteristics of the original data, it must also be transformed enough to eliminate the risk of exposure, while retaining referential integrity.

Enterprise IT landscapes typically have many production systems, that are deployed on premises and in the cloud, across a wide variety of technologies. To mask data effectively, here’s a checklist of must haves:

  1. Format preservation

    Your data masking tool must understand your data and what it represents. When the real data is replaced with fake data, it should preserve the original format. This capability is essential for data threads that require a specific order, such as dates.

  2. Referential integrity

    Relational database tables are connected through primary keys. When your masking solution hides or substitutes the values of a table’s primary key, those values must be consistently changed across all databases. For example, if Rick Smith is masked as Sam Jones, that identity must be consistent wherever it resides.

  3. PII discovery

    Although PII is scattered across many different databases, the right data masking tool should be able to discover where it’s hiding.

  4. Data governance

    Data access policies – based on role, location, or permissions – must be established and adhered to.

  5. Scalability

    Real-time access to structured and unstructured data and mass/batch data extraction must be ensured.

  6. Integration

    On-prem or cloud integration with any data source, technology, or vendor is a must, with connections to relational databases, NoSQL sources, legacy systems, message queues, flat files, XML documents, and more.


Data masking best practices

Here are the most common data masking best practices to assure data security and compliance.

  • Identify where your sensitive data resides

    Learn the location, access, movement, and usage of your sensitive data across your systems and environments.

  • Determine the right data masking techniques

    Choose the most appropriate data masking methods for your data, based on its sensitivity, usage, and security policies. Data masking techniques include anonymization, pseudonymization, encrypted lookup substitution, redaction, shuffling, date aging, and nulling out.

  • Test your data masking

    Verify that your data masking techniques produce the expected results, and that the masked data is realistic and functional enough for your needs.

  • Keep your data masking techniques secure

    Ensure that only authorized personnel can access and modify your data masking algorithms, and that they’re stored and managed securely.

  • Ensure referential integrity

    Make sure that the same data masking technique is applied consistently to the same type of data across your systems, to maintain the relationships and logic of the data.

Data masking software
By managing data with a business entity approach,
referential integrity and consistency are ensured.


Data masking by business entity

Entity-based data masking resolves the most common data masking challenges, while enabling data masking best practices. It masks all of the sensitive data associated with a specific business entity – e.g., customer, loan, order, or payment – and makes it accessible to authorized data consumers based on role-based access controls.

While other data protection methods store sensitive data in a staging environment, entity-based data masking ingests, masks, and delivers masked data inflight.

Here's how it works:

  1. The business entity dataset is ingested from all relevant sources.

  2. The sensitive data is masked with referential integrity maintained.

  3. The resultant dataset is securely delivered to downstream systems.

With business entities, it’s easy to protect data – at rest and in transit – for data analytics, software testing, and training environments. The entity-based approach supports structured and unstructured data masking, static and dynamic data masking, test data masking, and more. Images, PDFs, text, and XML files that may contain PII are protected, while operational and analytical workloads continue to run without interruption. 

Enterprises requiring the full range of the latest data masking capabilities, and wishing to avoid the vulnerabilities related to conventional methods, a business entity approach is the right way to go.
Data masking via customizable functions
Data masking substitutes real information with random characters.


Summary and recommendations

Data masking has become an important part of enterprise efforts to comply with privacy protection regulations, and to address emerging generative AI data outputs. 

Today, the sheer volume of structured and unstructured data, the challenging regulatory environment, and the demand for lifelike masked data require highly sophisticated data masking tools. 

The wide variety of data sources (mainframe, SAP, and noSQL databases) make automated PII discovery and masking even more complex – especially with every vendor touting their own data masking tools.

A business entity approach allows enterprises to consolidate all their data extraction and masking efforts under one roof, dramatically reducing the cost of hardware and software, the need for multiple vendor masking licenses, and the people required to manage a multitude of different tasks.

K2view data masking tools mask any kind of structured or unstructured data, inflight, from any source, statically or with RBAC. They automatically discover PII, wherever it may be, with complete referential integrity across all systems.

And, with K2view, you can produce compliance reports on your data masking status for internal and external auditors, protecting your organization from data breaches and resultant reputational and financial damages in the process. 

Data masking FAQs

What is data masking?

Data masking protects sensitive information in a database by replacing it with a disguised version of the data. Data masking ensures that any Personally Identifiable Information (PII) remains secure and confidential, but permits the data to be used for development, testing, and other legitimate purposes. By masking data, enterprises assure data privacy and security, reduce the risk of mass data breaches, and comply with privacy regulations such as the GDPR, CCPA, and HIPAA.

Why is data masking important?

Data masking is important to organizations for the following reasons:

  • Data security: If masked data is breached, the original information it replaced is kept safe and protected.
  • Test data management: Masking data is a safe alternative to using real production data needed for test data management tools.
  • CI/CD: Continuous Integration / Continuous Delivery (CI/CD) in DevOps requires clean and usable data on demand. Dynamic data masking allows DevOps teams to quickly provision, use, and test new applications. 
  • Compliance: As the amount and strictness of data privacy regulations grow, data masking and synthetic data generation play a critical role in data-intensive companies.
  • Customer 360: By masking data associated with Customer 360 use cases, companies gain access to representative and insightful data to enhance customer experiences, while protecting PII.
  • Third-party protection: Enterprises must mask any data that’s processed by, or that integrates with, third-party vendors to preempt any breaches in the supply chain

What are the different types of data masking?

The different types of data masking include:

  • Data anonymization, which removes or encrypts the personal data found in a dataset.
  • Data pseudonymization, which substitutes PII, such as a name or Social Security Number, with a fake name or figures.
  • Encrypted lookup substitution, which provides a table indicating realistic alternative values to personal data. 
  • Redaction, which replaces personal data with generic values in testing and development environments.
  • Shuffling, which randomly inserts other masked data instead of substituting data with generic values. 
  • Data aging, which applies policies to each data field to conceal the true date. For example, you can set back the dates by 150 or 1,700 days, to maximize concealment.
  • Nulling out, which gives a null value to a data column, making it invisible to unauthorized users.

What’s the difference between data masking and data tokenization?

Data masking best practices call for the substitution of real, sensitive data with fake, yet lifelike, data, in order to maintain its ability to carry out business processes. Masked data can’t be reverse-engineered. Data masking replaces personal or sensitive information with random values, without any way to reveal the original ones. 

Data tokenization obfuscates sensitive data by replacing it with a meaningless token, for use in databases or internal systems. The tokenization of data process secures data at rest, and in motion. If somebody needs the real data value, the token can be “detokenized” back to its original state.

What’s the difference between data masking and synthetic data generation?

Data masking tools protect personal information by replacing real-life, sensitive data with jumbled, yet statistically equivalent, data. Although information that has undergone data masking can’t be reidentified, it remains functional for non-production environments. By using masked, instead of real-life, data, personal information is protected in the case of a breach.

Synthetic data generation does not obscure sensitive data. Instead, it builds artificial, yet lifelike, datasets, enabling development and testing teams to test new software quickly, while virtually eliminating any risks of non-compliance.

What’s the difference between data masking and data encryption?

Data encryption and data masking techniques are two separate approaches to data privacy management. 

Data masking replaces real, sensitive data with fake, yet realistic, data. Although masked data can’t be reverse-engineered or identified, it’s still functional for software testing and data science.

Data encryption, which converts plaintext into incomprehensible ciphertext, employs a mathematical algorithm that acts as a cryptographic key. Those with access to the key can view the original, plaintext data.

Unlike masked data, encrypted data is vulnerable to data breaches via hacking or social engineering.

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