What is Data Masking? Types, Techniques and Best Practices

What is Data Masking?

The term data masking describes a method for producing a version of the data that conceals sensitive information while maintaining a structurally comparable appearance. This method, also known as data obfuscation, is used to safeguard private data, including personal information, that is kept in proprietary databases.

An approach that preserves sensitive data while enabling access to information is data masking. Data masking is the technique of changing the values of sensitive information while processing the same data format to make it look like the original.

A few instances of data masking include erasing sensitive information from data records, encrypting the data to prevent unauthorized users from accessing it without a decryption key, and substituting alternative symbols and characters for personally identifiable information and names.

Why is Data Masking Important?

Most sensitive information in an organization is kept in non-production settings for testing and development purposes. With data privacy and security regulations becoming increasingly important, data masking plays an important role in meeting essential security requirements. There are several reasons why data masking has become a key requirement for many organizations:

1. Third-Party Security: Today’s businesses depend more and more on apps and software from third parties. In the meantime, hostile nation states and malevolent attackers see the supply chain as a desirable target for enterprise database hacking. It can indeed be challenging to precisely evaluate and manage the security posture of your vendors. To prevent a supply chain compromise, data that integrates with or is handled by third-party providers, masking data is the technique of changing the values of sensitive information while processing the same data format to make it look like the original.

2. Compliance: By using data masking, businesses can adhere to data privacy regulations. Credit cards, medical records, transactions, events, and real individuals cannot be identified by unauthorized workers when data is appropriately masked. Data-intensive businesses are increasingly using masking data and creating synthetic data as a result of the growing number and severity of data protection requirements.

3. Data Protection: The original sensitive data it replaced is safe and secure even if the disguised data is compromised. While masking data can make it appear authentic, it cannot be utilized to identify a real person or to conduct fraudulent transactions. To maintain security in databases, apps, and internal systems as well as during cloud transit, data must be disguised, according to the best standards for data masking.

4. Manage Test Data: Teams that test software and applications need data that is accurate, comprehensive, clean, and trustworthy. Test data management methods require the usage of real production data, although masking data provides a secure and useful substitute. Relational integrity is maintained with disguised data without ever compromising real client data

Types of Data Masking

Three methods of data masking are frequently used.

1. Static Data Masking: Static data masking applies to a production database copy. This process begins with statistical data masking, which requires database administrators to make a copy of the original data, store it securely, and then replace it with a fictitious set of data. The procedure entails replicating database content onto a test environment, which the firm will then share with outside contractors. Consequently, the original sensitive data must be protected, remains in the production database, and is used in the test environment in a masked copy.
2. Dynamic Data Masking: Masking data dynamically is the technique of masking data when a query is sent to a database containing actual private information. Changing the question or the answer is how it is accomplished. Direct application of this technique is made to production datasets. Only authorized users will be able to view the real data, or any non-privileged user will only see masked data.
3. On-the-fly Data Masking: When data is moved from production settings to test or development environments, it’s known as “on-the-fly data masking.” For companies that: Constantly deploy software, on-the-fly data masking is ideal.
   Due to the difficulty of consistently maintaining a backup copy of masked data, this technique will only send a subset of masked data when required. To ensure that sensitive information is masked before it reaches the target environment, on-the-fly data masking alters sensitive information as it is moved between environments. Organizations transferring data between systems or ensuring ongoing integration or synchronization of diverse data types will find this strategy suitable.

Data Masking Techniques

There are numerous data masking strategies. To safeguard sensitive information in your database, let’s talk about some popular masking techniques.

1. Scrambling: A simple masking method called scrambling obscures the original text by randomly arranging the characters and digits. While it’s an easy technique to use, it only works with specific kinds of data and doesn’t make sensitive data as secure as you may think. For many critical use scenarios, it is not the most appropriate data masking method and is not the most secure.
2. Substitution: The act of disguising data by replacing it with a different value is known as substitution. It is thought to be the best method for masking data while maintaining the original appearance and feel of the data. This method works well to replace production data with realistic data and may be applied to a variety of data sources.
3. Shuffling: This technique keeps the original data but changes the order. The data in the same column would be moved inside the rows as a result. Although there is no guarantee of data protection, this can be helpful in some situations. Bad actors can simply reverse engineer the shuffled data if they know the technique, which is a disadvantage of this approach.
4. Encryption: One of the most popular and efficient methods for data masking is encryption. With the use of a secret decryption key, users can observe the unreadable format that encryption algorithms have created from the raw data. Without the decryption key, the data cannot be read by anybody. Data in motion that has to be able to return to its original form can benefit from encryption. Only when authorized individuals have access to the decryption key is encrypted data considered secure. An unauthorized user may be able to decode the private information and see it in its unaltered state if a key is compromised. Thus, secure key management is crucial.
5. Nullification: By assigning null values to data columns, the process of nullification stops unauthorized users from accessing the actual data. Despite its seeming simplicity, this technique has some disadvantages, including reduced data integrity and challenges when testing and developing with such data.

Challenges of Data Masking

Everyone should consider a number of things when putting data masking into practice. Choosing the best solution for a given data demand might be helpful by overcoming some challenges.

1. Format Preservation: The structure of many different data kinds, including email addresses, phone numbers, and identities, must be correctly recognized and preserved by the data masking solution. A divergence from the original format may result in problems in the subsequent procedures, hence it is important to make sure that the original data format is being maintained. For data threads like dates that are specific in a particular order, this is particularly crucial.
2. Gender Preservation: When changing a person’s name in the database, the masking system should be able to identify whether the user is male or female and should be gender aware. It is crucial to preserve the appropriate gender association while disguising names. Inaccurate analysis and reporting may result from a skewed gender distribution in the dataset caused by arbitrarily changing names without taking gender into account.
3. Data Uniqueness: Every data piece should have a unique value applied by the masking mechanism when masking unique data. It is best to preserve the masked data’s frequency distribution, particularly if it has significance (i.e., geographic distribution). The masked data values in each table column should, on average, be comparable to the original.
4. Maintaining Integrity: Making sure that sensitive data is consistently hidden across several databases is a major data masking challenge. Primary keys are used to link the tables in a relational database. These values must be changed uniformly throughout the database when the masking solution obscures or substitutes the values of a table’s primary key.In each database where a particular social security number occurs, for example, it should be masked in the same way. Enterprise systems may stop working properly if referential integrity is compromised, especially in lower environments where testing takes place.

Data Masking Best Practices

1. Identify Sensitive Data

You must understand the data you are holding and be able to differentiate between different sorts of information with different levels of sensitivity before you can safeguard it. Usually, business and security professionals work together to create a comprehensive record of every data component used by an organization. The effectiveness of masking depends on knowing what data is present in your analysis and storage contexts. Facilitating the detection and classification of sensitive material as it is added to your data stack is the simplest method to keep an accurate and consistent understanding of your data. As a result, data teams have visibility and control over the kinds of data they own, as well as over the storage and analysis locations. In this way, teams may better understand their data in relation to the people who must access sensitive data and the restrictions they are subject to.

2. Governance with its Cost

The GDPR, CCPA, and HIPAA compliance framework and laws are among the best practices for data masking. Understanding the relevant governance rules is crucial since they regulate the management of particular types of data and impose limitations on its processing and distribution. This is significant not just because frameworks frequently recommend or specify masking strategies for controlled categories, but also because masking specific components may reduce the operational classification of data processing activities, which would lessen the burden of compliance or permit wider sharing. These situations may result in the reduction or elimination of expensive procedures like review and audit, which would increase the total availability of the data while cutting operating costs and time to value.

3. Repeatability and Scalability

Any data masking system’s foundation should be constructed to support scale and repetition. It should be possible to use masking techniques on any fresh data forever without having to make any adjustments. The methods employed to preserve the data must be able to keep up with its evolution and growth. This implies that you should only select and use masking strategies if they will work for your data needs in the future. Only long-term solutions should be employed, and data masking should be considered a long-term strategy to safeguard your data against breaches.

4. Referential Integrity

Referential integrity is the final crucial practice. It is necessary to mask data that is referenced on other tablets in the same manner as the original data. This implies that the same technique should be used to conceal primary and secondary data. Referential integrity may be something you wish to preserve even while data is hidden. In some circumstances, it could be necessary to disrupt referential integrity in order to avoid “toxic” data pairings that could result in privacy violations. Salting and encryption keys can be used to maintain or destroy referential integrity through masking techniques like hashing and reversible masking. If done dynamically using DDM, this can have a lot of power.

How Lepide Helps with Data Security

The Lepide Data Security Platform can help you improve your data security strategy by aggregating and summarizing event data from multiple sources which can include both on-premise and cloud platforms. All important events are displayed on a single, centralized dashboard, with various options for sorting and searching. Below are some of the most notable features of the Lepide Data Security Platform:

Data classification: The Lepide data classification tool will scan your repositories, both on-premise and in the cloud, and classify sensitive data as it is found. You can also customize the search according to the compliance requirements relevant to your business.

Machine learning: Lepide uses machine learning algorithms to establish usage patterns that can be tested against to identify anomalous behavior.

Change auditing and reporting: Lepide’s change auditing and reporting tool enables you to keep track of how your privileged accounts are being accessed and used. Likewise, any time your sensitive data is accessed, shared, moved, modified, or deleted in an atypical manner, a real-time alert can be sent to your inbox or mobile device. Alternatively, you can simply review a summary of changes via the dashboard.

Threshold alerting: Lepide’s threshold alerting feature enables you to detect and respond to events that match a pre-defined threshold condition.

Inactive user account management: Lepide can help you locate inactive user accounts, thus preventing attackers from exploiting them.

If you’d like to see how the Lepide Data Security Platform can help give you more visibility over your sensitive data and protect you from security threats, schedule a demo with one of our engineers.