Data Masking: What It Is, Types, Techniques and Best Practices

Data Masking

What is Data Masking?

Data masking allows you to create a fake but real version of your organization’s data. It is used to protect sensitive data and provide a functional alternative when it is not necessary. This could be used, for example, in user training or sales demos, or software testing.

Data masking is a process that alters the data’s values while maintaining the same format. It is intended to make the data unreadable or impossible to reverse engineer. You can alter the data in several ways, such as character shuffling and word or character substitution, and encryption.

Why is Data Masking Important?

There are many reasons why data masking is important for many organizations:

  • Data masking can solve several critical threats, including data loss, data exfiltration or account compromise, and insecure interfaces to third-party systems.
  • Cloud adoption reduces data risk
  • Data is rendered useless by an attacker while maintaining many of its functional properties.
  • It allows data sharing with authorized users, such as developers and testers, but not production data.
  • This can be used for data sterilization. While normal file deletion leaves behind traces of data on storage media, sanitization replaces those values with masked versions.

Types of data masking

There are many data masking options that can be used to protect sensitive data.

Static data masking

You can use static data masking to create a clean copy of your database. This process alters sensitive data so that a safe copy of the database is available for sharing. The process typically involves creating a backup copy of the database in production, loading it into a separate environment, removing any unnecessary data, then masking the data while it’s in stasis. The target location can then push the masked copy.

Imperva has partnered with Mage™, Static Data Masking in order to provide SDM capabilities for Imperva customers. Imperva Data Security Fabric provides live protection for production data. Mage™, however, de-identifies data from non-production environments. Mage™, which complements Imperva DSF with a static data masking capability, supports multiple data platforms and allows for flexible deployment mechanisms that can seamlessly integrate into existing enterprise IT infrastructure without any architectural changes.

Also read: What is Data Lineage, Best Practices and Techniques

Deterministic data masking

This involves mapping two sets of data that have the same type of data, in such a way that one value is always replaced by another value. In a database, for example, “John Smith” will always be replaced by “Jim Jameson”. Although this is convenient in many situations, it is less secure.

On-the-Fly data masking

Masking data when it is transferred from production to test or development systems. Data is not saved to disk until masking is applied. Software deployments often result in software being unable to create a backup copy and mask the data.

They need to stream data continuously from production to multiple test environments. Masking can be done on the fly by sending smaller subsets of masked data whenever it is needed. Each subset is stored in the dev/test environment to be used by the nonproduction system.

Masking feeds from production systems to development environments should be applied on-the-fly. To prevent security and compliance issues at the beginning of any development project.

Dynamic data masking

Similar to on-the-fly masking, data is not stored in secondary data stores in the dev/test environments. Instead, the data is streamed directly to the production system and then consumed by another system within the dev/test environments.

Data Masking Techniques

Let’s look at some common ways that organizations mask sensitive data. IT professionals have many options for protecting sensitive data.

Data encryption

Without the viewer having the decryption keys, encrypted data becomes inaccessible. The encryption algorithm masks data. This method is the most secure form of data masking, but it is also difficult to implement. It requires technology to continue data encryption and mechanisms to share and manage encryption keys.

Data scrambling

The original content is replaced by characters that are reorganized in random orders. An ID number, such as 76498, in a production database, could be replaced with 84967 in the test database. Although this method is simple, it is not secure and can only be used for certain types of data.

Nulling out

Unauthorized users can view data and see that it is missing or null. This renders the data less useful for testing and development.

Also read: 7 Best Data Mining Techniques

Value Variance

A function replaces the original data values, such as the difference in the lowest and highest value of a series. If a customer has purchased multiple products, the purchase price may be replaced by a range indicating the difference between the highest and the lowest price. This can be useful data that is not disclosed to the original dataset.

Data Substitution

Data values can be replaced with fake but real alternative values. Real customer names, for example, are substituted with random numbers from a telephone book.

Data Shuffling

The only difference is that data values can be switched within the same dataset. Each column is rearranged using a random sequence. For example, you can switch between real customer names across multiple customer records. Although the output set appears to be real data, it does not contain all of the information needed for each individual or record.

Pseudonymisation

The EU General Data Protection Regulation has created a new term to describe processes such as data masking, encryption, and hashing to protect personal information: pseudonymization.

According to the GDPR, pseudonymization is any method that prevents personal identification from being made using data. It involves removing all direct identifiers and, preferably avoiding multiple identifiers that, when combined, could identify a person.

Additionally, encryption keys and other data that can be used for reverting to the original data values should be kept separate and secured.

Best practices of data masking

Determine the Project Scope

Data masking is a process that allows companies to know what data needs to be protected, who has access to it, which applications are using it, and where it is located, in both production and non-production domains. Despite looking easy on paper, this is difficult due to the complexity and many lines of business.

This process can be time-consuming and should be considered an independent stage.

Ensure Referential Integrity

Referential integrity is the principle that all information from a business application must have the same algorithm to mask it.

A single data masking tool that can be used throughout large companies is not feasible. Due to different budget/business requirements, IT administration practices, and security/regulatory needs, each business line may need to implement its own data masking.

Make sure that data masking tools and practices are synchronized across the organization. When dealing with the same type of data. This will help avoid problems later when data is needed across business lines. Protect the Data Masking Algorithms

It is crucial to think about how to protect data-making algorithms as well as other data sets and dictionaries that are used to scramble data. These algorithms are extremely sensitive as only authorized users should have the right to access the actual data. It is possible to reverse engineer large amounts of sensitive information if someone knows which repeatable masking algorithms have been used.

A data masking best practice is required by certain regulations to ensure the separation of duties.IT security personnel may decide which methods and algorithms are to be used, but only the data owners and data lists for specific algorithms should be available.

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