Data protection is becoming more and more important due to the ever-increasing amount of information stored in digital form. Have you ever wondered what the best practices for data masking are? Do you know what data masking techniques you can use to keep your data safe? Have a look at our article!
Data masking: what is it?
Data masking is the process of protecting information by creating an alternative but plausible version of the data. It replaces sensitive data with fictitious ones. It is an important practice to ensure data confidentiality during training, presentations, or software testing. Data masking techniques are used to change the value of data while preserving its structure. Data masking helps organizations ensure data security in situations where access to the full information is not necessary. Moreover, the process minimizes the risk of compliance violations and unauthorized access. Data requiring masking includes:
- Medical records (laboratory test results, health insurance information, and demographic data)
- Financial information (payment card numbers, credit and debit card transactions)
- Personal data (passport and social security numbers)
- Intellectual property (inventions and designs)
The data masking industry is projected to reach a value of $435 million by 2025, with annual growth of up to 15% from 2020 to 2025. This rapid growth is mainly driven by the growing demand for data protection due to the introduction of laws and regulations around the world. Also, the development of AI, especially ML models that require significant amounts of data, is further contributing to the growing interest in data masking.
Data masking techniques
Let’s briefly discuss some of the data masking techniques:
DATA SHUFFLING
Shuffling is a popular data masking technique. It involves randomly mixing values within the same set of data. In shuffling, the values shuffle inside the columns, preserving the overall structure of the data. For example, in the case of a table containing customer data, shuffling may involve randomly assigning different customer names to different records. As a result, it is difficult to identify real people.
SUBSTITUTION
Another data masking technique is data substitution. Here, the original data is replaced with fake but realistic values, preserving the overall nature of the data. For example, you can replace real customer names with randomly selected names from the phone book.
DATA ENCRYPTION
Data encryption is an advanced method of masking. It involves transforming data into an unreadable form using a decryption key. Consequently, individuals lacking the correct key find the data unusable. This is the most effective form of data protection, but it requires advanced technology and the management of encryption keys. For example, the system can read encrypted credit card numbers with a decryption key. However, they are completely unreadable for unauthorized persons.
NUMBER VARIANCE
Number variance is a data masking technique often used in financial information. It involves replacing the original data values with a variance, such as +/- 10%. This enables the creation of a new set of data while preserving the overall accuracy of the original data. In the case of customer transactions, you can replace the purchase price with a price range between the highest and lowest price paid. It enables using data for various purposes while maintaining confidentiality.
DATA SCRAMBLING
Data scrambling is a masking technique that involves reordering characters or numbers in a data field in a random order. It is an irreversible process, which means that the original data cannot be recovered from encrypted data. For example, you can transform an employee ID number, such as 97489376, into a 37798649 number. This method offers some data security, but it has limitations for certain types of data and may be less effective for more complex sets.
How to implement data masking – 5 steps
Implementing data masking requires a thoughtful approach based on five key steps:
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Defining the scope of the project
The first step involves determining which aspects of the data to mask, including their types, locations, and levels of access for different users.
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Defining the data masking technique stack
Select the appropriate techniques and tools for data masking, taking into account the specifics of the data types and algorithms required.
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Security of selected data masking algorithms
It is necessary to ensure the security of the masking algorithms used. The idea is to prevent unauthorized access to the original data.
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Maintaining referential integrity
Maintaining the integrity of data masking across data sets and their associated relationships is essential.
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Repeatability and automation of the process
The ultimate goal is to ensure automation and make the data masking process easily repeatable. Also, be sure to account for possible changes in the data and business environment.
Furthermore, exploring the integration of data masking techniques with big data consulting services can enhance the effectiveness of your data protection strategy. This collaborative approach enables tailored solutions that address the unique challenges of your data environment, ensuring optimal security measures while streamlining processes for sustained compliance and risk mitigation.
Conclusion
Data masking is an extremely crucial process in today’s digital world. Through the use of various data masking techniques, it is possible to effectively protect data from unauthorized access. Implementing appropriate practices enables maintaining the confidentiality of information. It also has an impact on maintaining compliance with data protection regulations.
