All You Must Know About Data Masking

50% of businesses have experienced some form of cyber security breach or attack in the last 12 months, Gov. UK‘s 2024 report suggests. One reason could be the less stringent controls that firms enforce on data used for non-production purposes compared to data used for production.

Such inconsistent implementation of data security measures can pose security and compliance risks, mainly when third parties access the data beyond the firm’s oversight. Data masking can alleviate such concerns. It ensures that data is masked when transferred outside production environments to prevent compromise.

What is Data Masking?

Data masking is a data obfuscation method that replaces the original data with structurally similar data. This means the data format remains the same; only the values are altered.

Techniques such as encryption, shuffling, or substitution are used to make these alterations. Data masking aims to create a functional substitute that does not reveal the real data.

Hence, once the data is masked, it is impossible to reverse engineer or seek original data without access to the original dataset.

Two main differences distinguish data masking from other data obfuscation methods.

• The data is usable, even when it is obfuscated.
• The original values cannot be recovered once the data is masked.

Consider a database with less sensitive customer data and sensitive financial data. The sales personnel must be given access only to customer data, not financial data. Financial data can be masked with data masking, so the attacker cannot access it when the sales personnel’s account is hacked.

Which Data Can Be Masked?

• Personally identifiable information (PII)

PII consists of individuals’ full names, passports, and driver’s license numbers—basically, data that is used to identify them.

• Protected health information (PHI)

PHI is the data collected by healthcare service providers to provide appropriate care. This includes insurance, demographic data, test and laboratory results, medical histories, and health conditions.

• Payment card information (PCI)

PCI means a debit, credit, or prepaid card “primary account number.” It is sixteen numbers on the card, the CVV or CVV2 (card security codes),

• Intellectual property (IP)

It includes data related to inventions, business plans, designs, and specifications. These are valuable for firms and must be secured from unauthorized access and theft.

Data Masking Vs. Data Encryption

Both encryption and data masking help firms remain compliant and minimize the risk of exposing sensitive data.

The key differences between masking and encryption are-

• Masked data remains usable. This means the original values can’t be recovered.
• Encrypted data is challenging to work with. But it can be recovered with the correct encryption key.

While encryption is ideal for storing or transferring sensitive data, data masking enables firms to use data sets without exposing the real data. Regardless of the chosen method, the encryption keys and algorithms used to mask data must be secured to prevent unauthorized access.

Many standards and regulations, including GDPR, HIPAA, PCI DSS, and CCPA, require firms to keep PII secure and private. While laws and standards surrounding data processing and protection are vital, they create a challenge for firms that want to extract value from and even share the data with others.

Also read: 5 Key Elements of Robust Data Protection Strategy

What are the Types of Data Masking?

• Static Data Masking

Static data masking is applying a fixed set of masking rules to sensitive data before it is stored or shared. It is commonly used for data that stays mostly the same and remains static over time.

The rules are predefined and consistently applied to the data to ensure consistent masking across multiple environments.

• Dynamic Data Masking

Dynamic data masking dynamically alters existing sensitive data as users access or query it. While it is applied in real time, it is mainly used for implementing role-based data security in customer support or medical record handling.

• Deterministic Data Masking

Deterministic data masking is used to ensure that the same input value is masked to the same output value consistently. This often involves data substitution or tokenization to maintain a consistent mapping between masked values and original data.

• On-the-fly Data Masking

This type of data masking masks sensitive data in memory. So, there is no need to store the altered data in the database. This technique is useful in continuous deployment pipelines and complex integration scenarios, where data frequently moves in production and non-production settings.

• Statistical Data Obfuscation

This type of data masking involves altering sensitive data values. It ensures that the masked data maintains the original data’s overall distribution, patterns, and correlations for accurate statistical analysis. Methods include applying mathematical functions or perturbation algorithms to the data.

What are the Techniques of Data Masking?

• Hashing

Hashing converts data into a fixed-length string of characters. It is used mainly for masking passwords or other sensitive data where the original value isn’t needed.

• Tokenization

Firms can replace production data with a randomly generated token or reference value with tokenization. The original data is stored in a separate secure location, and the token is used as a substitute during processing or analysis. Tokenization maintains data integrity, reducing the risk of data exposure.

• Nulling

Nulling involves replacing sensitive data with blank spaces or null values or blank spaces. This removes the data from the dataset and is suitable when firms want to retain the data’s format, but the specific data needs to be concealed.

• Randomization

With randomization, firms can replace sensitive data with randomly generated values. These values do not correlate with the original data. For instance, the names, addresses, or other PII data can be replaced with fictional or randomly selected values.

• Shuffling

Shuffling is a technique for reordering the values within a dataset. This technique preserves the relationships within data. For instance, column data can be randomly shuffled so row values change. The association between a customer and their transactions can be preserved just by switching names and contact details.

• Substitution

Substitution is the technique for replacing sensitive data with similar but fictitious data. For instance, actual names can be replaced with names given in a predefined list. Algorithms can be used to generate similar but fake credit card numbers.

• Encryption

With encryption masking, firms can encrypt sensitive data using cryptographic algorithms. This transforms the data into an unreadable format. Only authorized users that have the decryption keys can access the original data.

What are the Use Cases of Data Masking?

• Data Privacy

Data masking can help enhance data privacy by ensuring that sensitive data is not visible to unauthorized users.

• Testing and Development

Firms can use data masking to create safe and secure environments for testing and development. By masking sensitive data in test databases, they can ensure that developers and testers work with realistic data without risking sensitive data.

• Compliance

Data masking can help comply with regulations and standards such as PCI-DSS, HIPAA, and GDPR. By using suitable data masking techniques, firms can ensure that they protect customer information as per these regulations.

• Outsourcing

When outsourcing work to third-party vendors, firms can use data masking to ensure that sensitive data is not accessible to the vendor. By masking sensitive data, they can maintain control over the data while allowing the vendor to perform the necessary work.

• Analytics and Reporting

Firms can use data masking to protect sensitive data when conducting analytics and creating reports.

Conclusion

Data masking allows firms to secure sensitive data against unauthorized access and breaches. It replaces the original data with structurally similar but inauthentic data. The application of data masking is diverse.

It can be applied to PII, PHI, and IP—all crucial to remain compliant with GDPR, HIPAA, PCI DSS, and CCPA. Understanding the different types of data masking helps firms choose the right one based on their specific use cases, regulatory requirements, and data security needs. 

Whether ensuring the teams have access only to non-sensitive data or enabling the secure sharing and analysis of data sets in a compliance-focused world, data masking offers a robust solution for protecting sensitive data while maintaining its usability.