What is Data Masking?

It doesn’t matter if it’s your individual data or sensitive business data: Personal information is valuable and important. With the increasing number of cyber-attacks and data breaches in the U.S., the matter of personal information is urgent. 

Businesses and organizations must take necessary measures to protect their data from being compromised.

This is where data masking comes in.

What is data masking?

Data masking is the process of concealing sensitive data by replacing it with fictitious — but realistic — values. This allows people to use and share data without compromising its confidentiality.

Data masking involves altering data such that the data remains usable for testing or development but is secure from unauthorized access. This technique helps to:

• Ensures privacy. 

• Secure data during software testing and user training exercises.

How data masking works

Data masking is a meticulous process that involves several stages of transformation to protect sensitive data. Briefly, here are those steps:

1. Identify sensitive data: Locate sensitive elements within the data set that require masking.

2. Choose your masking technique(s): Choose appropriate algorithms or methods specific to the data type and context.

3. Apply the masking: Execute the selected techniques to substitute or obscure the identified sensitive data.

4. Validate and test: Ensure the masked data maintains integrity and the original data is not inferable.

5. Deploy: Utilize the masked data in the required operational environments, such as test and development platforms. The masked data retains its authenticity and viability for practical uses.

Despite its seemingly simple procedure, data masking is a critical element in data security plans, requiring precision and strategic implementation.

Types of data masking

Data masking isn't a one-size-fits-all approach, as different data types and scenarios may require various masking techniques. Here are some common types of data masking.

• Static data masking (SDM) is the most common type, where sensitive data remains unchanged over time or repeated use. It is useful for creating test environments.

• Dynamic data masking involves real-time masking of sensitive information during application usage or query execution. The data's value changes dynamically in real-time, even while in transit. This allows authorized users to view and access unmasked data, while unauthorized users can only see the masked version.

• On-the-fly data masking happens in transit, protecting data as it moves from one system to another. It's critical for high-velocity data environments.

• Format-preserving encryption, a hybrid approach, keeps the original data format after encryption, vital for operational continuity and compliance with data regulations.

Data masking techniques

Data masking takes on varied forms, depending on the type of information being masked. Here are some techniques used across different data types.

Substitution

Substitution is replacing sensitive data with fake but realistic values, for example, replacing names with fictitious ones. It ensures that the data remains non-inferable. This technique is used in both static and dynamic masking.

Shuffling

This technique involves swapping sensitive data elements within a dataset, making it difficult to infer relationships between the original and masked data.

For example, shuffling addresses within a customer database makes it challenging to identify the connection between customers and their locations.

Algorithmic masking

Algorithmic masking, also known as deterministic or rule-based masking, applies a consistent algorithm across sensitive fields, ensuring that the same input results in the same masked output. This predictability aids in testing and validation without compromising data utility.

Here's a simple algorithmic masking example:

Original Data: 
123-45-6789

Masked Output (using SHA-256 algorithm): 
3f5b8a88717570742c332cff0e68be4774e0732dcd6ec0065837dee26cdb1bd3

Tokenization

Tokenization replaces sensitive data with a non-sensitive surrogate token, often used for credit card numbers or social security numbers. The token can be reversed to its original value when needed, but the masked value is useless to unauthorized users.

Here's an example:

Sensitive data: 
1234 5678 9101 1121 

Tokenized data: 
XXXX XXXX XXXX X121

Data obfuscation

Finally, data obfuscation techniques, like nulling out or data scrambling, distort the actual data to the extent that the original values cannot be discerned or reconstructed. These techniques are often used when the usability of the masked data is not a primary concern.

For example, you can easily suppress sensitive fields such as "Social Security Number" in a dataset meant for developer training.

Benefits of data masking

The use of data masking techniques yields several advantages for organizations and individuals alike. Here are some known benefits.

Enhancing data security

Data masking serves as an essential countermeasure against unauthorized data exposure, creating a formidable barrier to safeguarding confidential datasets. It helps to prevent common data loss issues such as data exfiltration.

When implemented correctly, this technique prevents users from revealing sensitive information, especially when sharing data with third-party vendors.

Proactively applying data masking is not just about defense — it’s a strategic approach to data management. In a digitized world where data breaches are both costly and reputationally damaging, data masking is a must-have.

Compliance with regulations

When it comes to complying with data protection statutes, data masking is instrumental.

Stringent regulatory frameworks, such as the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA) in the United States, require rigorous standards for data privacy and security.

Organizations turn to data masking solutions to ensure that they handle sensitive information in a way that complies with these legal requirements. Notably, data masking helps maintain compliance even during intricate processes like data migration and system testing, where real data sets can be vulnerable.

Facilitates data sharing

Data masking is vital for safe information exchange in today's interconnected world too.

• Compliance alignment: Data masking aligns with privacy regulations, enabling secure data sharing.

• Risk reduction: It limits the exposure of sensitive data during collaboration.

• Operational continuity: Ensures business processes continue unhindered while using masked data. Masked data allows for collaboration and analysis without compromising confidentiality.

It's a key enabler for data-driven partnerships, ensuring secure and compliant data handling.

Data masking in action: examples

Let’s look at a few real-world ways to mask your data. 

Dynamic masking secures live transactional systems

A financial institution upgrading its core banking system can use real-time data masking to ensure customer account numbers and financial details remain obscured during the software development life cycle. Developers work with functional yet anonymized datasets, preventing any risk of sensitive information leaks.

This allows the bank's operational integrity to be maintained while it upgrades its technological capabilities.

Documents confidentiality

When a law firm shares documents with external consultants, it employs data masking to redact personal identification numbers and other sensitive information.This allows external parties to review relevant case material without accessing private client data — preserving confidentiality and adhering to legal privacy standards.

Tokenization for sensitive elements

For example, in the healthcare sector, when transferring patient records for analysis, identifiers such as Social Security numbers can be replaced with unique tokens. This safeguard protects patient privacy while enabling the necessary medical research to advance.

De-identification techniques for user analytics data

In online marketing, companies mask user data before analytics to comply with privacy regulations, like GDPR. Personal identifiers are stripped away to prevent misuse of data, yet valuable insights can still be derived from the anonymized patterns and trends retained.

Data masking plays a critical role in ensuring compliance with data protection laws. Data masking substitutes sensitive details with fictional yet realistic values, safeguarding personal identifiers, and maintaining compliance without interrupting operational dynamics.

Final thoughts

Wrapping up, data masking is an essential technique for protecting sensitive information, ensuring regulatory compliance, and facilitating secure data sharing. It offers a robust defense against potential breaches in today's rapidly digitizing world.

As more organizations look to data-driven strategies, the use of data masking will only increase in importance.