Data Masking: A Guide to Protecting Sensitive Data
As organizations collect more sensitive information, protecting that data becomes a top priority. Data masking helps teams safely use real data for development, testing and analytics — without exposing private or regulated information.
- Overview
- What Is Data Masking?
- When To Use Data Masking
- Types of Data Masking
- Common Data Masking Techniques
- Resources
Overview
Sensitive or confidential data — such as personally identifiable information, financial data and intellectual property — must be protected from unauthorized access or misuse. Yet in the course of business, this data needs to be shared with various systems, partners and users. Data masking is a collection of techniques designed to obscure sensitive information to protect it while enabling it to be used appropriately. Data that has been masked with these techniques can’t be traced back to its original values without access to the primary data set.
What Is Data Masking?
Data masking is a term that describes a variety of techniques for protecting sensitive or confidential data by obfuscating or hiding the original data values. It’s typically used in combination with other data security measures, such as access controls, data encryption and auditing, to provide a comprehensive approach to protecting sensitive data throughout its lifecycle.
When to Use Data Masking
Various types of data need to be protected from unauthorized use, from patient health data to intellectual property. When identifying data sets that should be protected, consider the following.
Regulatory compliance
Data masking is used to protect data covered by data privacy regulations, including the GDPR and the California Consumer Privacy Act (CCPA). Data masking is an excellent tool for compliance because it provides minute control over who has access to data, which data they can access (even down to the column level) and how data is tracked.
Development and testing
During development and testing, data is particularly vulnerable because engineers, developers, testers and others have access to sensitive data sets. Data masking allows teams to work with realistic test data that closely represents the original without exposing sensitive information.
Training and demonstrations
Data masking is often used for software training or demonstrations. Organizations can enhance these experiences by using realistic data without exposing actual customer or proprietary information.
Consumer privacy and trust
It’s a good idea to protect customer data that isn’t covered by regulatory requirements, simply because customers are concerned about data privacy. When a customer does business with a company, they put their trust in the organization to protect their private information. If this trust is betrayed, it can severely damage or end the relationship. By using data masking —and communicating that they are doing so — organizations help maintain customers’ trust.
Types of Data Masking
There are two basic types of data masking: static and dynamic. The choice of data masking technique depends on various factors, such as the data's sensitivity level, regulatory compliance requirements and the intended use case. Static and dynamic data masking techniques are also often used together in a complementary manner to provide comprehensive data protection across different environments and use cases.
Static data masking
Static data masking describes the masking of data in storage, and involves permanently replacing sensitive data with fictitious or masked values. The resulting data sets do not contain any real data. Static data masking is typically used for nonproduction environments, such as development, testing or training environments. Commonly used techniques include substitution, shuffling and masking out.
Dynamic data masking
Dynamic data masking is more suitable for production environments, where authorized users or applications may need access to the original, unmasked data for legitimate business purposes. The dynamic approach masks sensitive data in real time as it is being accessed or retrieved, allowing authorized users to view the original data while unauthorized users see only the masked version. Commonly used techniques include masking out and encryption.
On-the-fly data masking
On-the-fly data masking is a specific implementation approach to dynamic data masking. It refers to the technique where the masking process occurs in real time as the data is being accessed or queried, typically through a middleware layer or proxy between the database and the client application. The masking rules are applied dynamically as the data is being accessed, and the masked data is returned to the client application. The key distinction is that on-the-fly data masking does not require changes to the application or database.
Common Data Masking Techniques
Many different data masking techniques can be deployed, and organizations often choose to use a variety of techniques based on data sensitivity, regulatory requirements, intended use case, and level of protection needed. Here are several common data masking techniques:
- Encryption: Encryption involves converting sensitive data into a coded format that can only be read with the relevant decryption key.
- Tokenization: Tokenization replaces sensitive data with a substitute (a token) that has no intrinsic meaning but can be mapped back to the original data when required.
- Redaction or masking out: Redaction involves removing or obscuring sensitive data by replacing it with a mask character or blank spaces. This technique is often used for partial masking, where only a portion of the sensitive data is masked, leaving the rest visible for context or identification purposes.
- k-anonymization: k-anonymization is a technique that makes each record in a data set indistinguishable from at least k-1 other records. So, if someone looks at the data, they can't single out an individual based on those attributes because there are at least k-1 other people who look the same. This helps protect people's privacy by making it harder to identify them in the data set.
- Differential privacy: Differential privacy adds controlled noise or randomness to a data set to protect individual privacy while still allowing for meaningful statistical analysis. It ensures (mathematically) that the presence or absence of any individual's data in the data set will have a negligible effect on the results of queries or analyses performed on the data.
- Pseudonymization: Pseudonymization involves replacing identifiable data (such as names or identifiers) with pseudonyms or artificial identifiers. This technique separates the sensitive data from the pseudonym, making it harder to identify individuals while still allowing data processing and analysis.
- Averaging: Averaging involves replacing individual sensitive data values with the average or mean value of a group or subset of records. This technique can protect privacy by obscuring individual values while preserving the data's overall statistical properties.
