The chameleon changes its color to defend itself. Similarly, walking sticks mimic the appearance of twigs to deceive predators. Data masking follows the same principle! Let’s explore a methodical approach that ensures the security and usability of your data.
According to IBM’s 2022 report on the cost of data breaches, the average expense incurred by a data breach amounts to $4.35 million. The report further highlights that 83% of surveyed companies experienced multiple data breaches, with only 17% stating it was their initial incident. As sensitive data holds immense value, it becomes a desirable target and requires effective protection. Among all compromised data types, personally identifiable information (PII) is the most expensive. To safeguard this information and maintain its confidentiality, data masking has emerged as an indispensable technique.
What is data masking?
The purpose of data masking is to ensure the confidentiality of sensitive information. In practice, data masking entails substituting genuine data with fictional or modified data, while retaining its visual representation and structure. This approach finds extensive application in test and development settings, as well as in situations where data is shared with external entities, in order to avert unauthorized exposure. By employing data masking, data security is assured while preserving its usefulness and integrity, thereby mitigating the likelihood of breaches compromising confidentiality.
What are the different types of data masking?
To guarantee the effective masking of your data, data masking can employ various techniques, each with its unique advantages, allowing you to select the most suitable approach for maximizing data protection.
Static Data Masking
Static Data Masking is a data masking technique that involves modifying sensitive data within a static version of a database. The process begins with an analysis phase, where data is extracted from the production environment to create the static copy. During the masking phase, real values are substituted with fictitious ones, information is partially deleted, or data is anonymized. These modifications are permanent, and the data cannot be restored to its original state.
Format Preserving Masking
Format Preserving Masking (FPM) differs from traditional masking methods as it preserves the length, character types, and structure of the original data. By utilizing cryptographic algorithms, sensitive data is transformed into an irreversible and unidentifiable form. The masked data retains its original characteristics, allowing it to be used in systems and processes that require a specific format.
Dynamic Data Masking
Dynamic Data Masking (DDM) applies varying masking techniques each time a new user attempts to access the data. When a collaborator accesses a database, DDM enforces defined masking rules to limit the visibility of sensitive data, ensuring that only authorized users can view the actual data. Masking can be implemented by dynamically modifying query results, substituting sensitive data with fictional values, or restricting access to specific columns.
On-the-fly Data Masking
On-the-Fly data masking, also known as real-time masking, differs from static masking by applying the masking process at the time of data access. This approach ensures enhanced confidentiality without the need to create additional data copies. However, real-time masking may result in processing overload, especially when dealing with large data volumes or complex operations, potentially causing delays or slowdowns in data access.
What are the different data masking techniques?
Random substitution
Random substitution involves replacing sensitive data, such as names, addresses, or social security numbers, with randomly generated data. Real names can be replaced with fictitious names, addresses can be replaced with generic addresses, and telephone numbers can be substituted with random numbers.
Shuffling
Shuffling is a technique where the order of sensitive data is randomly rearranged without significant modification. This means that sensitive values within a column or set of columns are shuffled randomly. Shuffling preserves the relationships between the original data while making it virtually impossible to associate specific values with a particular entity.
Encryption
Encryption involves making sensitive data unreadable using an encryption algorithm. The data is encrypted using a specific key, rendering it unintelligible without the corresponding decryption key.
Anonymization
Anonymization is the process of removing or modifying information that could lead to the direct or indirect identification of individuals. This may involve removing names, first names, addresses, or any other identifying information.
Averaging
The averaging technique replaces a sensitive value with an aggregated average value or an approximation thereof. For example, instead of masking an individual’s salary, averaging can use the average salary of all employees in the same job category. This provides an approximation of the true value without revealing specific information about an individual.
Date Switching
Date switching involves modifying date values by retaining the year, month, and day but mixing them up or replacing them with unrelated dates. This ensures that time-sensitive information cannot be used to identify or trace specific events or individuals while maintaining a consistent date structure.
Conclusion
The significant benefit of data masking for businesses is its ability to preserve the informational richness, integrity, and representativeness of data while minimizing the risk of compromising sensitive information. With data masking, companies can successfully address compliance challenges without sacrificing their data strategy.
Data masking empowers organizations to establish secure development and testing environments without compromising the confidentiality of sensitive data. By implementing data masking, developers and testers can work with realistic datasets while avoiding the exposure of confidential information. This enhances the efficiency of development and testing processes while mitigating the risks associated with the utilization of actual sensitive data.
