GDPR is easily one of the most stringent and important regulations that businesses dealing with customer data have to comply with. One key principle of this regulation is data minimization which means that one must use least amount of data required to complete a task.
Another important GDPR principle is that when processing sensitive data, it should be ensured that personal identifiers of individuals such as their names, addresses, phone numbers, etc. are unrecognizable without additional information. Data masking is a technique that helps us to adhere these principles.
What’S Data Masking?
Data masking is the technique of generating an inauthentic but structurally comparable version of a database to prevent the exposure of sensitive information. In this technique, sensitive data elements are swapped out for fake but valid data to disguise private information.
Data masking ensures that sensitive data is adequately protected, which is necessary for GDPR compliance. Companies handling sensitive data can benefit from data masking in a number of ways, including ensuring data privacy, curbing data breaches, and increasing software development efficiency. They also get to avoid significant fines and legal penalties by masking private consumer data.
How Can Data Masking Help Businesses Achieve GDPR Compliance?
The following are a few ways data masking can help businesses comply with GDPR:
- Protecting sensitive data: Data masking helps organizations in safeguarding sensitive data by swapping it out for fake data that resembles the original but lacks personally identifiable information (PII). This protects private information from being accessed by unauthorized individuals.
- Reducing data exposure: Data masking reduces data exposure by restricting who has access to sensitive information. This allows organizations to adhere to the GDPR’s requirement that personal data must be treated with suitable security and confidentiality.
- Supporting data sharing: By enabling companies to share data with third parties without compromising sensitive information, data masking enables safe data sharing. This is crucial for companies that have to share data with suppliers or partners in order to run their operations.
- Improving data quality: Data Masking raises the standard of data by making sure that the information is correct and updated. This is important to companies who must comply with GDPR by keeping correct records of consumer data.
- Facilitating testing: Data masking makes it possible for companies to use real-world data in testing settings without disclosing sensitive information. For companies who need to test IT systems or software applications in a safe environment, this is a huge advantage.
What Are The Different Types Of Masking Techniques?
The following are some common data masking techniques used based on the level of data sensitivity and the specific use case:
Character Shuffling: Character shuffling involves scrambling the characters within a sensitive data range while still preserving the data format. For example, the last name of someone, like “Redfield” might be scrambled to “deRfldei.” This technique can provide a highly realistic data, but it doesn’t guarantee that the masked data will be valid.
Data Perturbation: Data perturbation involves adding random noise or variance to the data to mask its sensitive elements while still preserving statistical characteristics. This method is frequently applied to numerical data, such as income or age. An age of 50 might be changed to an age of 55 or 45, for instance, by inserting an arbitrary number between -5 and +5. While maintaining the statistical characteristics of the original data, this method can produce more accurate data than character shuffling.
Encryption: In encryption, sensitive information is encrypted so that only people with appropriate authorization can access it. The original data is swapped out for a cipher text that only authorized individuals can decipher. It supports multiple data formats and also offers a high level of security. It can, however, require a lot of computing and slow down data processing.
Tokenization: Tokenization involves replacing sensitive data with a unique identifier or token that has no intrinsic value. For example, a credit card number might be replaced with a token that’s meaningless without the original data. Tokenization is commonly used for credit card data and provides a high level of security while still enabling data processing. However, it doesn’t provide realistic data for testing or other purposes.
Masking with Data Subsetting: Masking with data subsetting involves masking a portion of the sensitive data while preserving the rest of the data. For example, a database might contain customer names, addresses, and credit card numbers. Masking with data subsetting may mask only the credit card numbers while leaving the names and addresses intact. This technique can provide more realistic data than some of the other techniques while still preserving data integrity.
In conclusion, data masking is an essential tool for companies handling sensitive data and bound by GDPR requirements. With this tool, they can use business data for testing and development while still safeguarding the private information of their clients. To achieve strong compliance with GDPR rules though, it is crucial for organizations to thoroughly assess their data masking strategy and to select the appropriate tools and approaches.