Examples of Data Masking That Should Be Followed

It’s becoming increasingly necessary for companies to use data masking. Firstly, because there has been an increase in the number of cyberattacks on computer systems. Secondly, because it is essential to comply with the requirements of sensitive information protection regulations, such as the GDPR or the CCPA.

It is an effective alternative for companies and organizations that base their activity on the collection and processing of personal information. And to understand its application in the various industries, it is important to delve into the examples of data masking and its diverse techniques.

What does language modeling consist of?

Data masking is the process whereby sensitive information within a certain data set is replaced, encrypted or modified, in order to obtain a new version of that record, with a similar structure, but with the confidential values hidden.

In this data masking process, a kind of surrogate for the original information is created that does not expose sensitive or personal data, and is fully functional in a variety of domains.

For example, identifiers (names, ID numbers, passport numbers, etc.) in a customer database are replaced or substituted by certain characters or symbols.

Types and examples of data masking

The types of data masking enable companies to use and share content and documents securely by protecting confidential information. There are basically two versions of this process:

Data masking

In static data masking, the sensitive values are permanently replaced, because the masking procedure is applied directly to the database. 

One of the great advantages of this type of data masking is the permanent removal of sensitive or confidential information. Therefore, an attack on the information system will be unsuccessful. 

• Among the examples of static data masking is the hiding or changing of sensitive values in an industry's original database, to share the information with suppliers or to use it for research and development, without revealing actual data. 

Dynamic data masking

On the other hand, in dynamic data masking, confidential identifiers or values are only replaced in the information that the user requires to view, keeping the original database intact.

One of the advantages of this type of data masking is that it works in real time, does not consume resources in the forward processing of information and adds an additional layer of security. 

• Among the main examples of dynamic data masking is the protection of information in the industrial production sector, as management systems need to view and read reports. 

More information:

 6 Personal Data Anonymization Techniques You Should Know About

The most common data masking use cases

Examples of data masking include the following common use cases:

• Masking of credit or debit card numbers. In this case, banks avoid exposing sensitive customer data by hiding the last 4 digits of the payment card number. In this way, any member of the institution can access the card information, guaranteeing data protection. 

• Data masking in digital files. Before digitally filing information, companies and institutions, such as those in the healthcare sector or in the legal field, employ the data masking process to hide names, social security numbers, bank accounts and other sensitive data of customers, patients, partners, etc.

• Data masking in resumes. These days, efforts are often made to eliminate any bias in recruitment and hiring processes. In order to focus only on the experience and skills a candidate possesses, companies are implementing data masking techniques to hide information such as gender, race and image from each resume.

• Companies or institutions also use different data masking techniques in the following cases: 

• In the safeguarding of intellectual property, by means of document encryption.

• To store or share health information, using data encryption.

• In the storage and use of data from access control systems.

• For the security of sensitive data, in court cases and legal documents. 

More information:

Best data anonymization tools and techniques

Sectors targeted:

Among the main sectors requiring a data masking process to be carried out are:

• The legal field (legal experts and court proceedings)

• Financial institutions

• Banks

• Insurance companies

• Hospitals and health care companies

• Consulting firms

• Public administrations, etc.

More information:

Data Protection and Anonymization in the Context of Financial and Legal Services

Examples of data masking and techniques

The most commonly used data masking techniques are:

Data anonymization

In data anonymization, sensitive identifiers or values that connect the owner to the stored information are removed or encrypted. It is a way of de-identifying information, guaranteeing data security in accordance with European regulations. 

Search substitution

In substitution, the original values are replaced with alternative random data provided by a given aggregated search file.  

• For example, several companies use a random search file to mask customer names. 

Encoding

Encoding is a data masking technique in which a specific algorithm is used to randomly alter or rearrange the order of numbers or characters.

• For example, data encryption is used in official documents to safeguard the security of government data and information. 

Data encryption

In data encryption, an algorithm of higher complexity and security is used to hide confidential or sensitive values. These values, being masked, can only be displayed if they are decrypted with a password. 

• For example, some companies employ encryption in their database so that only authorized users can access the information, using special keys.

Masker for efficient data masking

At Pangeanic, we offer a complete data anonymization service, using various techniques for the removal of identifiers from a database, documents and publications. This destroys electronic traces and clues that could expose confidential details. 

We have an efficient platform for AI-based language processing, which allows you to translate automatically in seconds using powerful neural networks and facilitates anonymization and pseudonymization of names and other personal data present in your communications and content.

We have also developed Masker, a data masking system that automatically detects personally identifiable information and allows you to adjust the sensitivity level of the process. 

For effective data masking, compliant with GDPR and other legal regulations, contact us.

At Pangeanic, we understand the importance of data protection for your company. We can help you safeguard your customers' privacy, as well as maintain and enhance your corporate reputation.