DataMasking

Data masking is fast , safe and representative

Quickly create an anonymized dataset that is representative of the original personal data. The data can no longer be traced back to unique persons, but does provide reliable test or analysis results.

What is data masking?

Almost every organization does everything it can to protect personal data against hackers and other criminals. The ‘front door’ is firmly locked. But there is also a back door and sometimes it is wide open. Databases with personal data are copied for analysis or to test software. That’s where data leaks lurk. Data masking is a method to anonymize or pseudonymize production data, so that the data can no longer be traced back to unique natural persons. At the same time, the representativeness of the data is maintained.

Pseudonymization

If it is desirable to make data traceable again, pseudonymization is a better option. This is also possible with data masking. In that case, all names are replaced by a fixed name according to a certain rule. This can be useful, for example, in medical analyses, where a doctor sometimes needs to be warned about an individual case.

Anonymize

With data masking you can both pseudonymize and anonymize. Anonymization is done by mixing personal data according to rules that the user enters himself. For example, by changing all names. For example, rules keep families together or maintain zip codes for geographical analysis. Anonymization is irreversible.

• Easy to implement
• Quick to roll out (on average 2 to 6 weeks)
• Low opertaing costs
• Anonymized data is irreducible (in line with requirements of the GDPR)
• Speeds up the development cycle
• Aligns with agile working
• Prevents the need to maintain risk capital
• Prevents the impact of data leaks (fines and reputational damage)
• Anonymized data can be widely used (Test, Analysis, Training, Demo, Support, Outsourcing, etc.)

• Anonymize consistently over time without using a ‘translation table’
• Consistently anonymize an entire application chain
• Maintaining relevant relationships (if desired)
• Geographical distribution of relationships remains intact (if desired)
• Ages remain unchanged (if desired)
• Generated data adheres to data-specific rules
• Data quality remains unchanged
• Anonymized data is easy to distinguish from production data

• Completely database independent
• Easily scalable
• High performance
• Cross-platform
• Minimal management effort
• Easy integration with CI/CD pipeline
• Supports large data sets
• Anonymization is done completely in-memory
• Ability to add your own masking rules
• Comes standard with more than 10 options to anonymize data

Our solution

The DataFactory is our fully automated solution to mask any type of database and application quickly and easily. This masked data can then be safely used outside the production environment.