Data Masking
Data masking is a crucial data protection strategy. It plays a key role in today’s digital world. In this age of digital growth and increasing cyber threats, protecting sensitive information is more important than ever.
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.
Why is data masking important?
In today’s digital world, data about all of us is collected and stored. Companies, governments and organizations have access to enormous amounts of data, including personal information such as names, addresses, telephone numbers and email addresses.
This data can be used for various purposes, such as providing personalized services and improving products. But if this data falls into the wrong hands, it can also be misused for criminal activities such as identity theft and fraud.
Data masking is therefore an important way to protect the privacy of individuals and reduce the risk of unauthorized access to sensitive information.
Product specifications Data Masking
General characteristics
- 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.)
Functional properties
- 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
Technical properties
- 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? DataFactory
DataFactory is our fully automated solution to quickly and easily mask any type of database and application. This masked data can then be safely used outside the production environment.
Would you like to receive more information or would you like a product demonstration? Please contact us!
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