Masking obfuscates parts of the data in columns, or completely substitutes the cleartext data with synthetic data. Typical use cases are for example to hide sensitive information from DBAs and other power users with broad access rights, or to display/hide sensitive information depending on the user role for example in a call center.

While data masking is a very important tool for many use cases that need to hide parts of sensitive records, it is often not suitable for protecting complex mass data. Common pitfalls include that masking does not always achieve the required level of security or that the masked data is stripped of too much information and can no longer be used for the intended purpose. Anonymization provides a structured approach to protect sensitive data while still preserving the ability to use it in defined analytic scenarios. It lets you gain insights from data that could not be leveraged before due to regulations.

Similar to masking, anonymization is applied when data is queried, leaving the original data unchanged.

We’ll help to implement the data masking and anonymizations requirements of the customers across the globe.