Data Masking

Protect Privacy While Maximizing Data Value

Data Masking is also known as Data anonymization, pseudonymization, or de-identification, is a technique used to protect sensitive data. They transform original data into a sanitized version, ensuring that sensitive information is not exposed to those who shouldn’t have access to it.

Static Data Masking

Irreversibly protect data within the database from being revealed and misused, either continuously or when queried for use.

Perform Safe Analytics

Mask data sets for use in safe analytics, risk reduction, and more. Values are only decrypted for authorized users.

File and Image Redaction

Remove sensitive information as soon as it appears in files, images, or email.

Eliminate Points of Failure

No linkage between original data and masked data ensures there is no possibility of retrieving sensitive information.

De-Identify Personal Information

Change identifiable details with pseudonymization and anonymization.

Cross-source Consistent Masking

Consistently mask data across data stores, including multiple disparate locations.

Data Masking Capabilities are available with

PK Protect Data Store Manager

What Customers Have to Say About PK Protect

“Data privacy is going to continue to be important. And given that we operate at a global scale, we have to stay on top of that. This is why we are making investments in technology and working with partners like PKWARE.”

Harveer Singh, Chief Data Architect & Global Head of Data, Western Union

Data Masking Benefits for Your Data Security Posture

Data Masking empowers organizations to implement industry standards such as PCI, HIPAA & GDPR which aimed to protect sensitive data, preventing insider and external threats, and maintaining privacy. The sensitive data is replaced with the fictitious yet realistic data which ensures mission critical applications continue to produce same results with the masked data which they would do on the authentic and production data.

Permanent Anonymization: Once the data is masked, the original data is irretrievable. This makes it secure for scenarios where the original data isn’t required, such as in test environments.
Consistency: Because the data is permanently transformed, the masked values remain consistent across all databases and datasets. This helps in maintaining referential integrity.
Reduces Data Breach Risk: If the static masked data is exposed or stolen, there is minimal risk since the actual sensitive data isn’t revealed.
Ideal for Non-Production Environments: Developers and testers can work with realistic, but non-sensitive data. This means they can test software or analyze data without risking exposure of the real data.
Regulatory Compliance: Helps organizations adhere to data protection regulations that demand data privacy in non-production environments.

Deterministic Data masking does not just replace the original sensitive data. It produces masked data by preserving the original format of data and maintaining its complexity and referential integrity and offers consistency across different platforms.