How can you enforce data privacy policies in data masking?

2 Choose the right data masking method

Depending on your data privacy goals and requirements, you can choose from different data masking methods, such as static, dynamic, partial, or format-preserving masking. Static masking is applied to a copy of the original data, usually for testing or development purposes. Dynamic masking is applied to the original data in real time, usually for operational or analytical purposes. Partial masking is applied to a portion of the original data, such as the last digits of a credit card number. Format-preserving masking is applied to the original data while maintaining the same format and structure, such as the length and type of a phone number. Each method has its own advantages and disadvantages, so you need to evaluate them carefully and select the one that suits your needs and expectations.

3 Implement data masking rules and processes

Once you have chosen the data masking method, you need to implement data masking rules and processes that ensure the quality and consistency of the masked data. Data masking rules define how the original data is transformed into the masked data, such as the algorithm, the logic, or the source of the fictitious values. Data masking processes define how the data masking is executed, such as the frequency, the scope, or the audit trail of the masking operations. You need to document and communicate these rules and processes to all the relevant parties, such as the data owners, the data users, or the data auditors, and monitor and review them regularly to ensure they are effective and up-to-date.

4 Validate and verify the masked data

After you have implemented data masking rules and processes, you need to validate and verify the masked data to ensure they meet your data privacy goals and requirements. Validation is the process of checking whether the masked data is accurate and complete, such as the format, the functionality, or the integrity of the data. Verification is the process of checking whether the masked data is secure and compliant, such as the level of protection, the traceability, or the accountability of the data. You need to perform these checks both internally and externally, using tools, methods, or standards that are appropriate and reliable.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?