Next, we discuss some common challenges in data masking.
Attribute preservation
It's important for research and analytics that data masking preserves the original data attributes for certain data types. You want to make sure your data masking tools preserve original data types or preserve the frequency of any associated data categories.
For example, if a tool alters the demographic representation of customer data or card category statistics when it obfuscates credit card details, this could impact analytics. Attribute preservation can become challenging in certain data masking processes like randomization or tokenization.
Semantic integrity
The generated fake values must adhere to the business rules and constraints associated with different data types. For example, salaries should fall within a specific range, and national identification numbers should follow a predetermined format. It’s challenging to preserve semantic integrity, but it ensures that the masked data remains meaningful and realistic.
Data uniqueness
In cases where the original data requires uniqueness, such as employee ID numbers, the data masking technique must provide unique values to replace the original data. Absence of uniqueness in key fields may create potential conflicts or inconsistencies.
Integration with existing workflows
It can be challenging to integrate data masking into existing workflows, especially during the initial stages of implementation. Employees may experience inconveniences as they adjust to the new processes and technologies. To ensure a smooth integration and minimal disruption, your organization should focus on careful planning, stakeholder collaboration, and addressing user concerns.