Description: Masking strategies are planned approaches to effectively implement data masking across various datasets. This process involves modifying sensitive data so that it cannot be identified or misused while still maintaining the data’s utility for analysis and testing. Masking can include techniques such as data substitution, randomization, and encryption, which help protect individuals’ privacy and comply with data protection regulations. These strategies are essential in environments that handle large volumes of personal information, such as healthcare, finance, and public services. Proper implementation of these techniques not only helps prevent unauthorized access to sensitive data but also facilitates collaboration between organizations by allowing the use of data without compromising privacy. In an increasingly digital world, where security breaches are common, masking strategies have become a crucial tool for data management, ensuring that information is handled responsibly and ethically.
