Data Masking
Data masking obscures or replaces sensitive information in a dataset to minimize exposure while maintaining the data’s functional value. The purpose is to prevent AI models from learning and incorporating sensitive information, such as personal identification numbers, financial data, or medical records, which could be used for malicious purposes or violate privacy laws.
Masked data can be used for training and analytics purposes while maintaining the confidentiality and security of the original data. Different data masking techniques include substitution and encryption.