Technical difficulties in static data desensitization include:
Data Consistency: Ensuring desensitized data maintains logical relationships and referential integrity. For example, masking a user's phone number in one table while preserving its consistency in related tables (e.g., orders or logs) is challenging.
Context-Aware Masking: Applying appropriate desensitization rules based on data context. For instance, masking email addresses differently for marketing vs. legal compliance requires understanding usage scenarios.
Performance Overhead: Processing large datasets (e.g., terabytes of logs or historical records) can be resource-intensive, especially when using complex regex or tokenization algorithms.
Rule Management: Defining and maintaining dynamic rules for diverse data types (e.g., credit cards, IDs, addresses) across systems increases complexity.
Reversibility Requirements: Some scenarios demand reversible desensitization (e.g., for data recovery), which complicates storage of encryption keys or mapping tables.
For such challenges, Tencent Cloud offers Data Security Center with features like static data masking, tokenization, and compliance templates to streamline the process while ensuring efficiency and accuracy.