Data masking techniques for development environments include tokenization (replacing sensitive data with random tokens), encryption (AES-256 for structured data), and pseudonymization (replacing names with aliases). Format-preserving masking retains data structure (e.g., masking credit card numbers while keeping length/type). Dynamic masking generates fake data on-the-fly, while static masking creates permanent masked datasets. Tools like HashiCorp Vault or Oracle Data Masking automate these processes for compliance (e.g., GDPR).
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Data masking techniques for development environments include tokenization (replacing sensitive data with synthetic values), encryption (scrambling data via algorithms), and shuffling (reordering datasets). Static masking creates permanent anonymized copies, while dynamic masking applies real-time filters during queries. Techniques like format-preserving encryption maintain usability for testing without exposing PII.
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Data masking techniques include tokenization (replacing sensitive data with synthetic tokens), shuffling (randomizing values while preserving structure), and encryption (AES-256 for static data). Dynamic masking applies rules at query time (e.g., showing last 4 digits of SSNs). Format-preserving encryption (FPE) maintains data types, while proxy servers intercept and mask real-time API responses. Masked datasets comply with GDPR and HIPAA.
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