Learn key data privacy terms like anonymization, authentication, encryption, and GDPR. Stay informed on compliance, security, and data protection essentials.

Data masking, de-identification, and pseudonymization explained. Explore key differences, global standards, compliance rules, and challenges in securing sensitive data.

Data encryption and data masking both protect sensitive information but differ in purpose and implementation. Encryption secures data from external threats by converting it into unreadable ciphertext, requiring a key for decryption, while data masking de-identifies data by replacing it with realistic, irreversible values to protect it from internal access, aiding testing and analysis without re-identification.

Protecting sensitive information through masking prevents unauthorized access, but many myths surround the process. Common misconceptions include confusing it with encryption, requiring centralized systems, and delaying implementation.

Data masking methods vary based on needs and roles within organizations. Static data masking (SDM) uses a stable, unchanging copy of a database, while dynamic data masking (DDM) allows role-based data access. On-the-fly masking and obfuscation are other options, with the latter being a more extreme and costly approach.

On-the-fly data masking masks data in real-time without a staging environment, supporting continuous development. It extracts, transforms, and loads masked data into the target environment.

The Hush-Hush Sensitive Data Discovery Tool helps identify and assess sensitive data across databases using proprietary algorithms. It scans for metadata and data patterns, creating workflows to de-identify data efficiently.

Sensitive data refers to personally identifiable or protected health information that can uniquely identify an individual, such as Social Security Numbers or fingerprints. Protecting this data is crucial to prevent fraud and privacy breaches.

Tokenization replaces sensitive data with randomly generated tokens, ensuring the original data is protected. It is often used to secure payment information and meet compliance requirements like PCI DSS.

Data masking transforms sensitive data into realistic values to protect privacy and ensure compliance with regulations. It is used for preventing breaches, creating safe test data, internal control, and more.

Data masking techniques like substitution and shuffling protect sensitive information while maintaining format. Methods such as random substitution and date variance ensure privacy and security.