Camouflage Data Masking
Protect sensitive data from exposure in non-production environments. Imperva Camouflage pseudonymizes and anonymizes sensitive data via data masking so you can safely use de-identified data in development, testing, data warehouses, and analytical data stores.
Maintain critical data relationships within databases, across different databases, between different database platforms, and over time. Camouflage uses both deterministic and random approaches to ensure consistency in how sensitive elements are masked and enable repeatable masking process.
Ensure enterprise-class scalability and performance
Mask large volumes of data quickly and easily. Imperva Camouflage Data Masking is engineered to meet the demands of your data-driven business. Platform-specific optimizations enable efficient and scalable masking regardless of database platform or dataset size. An open architecture allows Camouflage to easily adapt to your enterprise environment and existing automation tools.
Classify sensitive data
Automatically locate and categorize sensitive data in your databases. Integrated data classification uses heuristics and statistical analysis to locate personally identifiable information (PII) like name, email, date of birth, SSN and more. Leverage classification results to configure data masking rules.
Choose from multiple transformation techniques
Replace sensitive data with fictional but realistic values using a variety of masking techniques. Choose from pre-defined masking techniques or create custom data transformers. Masked data retains realism of production data and reduces sensitive data exposure, complying with data privacy and protection laws.
Support a broad range of platforms
Get a common interface to mask data across your enterprise data sources. Camouflage supports Oracle, SQL Server, DB2, SAP ASE, Teradata, Netezza, MySQL, PostgreSQL, IMS and more. It also supports a wide variety of operating systems from Windows, UNIX, Linux to mainframe and AS/400.
Specifications and System Requirements
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