In our project, developers frequently need production-like data to reproduce complex bugs and perform integration testing, and Tonic.ai generates a sanitized dataset while maintaining referential integrity, enabling realistic testing without exposing customer satisfaction. Tonic.ai works when we are doing project integration, and we can compare different products such as Microsoft. When implementing such cases, the development environment is available much faster, testing becomes realistic, and compliance risk is reduced. Those are the benefits we receive, especially since our production database contains sensitive information such as names, email addresses, and phone numbers. The developer and QA team need realistic data for testing, but using production data directly violates our security and compliance requirements. We evaluated several options and selected Tonic.ai because it automatically discovers sensitive data, generates realistic synthetic or masked data, preserves relationships between tables, and significantly reduces the manual effort required to prepare a non-production environment. The main use cases are to provide production-like data to reproduce complex bugs and perform integrations, and whenever we deal with pipelines and SQL Server, no manual effort is needed. No manual SQL masking script is required, as we can directly incorporate it with Tonic.ai. Those are the main use cases. With the automatic provisioning pipeline integrated with CI/CD, every time a new development or QA environment provisions, Tonic.ai creates a sanitized copy of the database automatically. Instead of a DBA manually restoring production backups and running masking scripts, the pipeline invokes Tonic.ai to generate a masked dataset. The application is then deployed against the sanitized data, allowing developers and testers to start work automatically. Typically, the pipeline flow goes this way: production database backups and provisioning a new server to a cloud database. Tonic.ai reads backups and schema, identifies sensitive fields, masks or creates synthetic data while preserving data relationships, synthesizes the data, and starts the application deployment. Afterward, automated integration and regression tests run, and at the end, the QA team receives a ready-to-use environment. These are the main use cases and benefits of the pipeline's integrations.
Data Masking transforms sensitive data into an obscured version while retaining usability. It ensures data privacy without compromising data functionality, making it a key tool for organizations dealing with sensitive customer information.Data Masking offers techniques to hide data in non-production environments, ensuring compliance with regulations. Its role is crucial in industries handling sensitive information, where testing and analytics need real data that doesn't expose personal...
In our project, developers frequently need production-like data to reproduce complex bugs and perform integration testing, and Tonic.ai generates a sanitized dataset while maintaining referential integrity, enabling realistic testing without exposing customer satisfaction. Tonic.ai works when we are doing project integration, and we can compare different products such as Microsoft. When implementing such cases, the development environment is available much faster, testing becomes realistic, and compliance risk is reduced. Those are the benefits we receive, especially since our production database contains sensitive information such as names, email addresses, and phone numbers. The developer and QA team need realistic data for testing, but using production data directly violates our security and compliance requirements. We evaluated several options and selected Tonic.ai because it automatically discovers sensitive data, generates realistic synthetic or masked data, preserves relationships between tables, and significantly reduces the manual effort required to prepare a non-production environment. The main use cases are to provide production-like data to reproduce complex bugs and perform integrations, and whenever we deal with pipelines and SQL Server, no manual effort is needed. No manual SQL masking script is required, as we can directly incorporate it with Tonic.ai. Those are the main use cases. With the automatic provisioning pipeline integrated with CI/CD, every time a new development or QA environment provisions, Tonic.ai creates a sanitized copy of the database automatically. Instead of a DBA manually restoring production backups and running masking scripts, the pipeline invokes Tonic.ai to generate a masked dataset. The application is then deployed against the sanitized data, allowing developers and testers to start work automatically. Typically, the pipeline flow goes this way: production database backups and provisioning a new server to a cloud database. Tonic.ai reads backups and schema, identifies sensitive fields, masks or creates synthetic data while preserving data relationships, synthesizes the data, and starts the application deployment. Afterward, automated integration and regression tests run, and at the end, the QA team receives a ready-to-use environment. These are the main use cases and benefits of the pipeline's integrations.