About DATPROF Privacy
When testing with production data, security and privacy concerns must always be a top priority. Whether you're working with an internal testing team or outsourcing to an external party, it's crucial to avoid exposing sensitive data that could identify individuals or harm your business. Storing such data in easily accessible locations only increases the risk. By performing due diligence on data protection, you mitigate these risks, safeguard corporate information, and ensure compliance with evolving data privacy regulations. In short, data masking is not just important, it is becoming an essential requirement for businesses.
Generally speaking, there are two ways to effectively comply with data privacy requirements. Data masking, where you take production data and change it so it can not be used to identify persons, or data generation, where you generate complete sets of data from scratch to emulate the production environment data. DATPROF Privacy can do both.
The working principle of Privacy
DATPROF Privacy does not process privacy-sensitive data directly. Instead, it works exclusively with database metadata. In simple terms, this means that DATPROF Privacy connects to the source database and uses information about its structure, such as table layouts and relationships, without accessing the actual sensitive data itself. This approach ensures privacy while still allowing for effective data masking and generation.
Each deployment package created in DATPROF Privacy consists of a set of instructions that are executed directly on the database, ensuring that the data remains within the local environment. This approach guarantees that no sensitive data ever leaves the premises, maintaining privacy and security throughout the process.
Imagine a source database with a table called "Customers" containing 15 columns. DATPROF Privacy can import the table and column names, such as "FIRST_NAME," and identify its datatype as VARCHAR, along with other relevant details. However, DATPROF Privacy does not access or know any of the actual values stored in the "FIRST_NAME" column. This is a crucial feature for maintaining data privacy, as the tool never handles or stores sensitive data. It only works with the structure and metadata of the database.
DATPROF Privacy provides various methods for anonymizing, modifying, removing, or altering data. Its use is strictly intended for modifying test data, defined as data derived from production data or synthetic data sources, which can be replaced if lost or damaged. DATPROF Privacy is not recommended or supported for modifying production data, as this can lead to permanent data loss. Users should never alter data that cannot be easily restored and are always responsible for ensuring the correct use of DATPROF software.
General instructions
Creating a masked database involves, in very general terms, performing the following steps:
Creating a new project.
Providing a connection to the target database.
Importing meta data from the target database.
Configuring masking functions, foreign keys and optional scripts.
Using DATPROF Runtime to generate a package.
Deploying the masking template.