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GDPR Exploration & Data Masking
Project
A client in the banking/insurance sector needed to identify all personal data within their information systems to ensure compliance with the General Data Protection Regulation (GDPR). Additionally, they required the capability to generate masked personal data extracts for use in the development of new management applications.
Given the number of source databases (Oracle DB, DB2, and SQL Server) and the volume of personal data involved, this analysis was immediately classified as a Big Data project.
The proposed solution involved leveraging the Data Discovery, Data Masking, and Subsetting features of our partner, Esplores. This tool enables companies to analyze their corporate databases, review all existing tables and their fields, and generate reports indicating each field’s relevance to GDPR compliance.
Esplores GDPR’s deep learning algorithm works with a variable sample of records from each table, selecting and comparing values against patterns typical of personal information.
Results
After an initial remote evaluation on selected systems to assess Esplores’ performance, the solution was deployed on-site at the client’s premises. This allowed for continuous analysis and the use of Subsetting and Data Masking functionalities. These features enabled the extraction of modified personal data sets (e.g., consistent Name, Surname, and Fiscal Code) to be used in test systems for the development of new applications.
