Newsletter
Apr 2025
As organizations accelerate their adoption of AI and Generative AI, the need to responsibly manage training data has never been more critical. This edition highlights three real-world case studies from distinct industries, showcasing how customers have used Mage Data’s Data Discovery & Data Masking capabilities to train their AI Models to unlock the value of their data – safely and effectively. These companies are enabling advanced AI initiatives while maintaining strict controls to prevent exposure of sensitive or regulated information.
A prominent analytics provider working with banks and financial institutions faced intense challenges. Their clients refused to share raw data and demanded strict SLA adherence, requiring data to be processed in under five minutes. Compounding the issue was the need to manage data in various formats while complying with global data privacy regulations.
Mage Data implemented an automated solution that transformed their workflow. Using a “watchdog” feature, every new file drop triggered instant discovery/anonymization through API calls. This automation ensured processing speeds remained within SLA thresholds and removed the risk of sensitive data exposure. The solution supported compliance with GDPR, CCPA, and HIPAA, all while preserving demographic insights essential to accurate analytics.
Download the full case study here.
A leading Swiss bank needed to share nearly 1,000 sensitive data files with a third-party BFSI provider – all within the constraints of Switzerland’s strict confidentiality regulations. The data included over 35 critical identifiers such as IBANs and personal details that required anonymization, encryption, or tokenization.
Mage Data’s response was a comprehensive security strategy. A secure data transfer gateway ensured all files were protected during transit. Context-preserving encryption allowed IBANs to retain usability, and support for diverse formats (CSV, JSON, XML, etc.) preserved data structure and integrity. The result: flawless re-identification of files after processing and full regulatory compliance without compromising the bank’s operational efficiency.
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One of the world’s largest consulting firms found itself in a bind – it had access to several Petabytes of data that was a goldmine for analysis and deriving business insights – not only for itself, but for its customer’s too. However, their Chief Privacy Officer restricted the use of this data because it contained Personally Identifiable Data (PII) of EU citizens – and therefore prohibited the use of this data for analytics due to GDPR compliance requirements. Traditional anonymization techniques either distorted the data, undermining model accuracy and business value, or did not protect the data well enough to prevent reversibility.
Mage Data offered a hybrid solution: Test Data generation for all operational data (PII) while maintaining all integrity of the data. Our patented technology, identities, created fake datasets that accurately mirrored real-world demographics without exposing any personal information. The deidentified data seamlessly integrated with the firm’s existing systems and maintained the structure needed for accurate analytics. This enabled the firm to continue processing massive data volumes while ensuring privacy and maintaining the quality of its AI outputs.
Download the full case study here