One of the largest U.S. mortgage lenders manages over $350 billion in loans across a complex ecosystem of production and non-production cloud environments. They rely on data-intensive applications to support underwriting, processing, and customer management.
Given the nature of their business, mortgage lenders and financial institutions are subject to stringent and multi-layered data protection and privacy regulations, such as; FTC Safeguards Rule, Gramm-Leach-Bliley Act (GLBA), Consumer Financial Protection Bureau (CFPB), SOX, FFIEC guidelines, and increasingly state-level privacy laws like the California Consumer Privacy Act (CCPA). Compliance requires rigorous control over non-production data environments where customer data often gets replicated for development and testing. Most relevant regulations either require or recommend data masking for sensitive customer data.
The mortgage lender had a legacy DSPM solution that generated large volumes of false positives, and lacked the precision to support automated masking workflows needed to ensure compliance. This created significant manual overhead for the data security team.
The financial institution’s data security and compliance teams turned to Sentra and within weeks, they gained column-level visibility into regulated data, automated classification and masking of workflows, and uncovered hundreds of orphaned data stores that could be deleted to both significantly improve regulatory compliance, reduce storage costs and reduce manual workload for the security team.
The mortgage lender uses a data masking tool to mask regulated data in non-production environments. Their previous DSPM solution lacked depth and breadth of classification and created too many false positives, leading to over-masking and a labor intensive manual verification process. This made it very difficult to spot what data needed to be masked. Like all financial institutions, the lender also has many sensitive data classifications unique to its business operations that had to be manually tagged. Together, all these classification limitations made it difficult to create data reports to feed to their data masking tool.
For known and correctly classified sensitive data, their data masking tool was able to transform it into realistic synthetic records. Once the original required data masking was performed, there was no reliable way to confirm whether data remained masked after refreshes, especially since the masked data resembled real data so closely. The mortgage lender needed visibility into where PII/PCI and toxic data combinations lived across non-production environments and accurately classified sensitive data before and after being masked.
“The challenge wasn't just masking data; it was the persistent uncertainty of whether that data stayed masked after system refreshes. We needed a reliable way to verify ongoing compliance at a granular level.”
— Chief Compliance Officer, Leading US Mortgage Lender
After a thorough evaluation of leading DSPM vendors, the mortgage lender chose Sentra due to several key capabilities. Its flexible classifier system, which supports both regex and contextual logic using AI-powered classifiers, made it easier to identify masked and unmasked data accurately. The platform’s policy engine offered automated scanning for missing or reverted markers, helping teams detect issues early. Sentra also seamlessly integrated into existing workflows without requiring invasive changes to systems or processes.
Key Outcomes:
The mortgage lender deployed Sentra in under six weeks, scanning thousands of data stores across AWS, Snowflake and other cloud and SaaS environments and applied accurate sensitivity labels. Sentra’s classification output determined user roles based on data sensitivity. The integration with Jira and their data masking tool enabled an automated masking workflow, flagging issues to executives and eliminating manual triage.
Following the initial deployment, the financial institution decided to build on this momentum and extend Sentra’s coverage to Google Workspace.
With Sentra, the data security and compliance teams gained deep visibility into sensitive and regulated data across cloud environments and SaaS applications, transforming how they enforce compliance and scale a proactive, automated data protection strategy.
What started as a goal to streamline masking and compliance has become a long-term foundation for cloud data governance. The data security team replaced an underperforming legacy DSPM and gained deep visibility into sensitive and regulated data across cloud environments and SaaS applications, transforming how they enforce compliance and scale a proactive, automated data protection strategy. They also implemented a strategic, automated framework for protecting customer data across every environment and ensuring compliance.
Together, the mortgage lender and Sentra have transformed how the financial institution security team supports excellence in development speed, data protection, and regulatory compliance.
“The challenge wasn't just masking data; it was the persistent uncertainty of whether that data stayed masked after system refreshes. We needed a reliable way to verify ongoing compliance at a granular level.”
