{"id":18352,"date":"2025-04-16T19:01:35","date_gmt":"2025-04-16T23:01:35","guid":{"rendered":"https:\/\/www.iri.com\/blog\/?p=18352"},"modified":"2026-01-07T09:18:21","modified_gmt":"2026-01-07T14:18:21","slug":"data-masking-in-the-bfsi-sector","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/iri\/business\/data-masking-in-the-bfsi-sector\/","title":{"rendered":"Data Masking in the BFSI Sector"},"content":{"rendered":"

The Banking, Financial Services, and Insurance (BFSI) industry handles huge amounts of personally identifiable information (PII) and sensitive financial data each day. This data ranges from NID, account information and credit scores to financial transactions and loan documents.\u00a0<\/span><\/p>\n

With cyber risks accelerating and regulations tightening around them securing this data has become a mission critical priority in production, test, and storage contexts. <\/span>Data masking offers an essential layer of protection, enabling institutions to interact with data without exposing the actual values.\u00a0<\/span><\/p>\n

What follows is a non-exhaustive list of key areas where data masking is actively used in the BFSI sector to support innovation, compliance, and risk reduction.<\/span><\/p>\n

1. Protecting Test Data in DevOps and QA<\/b><\/h5>\n

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Banks and insurance companies often work on modernizing their digital infrastructure\u2014building new mobile apps, customer service platforms, or back-office automation tools. These projects require testing in environments that mimic production as closely as possible.<\/span><\/p>\n

Masked datasets can simulate real-world scenarios rather than using real account numbers, card data, or social security numbers. This protects customer privacy while enabling development teams to test functionality, scalability, and performance accurately across systems.<\/span><\/p>\n

Test data realism in masking is achieved with <\/span>static data masking<\/span><\/a> functions like format-preserving encryption or scrambling, as well as pseudonymization and blurring which anonymize values while maintaining their original appearance. In relational database testing, equally important is the preservation of referential integrity in the masked test schema.\u00a0<\/span><\/p>\n

Where there are multiple databases or even disparate data sources, consistent value masking with deterministic functions is possible through the application of rules to like data. Like data can be discovered across sources and have the same function applied as a rule.<\/span><\/p>\n

Most of the use cases in the BFSI market for IRI data masking tools fall into this broad category. For example, several regional banks in the US and federal financial ministries abroad use the IRI FieldShield<\/a> data masking tool to classify and consistently de-identify non-public information (NPI) like account numbers and amounts, as well as personally identifiable information (PII) in lower RDB environments<\/a> — as well as legacy, ACH<\/a>, and Fedwire<\/a> files — where realism and referential integrity matter in the masked results.<\/span><\/p>\n

Realistically masked data is also important in DevOps and quality assurance projects that can involve masked subsets or documents in PDF and MS Office formats, and\/or with PII embedded in images. Data masking tools like IRI DarkShield can find the data in these sources as well and apply the same masking rules across structured, semi-structured, and<\/strong><\/em> unstructured test targets.<\/span><\/p>\n

2. Sharing Data Across Business Units<\/b><\/h5>\n

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BFSI companies are often large, distributed, and compartmentalized. Data routinely moves between departments for risk analysis, customer segmentation, and fraud detection. This internal data movement increases the surface area for potential exposure if shared without precautions.<\/span><\/p>\n

Data masking ensures that only non-sensitive or anonymized versions of data are shared within the organization. Role-based masking and selective redaction allow teams to access the fields relevant to them, without accessing full or identifiable records.<\/span><\/p>\n

There are a number of ways to mask specific classes of data in on-demand scenarios while leaving production data alone. These include:<\/span><\/p>\n