Banks, financial services providers and insurance companies process large volumes of sensitive financial data and personal information every day. Data masking helps to ensure that this data can still be used for development, testing, analysis, cloud and AI projects without unnecessarily disclosing real customer information!
The banking, financial services and insurance sector – BFSI for short – is one of the most data-intensive sectors of all. Account numbers, credit card details, personal identifiers, transaction information, creditworthiness data and credit records must be processed whilst being protected against unauthorised access.
In addition to the GDPR, further regulatory and organisational requirements play an important role for financial institutions. These include, amongst others, PCI DSS and, in Europe, the requirements for digital operational resilience set out by DORA.
An effective way to reduce the risk when working with sensitive data is data masking. This involves altering, replacing, pseudonymising, encrypting or redacting confidential values, whilst largely preserving the structure and usability of the data.
Why is data masking important for banks and insurance companies?
Many business processes require realistic data, but not necessarily the actual identities of customers.
Data masking therefore makes it possible to protect sensitive PII – Personally Identifiable Information – even before it is used in development, testing, analysis or external environments.
The advantage: applications and processes can continue to work with realistic data structures, whilst the risk of disclosing genuine customer data is significantly reduced.
Secure test data for DevOps and QA: Banks and insurance companies are constantly developing new applications, mobile services, customer portals and automated business processes. Software testing requires data that reflects production data as realistically as possible.
However, the direct use of real customer data in development and test systems can pose significant data protection and security risks.
Static data masking allows account numbers, credit card details, names or identification numbers, for example, to be replaced with realistic substitute values.
Methods such as:
help to protect sensitive information whilst preserving the data format and structure for testing purposes.
Particularly with relational databases, it is important that relationships between tables are maintained. Consistent and deterministic masking therefore also enables referentially correct test data across multiple tables and data sources.
Data masking across different data sources!
Sensitive information is by no means confined to relational databases any longer.
PII can be found, amongst other places, in: databases, flat files, CSV and JSON files, XML, NoSQL systems, PDFs, Microsoft Office documents, images or other unstructured data sources.
An enterprise-wide data protection strategy should therefore be able to identify, classify and protect such information regardless of its storage location, using consistent rules.
IRI FieldShield focuses in particular on structured data in databases and files. IRI DarkShield extends data masking to semi-structured and unstructured data, as well as NoSQL, document and image formats. This enables organisations to apply masking rules consistently across a wide variety of data landscapes. Both products, along with further features, are included in the end-to-end data management platform IRI Voracity!
Secure data exchange between departments: Banks and insurance companies typically operate with highly decentralised IT and organisational structures. Data is exchanged between different departments for purposes such as risk analysis, fraud detection, customer analysis, reporting or development.
Not every employee or system requires access to complete customer information. Through role-based or dynamic data masking, sensitive data can be displayed differently depending on the user, role or application.
For example, an application may only display the last few digits of a credit card number, whilst authorised systems can still access the necessary information.
Sharing data securely with service providers and partners: Financial institutions regularly collaborate with external IT service providers, software developers, call centres, analytics firms or other partners. The more original data that leaves the organisation’s own controlled infrastructure, the greater the potential attack surface becomes.
An effective strategy is therefore to mask or pseudonymise personal or confidential information before it is shared. As a result, external partners receive only the information they actually need to carry out their tasks.
This principle also supports concepts such as data minimisation, least privilege and privacy by design.
GDPR, DORA and data governance: Data masking can make an important contribution to a comprehensive data protection and data governance strategy.
The GDPR cites pseudonymisation as a possible technical and organisational safeguard. However, pseudonymised information remains personal data if it is still possible to link it to an individual using additional information.
Data masking therefore does not replace a data protection strategy, but rather complements access controls, encryption, authorisation schemes and organisational measures.
For banks, insurance companies and other financial institutions, the DORA – Digital Operational Resilience Act – is also gaining in importance. Among other things, it focuses on ICT risk management, operational resilience and risks posed by external IT service providers.
Consistent data masking can help to reduce the impact of unauthorised data access, as sensitive original values are no longer present in many non-production or external environments.
Protecting sensitive data for the cloud, data lakes and AI: Cloud platforms, data lakes, machine learning and generative AI also open up new opportunities for analysis and automation for financial institutions.
At the same time, a key question arises: how can sensitive financial and customer data be used for AI and analytics without unnecessarily providing original personal data? Data masking, pseudonymisation and synthetic test data offer important approaches to this end.
Sensitive information can, for example, be protected even before migration to a cloud or AI environment. Analytics, development or AI systems then work with protected data rather than real customer identities. This reduces the circulation of actionable PII within complex IT landscapes.
Data masking as an integral part of modern BFSI data security!
For banks, financial service providers and insurance companies, data masking is now far more than just a tool for traditional test data.
Among other things, it supports: secure software development, DevOps and QA, cloud migrations, AI and machine learning projects, data analytics, collaboration with external service providers, as well as data protection and data governance strategies.
The key approach is: Sensitive data should not merely be protected against unauthorised access. Where original values are not required, they should, where possible, not be disclosed in the first place.
IRI solutions enable sensitive information to be identified, classified and selectively masked across various data sources. This allows organisations in the BFSI sector to retain the value of their data whilst better protecting personal and confidential information.
Data protection and data usage do not have to be at odds with one another: Modern data masking lays the foundation for the secure use of financial data in development, testing, analytics, the cloud and AI!
Efficiency meets experience: For more than four decades, our software solutions have been supporting companies in data management and data protection – technologically leading, reliable in productive use and applicable across all industries.
In use since 1978: Numerous well-known companies, service providers, financial institutions and state and federal authorities are among our long-standing customers.
Maximum compatibility: Our software supports both classic mainframe platforms (Fujitsu BS2000/OSD, IBM z/OS, z/VSE, z/Linux) and modern open system environments such as Linux, UNIX derivatives and Windows.