Challenges
As the large Canadian bank embarked on its digital transformation journey, it was faced with the daunting task of maintaining data integrity across a diverse array of databases and files. With critical systems reliant on SQL Server, Oracle, MongoDB, and PostgreSQL databases, achieving uniformity and consistency across this varied landscape was crucial. This would ensure that masked data, such as ‘Joe Smith’ converted to ‘John Doe’ in one system, remained ‘John Doe’ across all systems.
Simultaneously, ensuring the synchronicity of masked data was complicated by the large Canadian bank’s requirement to ensure the privacy and security of their customer’s information while meeting regulatory obligations. Data breaches in test, development, or third-party environments, all of which potentially harbored sensitive data, posed a significant risk.
Additionally, this transformation project was not isolated to a single department. The interdepartmental nature of data management meant that the cybersecurity team, databases and data operations team, and DevOps team all needed to work in close collaboration. Ensuring seamless communication and coordination between these teams was a challenge in itself, demanding a solution that would bridge these disparate units effectively.
Moreover, the task of identifying and masking sensitive data was far from straightforward. Data masking had to be executed in a way that ensured data remained functional and met the bank’s strategic needs without compromising its security. The task was further complicated by the need to preserve the format of the masked data and retain usability, despite transforming the information to a non-sensitive form.
Navigating these complex challenges required a sophisticated solution that could deliver on multiple fronts. It needed to integrate seamlessly across a multitude of platforms, facilitate cross-team collaboration, maintain data integrity, and meet regulatory obligations, all while ensuring optimal security.