In today’s digital landscape, businesses of all sizes generate, accumulate, store, and process more data records in more places than ever before. However, it has become increasingly challenging to classify and track all that data, let alone make use of it. Enterprises rush into digital transformation with their isolated data silos and outdated legacy code while 86% of developers admit they do not consider application security a top priority when coding. As a result, CISOs are facing burnout as they attempt to enforce code security best practices, privacy regulations, and compliance standards into the software development lifecycle that is often chaotic.
In such a scenario, mapping distributed data is a necessity for businesses. However, it is not as easy as it seems. Data mapping involves discovering, assessing, and classifying an application’s data flows, using manual, semi-automated, and fully automated tools to survey and list every service, database, storage, and third-party resource that makes up data processes and touches data records. This gives a holistic view of the application’s moving parts and helps understand the relationships between different data components, regardless of storage format, owner, or location (physical or logical).
There are significant challenges when it comes to data mapping. These include the fact that depending on an application’s overall size and complexity, a manual data mapping process can take weeks or even months. Furthermore, it is effortless to deploy new data-based features, microservices, and workflows using cloud-based tools and services, making the process even more challenging. Additionally, data management in outdated legacy systems can be intricate due to a lack of collaboration and documentation from the original writers of the code.
Despite these challenges, businesses must map their data effectively to ensure privacy by design principles are well implemented. In doing so, they need to scan their codes to map their data utilizing static code analysis techniques to discover and map out data flows in their application. Enforcing clear boundaries for microservices and minimizing the coupling between these services is another effective strategy in mapping distributed data.
In conclusion, data mapping is a crucial part of any business’s data-oriented operations in the digital age. By mapping data effectively, businesses can shift privacy left and integrate privacy engineering, compliance, and code security into the CI/CD pipeline. The Head of Developer-First Security at Check Point Software Technologies, Dotan Nahum, believes businesses can achieve this by taking proactive measures to protect their sensitive data. He also thinks businesses should continue to adopt modern CI/CD pipelines and processes that employ Static Application Security Testing (SAST) tools in identifying code issues, security vulnerabilities, and code secrets accidentally pushed to public-facing repositories. By doing this, businesses will be equipped with the right tools to ensure the safe and secure movement of their distributed data.