Introduction
In today's data-driven world, the ability to access and analyse information from a variety of sources is critical. Whether it's for customer insights, business intelligence, or AI model training, organisations are increasingly consolidating their data silos using data integration approaches like Data Hubs, Data Lakes, Data Warehousing, or Data Virtualisation.
However, the more data we gather and centralise, the more challenging it becomes to manage access to that data. It's a balancing act between usability and cybersecurity, with the need to protect sensitive data and comply with regulations on one hand, and the need to enable flexibility and speed in accessing data on the other.
Transitioning to Data-Centric Access Policies
Data integration strategies often abstract away the backend system locations of data from consumers. This means that maintaining system-level access rules simply won't work, as consumers will look to integrate using data-driven models from a centralised capability. This transition from system-centric to data-centric access can be challenging, but it is crucial for the efficient use and management of data.
Balancing Usability and Cybersecurity
Striking the right balance between restricting access to data and enabling its use is a delicate task. If access rules are too tight, data consumers might bypass the access controls or create new silos to avoid it. If the access rules are too loose, the confidentiality and integrity of the data are put at risk.
The level of exposure an organisation is willing to take will ultimately depend on its risk appetite and regulatory environment.
Our recommendation during the design phase is to utilise a method known as the Bow Tie risk assessment. The Bow Tie method is an effective tool that visually represents all plausible accident scenarios. It outlines the potential causes of an event, pinpoints the safety barriers, and describes possible consequences if those barriers fail.
Establishing Automation at Scale
As data volumes grow, manual workflows and cybersecurity approvals for access can become a bottleneck, slowing down data consumers and inhibiting effective governance. Data access processes need to be automated to scale with the data integration platform.
Building a Multi-Layered Data Access Approach
To manage these challenges, a multi-layered data access model can be established. The foundation of this model is integrating meta-data with access. This allows data consumers to search and identify information based on specific attributes, while enabling the application of data-driven access policies.
Data access rules should be abstracted and centralised to ensure consistent application across various technology services. These rules should also be defined in multiple layers, addressing different stakeholders and providing flexibility in managing the lifecycle of these rules.
Summary
Managing data access in modern environments requires a balance between usability and cybersecurity. Transitioning from system-centric to data-centric access policies is crucial, and automation is needed to scale with data integration platforms.
As we continue to centralise and democratise data, prioritising cybersecurity becomes even more important. Ensuring secure access to data isn't just about protecting against external threats, but also about managing internal access to sensitive data.
By implementing a robust, multi-layered data access model, organisations can better manage their data, improve their cybersecurity posture, and enable their teams to make data-driven decisions more efficiently and securely.
Comments