August 28, 2023

Data is one of the most important assets in today’s organizations. As mentioned in the previous article, logical data segregation stands out as a simple solution for organizations seeking to safeguard valuable data assets, comply with regulations, and maintain a strong security posture. Logical data segregation is the practice of logically separating data based on specific criteria, such as sensitivity, access requirements, or functional requirements. It involves implementing measures to control access, visibility, and security of data based on its classification, user roles, or other relevant factors.

In this part of the article, we will delve into the importance of logical data segregation in today’s data-driven world, along with how this practice empowers organizations to efficiently manage data, enhance data governance, and build trust with stakeholders.

The importance of logical data segregation in safeguarding data

Organizations are modernizing IT and increasing adoption of new technology such as collaboration, Big Data, and AI by adopting the cloud mindset, strengthening cybersecurity, while accelerating collaboration inside and outside the enterprise. For this reason, it is imperative for organizations to embrace zero trust security model to segregate and segment network, data, and applications in an agile manner.

Over the course of last decade, intelligent enterprises have made significant investment to consolidate core business applications and unify business processes across the organization to increase business agility and efficiency. By standardizing processes, consolidating systems, and fostering collaboration, organizations can adapt quickly to changes, optimize operations, and achieve higher levels of productivity.

As the result, the number of access points and users who need access to the data increased drastically. This results to many applications and systems being shared by human and non-human users that do not have the same permission to all the data, causing the access and data security control over application and data to become more complex and costly to maintain. In particular, when different teams or individuals (both insiders and contractors) have distinct responsibilities. Logical data segregation can automatically segregate data based on roles or functions, thereby enforcing separation of duties. This controls increase safeguard to sensitive information and minimizes the risk of unauthorized actions.

In addition, Deloitte has noted the increasing importance of logical separation for organizations pursuing JV and undergoing M&A and emphasized that logical separation should be of top priority for IT executives to enable day-to-day activities and prevent unauthorized access to data. Organizations may have systems that are shared between different entities in relationships like joint ventures, mergers, acquisitions, and divestitures. This heightens the importance of having logical data segregation to limit the access to confidential data (proprietary information, trade secrets, or classified data) to only authorized users.

Furthermore, based on Accenture’s latest Compliance Risk Study – 2022, 95% of the respondents have built or are building a culture of compliance to share the responsibility across the enterprise. It is observed that compliance functions worldwide are experiencing the pressure of rapid transformation and the necessity to address an expanding agenda of compliance. As such, logical data segregation becomes essential to ensure that access controls and security measures are in place to protect sensitive data and maintain compliance. This approach helps organizations to be compliant with different data protection regulations if they deal with sensitive or regulated data like personally identifiable information (PII), financial data, or controlled technical data.

With the rise in remote works and increased use of cloud-based technologies and collaboration tools, it is crucial to implement a robust data segregation solution along with network segregation to limit access to sensitive data as the impact of unauthorized access to data can be detrimental to businesses.

Moreover, logical data segregation is significant in the geopolitical climate where data sovereignty, national security concerns, and regulatory requirements vary across different regions. Organizations will need to enable fine-grained authorization within the same company by externalizing security controls and enforce zero trust access policies to determine a user’s access rights. With logical data segregation, organizations can adhere to regional data protection laws, safeguard sensitive data from geopolitical risks, and maintain trust with customers and stakeholders.

Techniques to segregate data logically

Logical data segregation is a modern approach used to enhance data management, security, and privacy. It applies advanced technologies and methodologies such as data classification, entitlement, fine-grained access controls, and digital rights management to ensure that the right set of data is being disclosed with the proper safeguard to the right users.

There are several techniques and strategies you can employ to logically segregate data. The specific technique you choose will depend on the nature of the data and your organizational requirements. Following are some commonly used techniques:

Techniques to segregate data logically
  • Data segmentation is a technique that can segregate data logically by dividing data into smaller, and more manageable partitions based on specific criteria. It can be divided logically based on attributes or characteristics such as data type, sensitivity level, departmental boundaries, geolocation, or access patterns. This segmentation helps enforce data separation and allows for more granular access controls. ZTA emphasizes a data-centric security approach which focuses on protecting the data itself rather than solely relying on network or perimeter defenses. By implementing data segmentation based on Zero Trust Architecture, organizations can apply a structured method to categorize and manage data based on its sensitivity and control access dynamically based on policy.
  • Data masking is another way for organizations to segregate data by replacing sensitive data with fictitious but realistic values using techniques such as nulling out, encryption or substitution. These techniques allow organizations to protect sensitive data while still preserving the format and structure required for analysis or application testing. Data masking helps maintain data privacy and security while allowing access to non-sensitive or anonymized data.
  • Data Filtering is a different technique to logically segregate data by applying filters to data sets to control access and visibility based on specific criteria. Data filtering can be implemented at the application and data access layer, ensuring that users can only access data they are authorized to. There are various approaches to filter the data such as row-level filtering, column-level filtering, attribute-based filtering, time-based filtering, location-based filtering, and custom filtering rules. Data filtering allows for fine-grained control over data visibility and access, ensuring that only authorized users can access the data while maintaining logical data segregation.
  • Fine-grained Access Control is an approach that uses attribute-based access control (ABAC) to ensure that users can only access the data they are authorized to see. With the implementation of ABAC, data access is restricted by policy that is based on identity, role, user attributes, and metadata. This enhances logical data segregation by enabling least privilege access based on dynamic and context-aware access control policies to ensure that only authorized individuals or systems can access specific datasets.
  • Digital Rights Management (DRM) is a technique that can be used to segregate data logically by applying rights protection to selected files based on policy, ensuring persistent protection of sensitive information at rest, in transit, and throughout its lifecycle. DRM helps prevent unauthorized access even if the data is compromised or misdirected. Employ policy-based authorization with strong encryption algorithms and secure key management practices to ensure the integrity and confidentiality of the data.

In a nutshell, data segregation helps protect sensitive information, comply with regulations, enhance data management, and ensure the continuity of critical business operations. By implementing effective data segregation practices based on Zero Trust principles, organizations can achieve efficient data management, safeguard their data, maintain trust with stakeholders, and reduce the potential negative consequences associated with data breaches or non-compliance.

Now that you understand the benefits and techniques of logical data segregation, you can learn more about some technical examples in this whitepaper on Dynamic Data Protection Using Attribute-Based Access Control (ABAC).

For additional articles, product updates, and more; join NextLabs community at: