August 25, 2023

In today’s data-driven world, where information is a valuable asset, safeguarding sensitive information has become paramount. With the move toward digital transformation, enterprises are consolidating all their digital services into a single global unit where data is stored in a single repository, making it easier to analyze and share. But this comes at a cost, by consolidating enterprise data, it means data is commingled in a single instance and everyone has access to it, resulting in increased risk of unauthorized access and data breaches. On the contrary, if data is not in a single repository, data may be more secure, but it becomes a challenge for organizations to analyze and share the data, creating inefficiencies and impeding decision making.

In this age of interconnectedness and data-driven decision-making, logical data segregation emerges as simple solution to unify and share data in a single global instance without the risk of wrongful disclosure and unauthorized access. Logical data segregation removes the need to physically segregate data by controlling access and safeguard valuable information in the single global instance using zero trust principles. With logical data segregation, organizations can become a more effective intelligent enterprise. So, what does logical data segregation entail and how does it contribute to strengthening data privacy and security within an organization?

What is logical data segregation?

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.

What is Data Segregation

Data segregation focuses on ensuring that different types of data are properly separated, and that appropriate security controls and access policies are applied to each data category. The goal is to protect sensitive information, prevent unauthorized access, comply with regulations, and manage data in a controlled and secure manner.

Scenarios where logical data segregation is needed

As an intelligent enterprise in the digital economy, organizations face a myriad of challenges when it comes to safeguarding sensitive information. Here are a few scenarios where logical data segregation becomes crucial for organizations:

  • Data Privacy and Compliance Challenges: With the proliferation of data privacy regulations, such as GDPR, CCPA, SOX, or industry-specific standards like CMMC, ITAR, EAR, GLBA, HIPAA, organizations face increasing pressure to protect sensitive data and comply with stringent requirements.
  • Shared Infrastructure or Multi-tenant Environments: In scenarios where multiple users or tenants share the same infrastructure, such as cloud platforms or data centers, logical data segregation becomes essential.
  • Insider Threats and Data Leakage: Organizations face the risk of insider threats, where employees or authorized individuals may misuse or leak sensitive data.
  • Data Governance and Data Management Complexity: As organizations accumulate vast amounts of data from multiple sources, data governance and management become increasingly challenging.
  • Collaboration and Data Sharing Requirements: Organizations often need to collaborate with external partners, suppliers, or customers, requiring controlled data sharing.

Now that you understand the need for logical data segregation and the scenarios for implementing this, you may be wondering how can organizations implement data segregation with zero trust principles? Stay tuned to the next part of this article as we discuss why logical data segregation is a vital pillar of modern data protection strategies and the techniques that organizations can use to segregate data logically. Meanwhile, I welcome your input, please share your thoughts in the comments.

If you’re interested in learning about NextLabs’ approach to the logical data segregation, click here to find out more