How Sensitive Data Sprawl is Hurting Your Organization’s Data Management

Any data security officer will tell you that protecting sensitive data is a constantly-evolving uphill battle. The threat landscape is always changing, while new vulnerabilities are discovered at an ever-increasing (and frightening) rate. 

The accelerating sprawl of sensitive data in enterprise environments only complicates this challenge. In most companies, data security officers simply have a bigger and growing footprint of sensitive data to protect. But letting sensitive data sprawl grow within organizations is unsustainable in the long term at best and dangerous at worst.

The best way to solve an issue is to recognize that the problem exists and then identify its root causes. Sensitive data sprawl is no different. Nipping the issue in the bud with full knowledge of what is being prevented will allow enterprises to circumvent all of the issues that sensitive data sprawl can cause.  

What is Sensitive Data Sprawl?

Sensitive data sprawl refers to the widespread and often uncontrolled distribution of sensitive information across various platforms and locations within an organization's IT environment. Sometimes, the sprawl extends beyond the organization’s perimeters to trusted third parties, such as offshore development teams or testers. 

Today’s enterprise security leaders are aware of the propagation of sensitive data sprawl. In a recent poll of 61 enterprise CISOs conducted by Bob Bragdon of RiskStrat Advisory, over 90% of respondents reported that innovation projects are expanding the footprint of sensitive data “somewhat” or “a great deal.”  

Regardless, sensitive data, such as personally identifiable information (PII), financial records, intellectual property, and health records, can end up being stored in multiple, potentially unsecured locations, including on-premises servers, cloud storage systems, laptops, mobile devices, and third-party applications. The sprawl makes it challenging to track and secure this data, leading to increased risks of unauthorized access, data breaches, and non-compliance with data protection regulations.

The Leading Driver of Sensitive Data Sprawl

Many factors drive sensitive data sprawl.  The most commonly cited are the increasing digitization of business processes, the adoption of cloud services, and the proliferation of mobile and remote work arrangements. 

The biggest driver of sensitive data sprawl, however, is the skyrocketing number of non-production data environments. These non-production environments include development, testing, staging, and quality assurance (QA) environments, as well as many data stores used for analytics and AI model training. They can be found across on-premises data centers to public cloud services, and everywhere in between. They play a crucial role in the software development lifecycle, machine learning, building analytics pipelines, and other important activities of innovation. Many of these environments contain replicas of production data, much of which is sensitive. 

The number of non-production environments is increasing (sometimes rapidly) due to many factors indicative of modern IT:

• Adoption of Agile and DevOps Practices: Agile methodologies and DevOps practices emphasize continuous integration and continuous delivery (CI/CD) and rapid iteration. This approach requires multiple environments to manage different stages of the software release process, increasing the need for various non-production environments.
• Microservices and Modular Architectures: The shift towards more componentized software designs means that different teams might be working on different services or components simultaneously. Each team often requires its own development and testing environment to work independently.
• Increased Focus on Testing and Quality Assurance: With software's growing complexity and the importance of user experience, there's an increased emphasis on thorough testing, including automated testing, performance testing, and user acceptance testing (UAT). This necessitates dedicated environments for different types of tests.
• Cloud and Virtualization Technologies: The availability of cloud services and virtualization has made it easier and more cost-effective to spin up new environments as needed. Cloud platforms enable on-demand resource provisioning, allowing organizations to create multiple, scalable non-production environments with ease.
• Regulatory Compliance and Security Testing: Compliance with regulatory standards often requires rigorous testing in environments that mimic production closely but do not contain sensitive data. Additionally, security testing, including penetration testing and vulnerability assessments, requires separate environments to avoid impacting production systems.
• Continuous Feedback and Iteration: Modern software development emphasizes continuous feedback and iteration. Multiple environments allow for parallel development and testing cycles, enabling faster feedback and more frequent releases.
• Remote and Distributed Teams: With the increase in remote work and distributed teams, there is a need for more accessible and isolated development and testing environments to ensure that team members can work effectively from different locations.
• Offshoring and Outsourcing: Many businesses rely on third-party providers for development, testing, troubleshooting, model training and other activities and require local copies of systems and data for their work.
• Complex Integrations: As software systems increasingly integrate with external systems and APIs, additional environments are needed to test these integrations without affecting the production systems.

Put more succinctly, the number of non-production environments in corporate IT departments is increasing due to the adoption of modern software development practices, the ease of creating and managing these environments through cloud and virtualization technologies, and the growing need for thorough testing and compliance with regulatory standards. This trend reflects the ongoing evolution of IT towards more agile, flexible, and quality-focused practices

The Impact of Sensitive Data Sprawl

The implications of sensitive data sprawl are significant and multifaceted. From a security standpoint, sensitive data sprawl amplifies the risk of data breaches and cyberattacks. This is due to the fact that the more dispersed the data is, the more difficult it becomes to implement consistent security measures and monitor all access points. Sensitive data sprawl also complicates compliance with data protection laws like GDPR, HIPAA, or CCPA, as organizations struggle to manage and control data spread across various non-production and production environments. Furthermore, it impedes effective data management and governance, making it hard to ensure data accuracy, prevent data redundancy, and maintain data integrity. 

For businesses, these challenges translate into increased operational complexities, higher costs associated with data management and security, and potential reputational damage due to data mishandling. Therefore, addressing sensitive data sprawl is crucial for organizations to safeguard their data assets and maintain trust with customers and stakeholders.

In upcoming posts, I’ll discuss the failings of common approaches to mitigating the risks of sensitive data sprawl. Then I’ll suggest an alternative approach that combines data masking and database virtualization to both mitigate risks and make it easier for your developers and other innovators to do their jobs.

Todd Tucker

Mar 19, 2024