Why Common Approaches for Mitigating Sensitive Data Sprawl Fail

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. 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 biggest driver of sensitive data sprawl 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. The number of non-production environments in corporate IT departments is increasing due to the ongoing evolution of IT towards more agile, flexible, and quality-focused practices.

Growing numbers of non-production environments in corporate IT departments, however, also results in sensitive data sprawl multiplying across organizations as a whole. The implications are vast and multifaceted— increased sensitive data sprawl amplifies the risk of data breaches and cyberattacks, complicates compliance with data privacy laws like GDPR, HIPAA, and CCPA, and it impedes effective data management and governance. For businesses, these challenges produce increased operational complexities, higher data management and security costs, and potential reputational damage due to data mishandling. 

The accumulating challenges that sensitive data sprawl presents to IT departments and organizations as a whole makes it crucial for these organizations to address sensitive data sprawl. Organizations employ a variety of measures to mitigate the risk of sensitive data sprawl, but it’s just as important to recognize that some of these common approaches are inadequate in fully addressing the problem as a whole.

Common Mitigation Approaches

Mitigating the risks of sensitive data sprawl commonly involves a multifaceted approach, combining technological solutions with robust policies and employee awareness. Following are several common strategies, broken into two categories.

Governance Measures

• Data Discovery and Classification: The first step in mitigating data sprawl is identifying where sensitive data resides. This involves using data discovery tools to scan, locate, and classify data across the organization's networks, devices, and cloud services. Data classification labels data based on its sensitivity and helps in applying appropriate controls.

• Data Governance Policies: Establishing clear data governance policies defines what constitutes sensitive data, how it should be handled, stored, and shared, and the procedures for data deletion or archival. Ensuring these policies are understood and followed across the organization is key.

• Employee Training and Awareness: Educating employees about the importance of data security and the risks associated with data sprawl helps them understand the best practices for handling sensitive data.

• Regular Audits and Compliance Checks: Conducting regular audits helps identify any deviations from data governance policies and regulatory requirements and allows corrective actions to be taken.

• Vendor Risk Management: Evaluate the security practices of third-party vendors who have access to sensitive data. Implementing vendor risk management processes ensures that vendors comply with your organization’s data security standards.

Technical Measures

• Access Controls and Authorization: Strict access control measures, such as role-based access controls (RBAC) and regularly reviewing access permissions, help ensure that only authorized personnel have access to sensitive data.

• Data Masking: Also known as anonymization, redaction, tokenization and other terms, data masking replaces sensitive details with fictitious or otherwise non-sensitive values.

• Encryption: Encryption acts as a last line of defense, ensuring that even if data is accessed unauthorizedly, it remains unintelligible and secure. Oftentimes, sensitive data is encrypted both at rest and in transit. Compare data masking vs. encryption.

• Data Loss Prevention (DLP) Tools: Utilizing DLP tools to monitor and control data transfer can help prevent the unauthorized sharing of sensitive information.

• Secure Data Storage Solutions: Implementing and mandating the use of storage solutions helps ensure the protection of sensitive data.

• Endpoint Security: Securing all endpoints—including laptops, mobile devices, and desktop computers—is key because they are often used to access and process sensitive data.

The 2024 State of Data Compliance and Security Report

75% of organizations say that sensitive data volume in non-production has increased. 91% are concerned about the expanded exposure footprint. Find out what you can do about it when you get the insights from 250 global leaders in this report.

Get the Data Compliance Report >>

The Fatal Flaw: Scalability

Many of the aforementioned approaches to sensitive data risk mitigation fail for one simple reason: they must be scaled to handle the amount of sensitive data. Indeed, most measures mitigate the risks created by sprawl but do little to prevent it in the first place. Therefore, the level of security investments must increase with the proliferation of sensitive data. 

To illustrate, consider a few examples:

• Access Controls and Authorization: As sensitive data ends up in more places, the demand for access controls and authorization grows. This results in a growing administrative burden, and it often increases the direct costs of protections such as privileged user management tools, encryption software and more.

• Regular Audits and Compliance Checks: Compliance assessments can be automated, but they still require skilled labor to interpret the results and mitigate exceptions. As sensitive data finds its way into more places, more assessments must be performed and communicated and their exceptions resolved.

• Secure Data Storage Solutions: Data storage remains very expensive. As sensitive data sprawls, it consumes more storage that, in turn, must be protected. 

• Endpoint Security: Endpoints are expensive to protect, especially when they hold sensitive data. Regulations like GDPR, HIPAA, or CCPA set high standards for data protection, which may require advanced endpoint security solutions that are more costly.

• Vendor Risk Management: As more third parties have access to sensitive data, more steps must be taken to mitigate vendor-related data risks. Third-party and/or offshore development teams and QA testers rarely have a legitimate need for sensitive data but are often granted access, requiring a higher level of risk management.

Since security budgets and skilled resources are in tight supply, any risk mitigation approach that does not scale isn’t a sustainable solution for sensitive data sprawl. Businesses must find another way that cost-effectively addresses the risks of sprawl.

In an upcoming post, I’ll detail an alternative approach to the methods above: the compliant data layer. The compliant data layer combines data masking and database virtualization to both stop the sprawl of sensitive data and make it easier for your developers and other innovators to do their jobs. I’ll also highlight testimonials from technology leaders in various industries who are using the compliant data layer approach within their respective organizations.