What is Data Sprawl and How to Regain Control

Data sprawl is a major cybersecurity challenge. Driven by cloud adoption, business agility, and more recently AI, sensitive information inevitably scatters beyond traditional perimeters.

For leaders, simply building higher walls is futile as threats exploit this spread. Instead of solely focusing on prevention, we must prepare for the inevitability of a breach and pivot.

This means managing sprawl by protecting the data itself, wherever it lives, fundamentally minimizing impact and securing your organization from the inside out.

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What is Data Sprawl?

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Data sprawl is the rapid and often uncontrolled increase of an organization’s data, which spreads across numerous systems, locations, and storage methods.

This means company information often gets scattered among different cloud platforms, on-site servers, software applications, and even employee devices like laptops or smartphones.

However, as data expands and disperses in this way, organizations will often struggle to know exactly what information they possess, where it’s located, and who has access to it.

To put into perspective how much data is being created, some researchers estimate that 463 exabytes of data will be created each day globally throughout 2025. Over 41% of organizations generate at least 500 petabytes of data.

This widespread data includes various types, both structured and unstructured, and tends to increase as businesses adopt more digital tools, cloud services, and - increasingly - generative artificial intelligence (GenAI) platforms, be it standalone or within existing applications.

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What Causes Data Sprawl?

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Data sprawl frequently arises because the fast pace of business operations and technology adoption often outpaces IT's ability to proactively manage the resulting data.

This dynamic commonly forces cybersecurity leaders or teams into a reactive posture, working to secure and control information after it has already been dispersed across various systems to meet immediate business demands.

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AI and LLM Usage

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Over 20 years ago, the big issue for enterprise cybersecurity was managing BYOD, whereby employees would use their personal laptops, smartphones, and/or tablets at work. This led to company data and information being saved to or loaded on unauthorized devices.

Today, ‘Bring Your Own AI’ (BYOAI) represents a familiar, yet also exponentially bigger and more complex, challenge. To leverage generative AI (GenAI) and large language models (LLM) as much as possible, employees are copying company documents (from emails to reports) and feeding them into AI tools to quickly get summaries, insights, or even original content. 

In fact, a TELUS Digital study found that 68% of enterprise employees using the top GenAI tools (e.g., ChatGPT) at work access them via their personal accounts. 

More than half of them (57%) admitted to entering sensitive information into those tools, from confidential company information to customer information.

It’s worth noting that employees are feeding data to AI in a sincere effort to be more efficient at work, but more often than not, they’re unknowingly sharing confidential data or files.

Moreover, they’re often sharing that information without knowing the potential ramifications. For example, if they’re working with unauthorized tools, they could be transferring ownership of the company’s PII or IP to a third-party vendor. 

Hence, the data sprawl problem is moving beyond the problem of creating new and unmanaged copies of sensitive data without oversight, but complex legal waters as well. 

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Application Development and Testing

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Creating and testing new software also contributes to data sprawl. Development teams require realistic data at scale to ensure their applications work correctly. 

They often request data that closely mirrors live customer information. However, since giving direct access to production systems is risky, companies resort to making copies or replicas of that data for testing purposes. 

But this process leads to more data spreading to different environments and, potentially, with limited controls to ensure only authorized people see it.

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Hybrid and Multi-Cloud Environments

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Many organizations adopt cloud strategies, often combining their own private servers with public cloud services, or multi-cloud strategies (i.e., using services from several different cloud service providers like AWS or Azure). 

While this offers flexibility, it often involves replicating data across all these different on-premise and cloud systems to support various projects. This scattering of data across multiple platforms makes it harder to manage and secure, as information ends up in many different locations.

The complexity of data sprawl increases because different cloud environments often operate independently without built-in connections. 

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General Business Needs

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In addition to the above, general business operations also drive data sprawl. The sheer volume of sensitive data generated today – from sources like social media, online transactions, Internet-of-Things (IoT) devices, etc – is enormous. 

Moreover, remote and hybrid work arrangements also contribute to data sprawl, especially as employees use various collaboration tools and personal devices. This causes data to scatter across more locations and systems.

Sometimes, departments adopt new software or cloud services without IT’s approval. This is known as “Shadow IT,” which creates hidden pockets of data.

Simple data duplication, be it accidental or intentional for backup or sharing, can also lead to unmonitored copies accumulating across the organization.

Finally, a lack of clear rules or a unified strategy for managing the organization’s data storage further allows information to fragment and spread uncontrollably.

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Why is Data Sprawl a Problem?

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Data sprawl presents significant problems, stretching traditional security approaches to their limits as they struggle to keep up with the sheer scale and dispersed nature of data.

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Disparate Policies Increase Security Risks

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When company data is spread across many different systems, cloud platforms, and employee devices, it becomes much harder to protect consistently. Each location where data resides can become a potential weak point. 

This fragmentation increases the overall attack surface, meaning there are more potential entry points for bad actors. Implementing and maintaining uniform security measures like encryption and access controls across these different storage locations is very complex and challenging. 

This difficulty in applying consistent security policies everywhere weakens the organization's overall defense against threats.

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Difficulty in Meeting Compliance Requirements

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Data sprawl also significantly complicates an organization’s ability to meet its regulatory and compliance obligations. 

Regulations like GDPR, HIPAA, CCPA, PCI DSS, and others require strict control over sensitive data, but sprawl makes it difficult to locate, classify, track, and manage this data effectively. As a result, the risk of compliance gaps increases, resulting in potential fines and penalties.

Moreover, the regulatory landscape is also getting more complicated, especially with data sovereignty, the ‘right to be forgotten,’ and other factors coming into play. 

Keeping pace with such changes while also dealing with data sprawl can add overwhelming technical and administrative challenges for cybersecurity teams and leaders. 

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Data Leakage or Exposure

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The scattered nature of data due to sprawl raises the risk of sensitive information being leaked or exposed.

When data ends up in places it shouldn't, like personal cloud drives, unapproved applications, or development environments using copies of real customer information, the chances of accidental leaks or unauthorized access increase significantly.

This can happen through misconfigurations, giving too much access to users, or simply poor data management practices. Without proper control, sensitive information like customer details (PII) or company secrets (IP) could be exposed, leading to serious consequences.

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Lack of Visibility and Control

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Data sprawl makes it incredibly difficult for organizations to have a clear picture of all the data they possess and where it is stored.

This lack of visibility means companies struggle to know what sensitive information exists, who has access to it, and whether it's being used appropriately.

Finding unknown copies of data hidden away in various systems is a major hurdle. And without a comprehensive view, managing access rights effectively becomes nearly impossible.

Furthermore, this obscurity makes it challenging to enforce data governance policies, ensure compliance with regulations, and even leverage data effectively for business decisions.

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Stretches Traditional Security Strategies to Their Limit

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Traditional cybersecurity approaches – i.e., Data Security Posture Management (DSPM) and Data Loss Protection (DLP) – aim to control data movement. However, this approach wasn’t designed to work in the dynamic, multi-cloud environments of today. 

DSPM and DLP are predicated on the assumption of having total control. This made sense in centralized environments where the IT department gatekept the introduction and management of hardware, software, and services. It was easier to map and control the flow of data.

Today, however, enforcing that level of control is unrealistic. Data rapidly replicates across SaaS apps, shadow IT, and hybrid clouds faster than security teams can track. Data also moves very rapidly across collaborators, third parties, and AI services, hence bypassing security checks. 

The assumptions that drove DSPM and DLP are no longer true today. Current environments are dynamic and, in turn, create new blind spots. Organizations don’t know where their data is, and they can’t realistically control its movement.

Thus, the focus needs to shift away from trying to control the flow of data to, instead, securing the data directly so that sprawl doesn’t necessarily lead to other problems. 

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How to Manage and Limit Data Sprawl

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Tackling data sprawl isn't just about cleanup; it's about fundamentally changing how you secure your organization from the inside out.

As a cybersecurity leader, you know that trying to secure the perimeter isn’t feasible, especially not today. With each passing day, an employee somewhere in your company is deciding that a new AI tool is the next great thing, and they’ll give themselves the go-ahead to feed it sensitive data, like internal documentation or contracts. You will not know about it.

Thus, as you try building higher and thicker walls, these people will keep drilling tiny holes in it. 

So, do you want to spend your time and energy trying to plug these holes, only for another one to come up a short while later? 

Or, instead, do you want to lessen the impact of a data breach so that nothing confidential or of high value gets lost or stolen, regardless of where it ends up?

Managing sprawl means accepting its existence and implementing proactive, dynamic controls to protect data regardless of where it ends up. 

This shifts the focus from solely building higher walls to ensuring that even when data inevitably spreads, its core value remains protected.

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1. Acknowledge the Existing Sprawl

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The essential first step is accepting that data sprawl is likely already happening within your organization. It's a natural consequence of rapid growth, cloud adoption, remote work, and the drive for innovation.

Instead of seeking blame, focus on acknowledging this reality. 

Recognize that data has likely been copied and moved for legitimate business reasons, perhaps outpacing established governance. This acceptance paves the way for realistic assessment and strategy.

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2. Data Discovery

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Once data sprawl is acknowledged, the next phase is comprehensive data discovery. 

This isn't just about confirming data in known locations; the real goal is to uncover the unknown repositories. Where have copies of sensitive production data landed? Who created them? Why? 

Automated data discovery and classification tools are vital for scanning across on-premises systems, cloud environments, and SaaS apps to identify and categorize sensitive information based on risk and type.

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3. Develop a Strategy

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Armed with insights from discovery, you can develop a targeted strategy. 

This stage involves understanding the business drivers behind the sprawl – why was data copied or moved in the first place?

Your strategy should outline clear data governance policies covering approved storage locations, access controls, data retention, and secure disposal aligned with compliance needs like GDPR or HIPAA.

Establishing data lifecycle management practices helps prevent future sprawl by defining how data is handled from creation to deletion.

Consider centralizing data storage where feasible, perhaps using cloud platforms, to create a more unified view and single source of truth.

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4. Implement Solutions

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With a strategy in place, the focus shifts to implementing technical solutions that actively protect data. This moves beyond simply understanding your security posture (a core function of DSPM) towards embedding security directly into the data itself, offering more robust protection.

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Dynamic Data Masking and Data Tokenization

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These techniques protect sensitive data elements while often preserving the data's usability for specific tasks.

Data tokenization replaces sensitive data with non-sensitive placeholder values called tokens. The original data element and the token (i.e., the key-value pair) are stored in a separate, highly protected vault, reducing risk significantly because even if the tokenized dataset is breached or stolen, the tokens themselves have no exploitable value. 

Moreover, tokenization also protects you from the risk of “harvest now, decrypt later” strategies, where attackers would steal encrypted data with the intent of using quantum computing or other future tools to decrypt at a later time. 

Both methods allow business processes to continue using formats that look real but contain no sensitive information. 

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Test Data Management (TDM) / Synthetic Data Generation

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One’s application development and testing shouldn’t require access to or use of sensitive data at all; synthetic data generation solves the problem completely.

Development teams require realistic data, but copying sensitive production information creates significant risk and sprawl. 

Instead of risky duplication, synthetic data solutions offer developers high-fidelity, referentially intact test data that behaves like real data but contains no sensitive information.

Modern TDM approaches can create this safe test data efficiently, sometimes even in real-time, eliminating risky data duplication and latency.

By providing development and testing teams with the realistic data they need through safe methods like static masking or synthetic generation, you enable them to test thoroughly and confidently without compromising security or contributing to further data sprawl.

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Replicate Protected Data

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Sometimes, data replication across different environments is a business necessity. If data needs to be copied, ensure you replicate data that has already been protected through tokenization.

This way, even if a replicated data store in a less secure environment is compromised, it doesn't expose sensitive customer details or intellectual property, since the data within has already been de-identified. This is a proactive approach as it secures the data before it moves.

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Ready to Tackle Data Sprawl? 

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Start today by:

1. Initiating conversations with key teams and stakeholders (e.g., the development team, GenAI users, etc) to understand why they’re moving data.
   
   
2. Quickly review your data policies for gaps that are enabling uncontrolled data sprawl.
   
   
3. Proactively get to the heart of your fear – i.e., a data breach – by securing the data itself, regardless of where it lives or ends up. Reach out to DataStealth to see how you can secure your data with our data tokenization, dynamic data masking, and test data management.

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