Understanding shadow data

Imagine a scenario where an employee, for convenience, copies sensitive company data to a personal, less secure, cloud-based spreadsheet. Or another scenario where customer data gets copied into a dev environment from production to be used as test data, but the duplicated data gets forgotten and is either never erased or is backed up to a less secure location.

In these scenarios, the data was more secure in its original location, and never intended to be copied – or at least not copied and forgotten. These actions may seem harmless, but they introduce substantial security risks, transforming this data into what is known as shadow data.

This concept of shadow data — data managed outside of your secure organizational controls — is a critical cybersecurity concern. In this post, we’ll look more closely into what shadow data is, the risks it poses, and what your organization can do to rein it in.

What is shadow data?

Put simply, shadow data is any created, stored, or shared data that exists outside the centralized and secured data management framework.

Shadow data may show up on personal devices, often due to employees transferring information for convenience. In other cases, shadow data may wind up in cloud storage, on platforms like Amazon S3 without proper security measures, or in overlooked data tables within a database.

Where does shadow data come from?

Shadow data may happen inadvertently or on purpose:

• Decommissioned legacy applications: When historical customer data is migrated to a new application, it is frequently left dormant in its original storage location, lingering until a decision is made to delete it - or not. The dormant data may persist for a very long time, and in doing so, it becomes increasingly invisible and vulnerable to exploitation.
• Business intelligence and analysis: Data scientists and business analysts make copies of production data to mine it for trends and new revenue opportunities. They may test historic data, often housed in backups or data warehouses, to validate new business concepts and develop target opportunities. This shadow data may not be removed or properly secured once the analysis is completed so it becomes vulnerable to misuse or leakage.
• Migration of data to SaaS: Employees frequently adopt software as a service (SaaS) solutions without formal approval from their IT departments, leading to a decentralized and unmonitored deployment of applications.
• Human error: Persistence of customer data in development environments is the classic example of human error causing shadow data due to it being forgotten or not properly backed up. This also includes employees who might store sensitive work documents or data in insecure personal devices.

Shadow data as an analog to shadow IT

Shadow data and shadow IT are closely related concepts in the realm of information security, as they both stem from the use of unauthorized technology within an organization. Shadow IT involves employees using unapproved software, devices, or services to perform work-related tasks. Often, this can lead to the generation of data that is stored or processed in ways not sanctioned or monitored by the organization's IT department — hence, the connection between shadow IT and shadow data. However, it is still possible for shadow data to be created without the use of unauthorized hardware, software, or cloud services.

The relationship between shadow IT and shadow data highlights the complexities of modern IT environments. Today’s ease of access to technology can compromise security, exacerbating the risks of data leaks and compliance breaches.

The risks of shadow data

Shadow data can pose significant risks to your organization. Managing these risks is crucial to safeguarding sensitive information and maintaining organizational integrity.

Data breaches

Typically, shadow data lurks undetected, leaving it inadequately protected. This makes it an easy target for cyber threats. And any unauthorized access to data — even shadow data — can lead to data breaches. The result may be the exposure of sensitive customer information, trade secrets, or internal communications.

Compliance and legal implications

Storing data in unsanctioned environments can lead to noncompliance with regulations such as the GDPR, HIPAA, or other data protection laws. If your organization violates these laws, it may result in hefty fines, legal disputes, and severe damage to your business reputation.

Operational risks

Because shadow data is unmanaged data, this can contribute to inaccurate data analysis and lead to flawed business decisions. In addition, the proliferation of untracked and uncontrolled data increases the complexity and cost of IT management, straining resources and potentially hindering operational efficiency.

Managing shadow data

Effectively managing shadow data is critical for minimizing your risks and ensuring data integrity across your organization. A structured approach to data management involves layering the following strategies.

Detection techniques

The first step in managing shadow data is identifying it. Runtime capabilities allow your team to truly know in real-time what data is moving where.

Additionally, by using auditing and monitoring tools, you can automate the discovery of all data used throughout your organization. This process should also include classification of both structured and unstructured data to determine the sensitivity of data and the potential impact to your organization in the event of its exposure. Once you have identified your shadow data, you can begin to manage and govern it.

Prevention strategies

Preventing the creation and propagation of shadow data begins with establishing security policies that govern how data will be used in your organization. Then, you can utilize tools with runtime capabilities to continuously monitor data flows and enforce those policies. By using tools that help you craft and enforce dynamic policies, you can readily adapt to new threats and changing data access patterns.

In addition, lean on anomaly detection systems to help you proactively identify irregular patterns or behaviors that could indicate a data policy violation or breach.

Mitigation approaches

Ensure that the data governance frameworks in your organization incorporate shadow data considerations to address and mitigate risks. This includes considering data loss prevention (DLP) tools and enhancing cloud data security measures. Focus on seeing the holistic movement of data between apps, data stores, and endpoints. With this view in mind, you can implement automated and proactive prevention measures to maintain secure data operations.

Conclusion

Managing shadow data effectively is essential for maintaining the security and integrity of your organization's data infrastructure. In this post, we’ve looked at the origins and risks of shadow data as well as best practices for managing it. By keeping these concepts in mind, your organization can enhance its cybersecurity measures and ensure compliance with various regulatory standards.A critical component to safeguard your data in cloud environments is DSPM, as it provides a comprehensive framework for classifying, analyzing, and protecting such data across dynamic cloud environments. CrowdStrike Falcon^® Cloud Security brings runtime capabilities into DSPM, which provides an additional context layer that makes it easier to prioritize risks and reduce alert fatigue. This enables organizations to safeguard their data across deployments by responding to threats in real-time.

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