Shadow data

Key takeaways

• Shadow data refers to data that exists outside an organization's secured perimeter and is exposed with minimal or no security controls.
• Poor permission hygiene, reckless data migration, and shadow IT are key contributors to the creation of shadow data.
• Shadow data and shadow IT are closely related, with shadow IT acting as one of the primary sources through which shadow data is generated.
• Shadow data introduces significant risks, including data inconsistencies, exposure, and regulatory compliance violations.
• Many data breaches originate from shadow data due to the absence of proper security measures, and this issue affects organizations of all sizes.
• Mitigating shadow data begins with awareness and visibility into what data organizations hold, how it is used, and how it is shared or transferred.

What is shadow data?

Shadow data refers to information that has fallen outside an organization's visibility and exists outside your network perimeter. As organizational security policies do not extend to this data, the risk of accidental exposure increases significantly, making it easy prey for attackers. As the adage goes: You can’t protect what you don’t know. Being unaware of shadow data presence leaves your organization vulnerable to cyberattacks. In the following sections, you’ll learn what gives rise to shadow data, how it can be exploited, and the steps you can take to minimize this risk.

What gives rise to shadow data?

The risks of shadow data are amplified by limited visibility into where data resides, how it is created, and who has access to it. Common sources include:

• Uncontrolled data exports: Employees often export sensitive data from core systems into spreadsheets, local files, or shared folders to perform ad hoc analysis or reporting. When these copies are not governed or tracked, they quickly become shadow data.
• Business intelligence and analytics extracts: As organizations increasingly rely on analytics, sensitive data may be exported to ungoverned BI tools or spreadsheets. Without proper oversight, this data can proliferate outside controlled systems and becomes shadow data.
• Data migration to cloud applications: The shift from on-premises infrastructure to cloud platforms is a key part of digital transformation. During migration, administrators may overlook decommissioning legacy storage, leaving residual data behind. This leftover data in unmanaged environments constitutes shadow data.
• Shadow IT: Employees frequently adopt cloud applications outside approved IT policies to increase productivity. The use of these unsanctioned tools can move sensitive data to uncontrolled environments, increasing the risk of shadow data.

Shadow data vs. shadow IT

Shadow IT refers to the use of unsanctioned applications or services outside the oversight of IT and security teams, where organizational data is accessed, processed, or stored. Shadow data, in contrast, refers to the data itself when it exists outside the organization’s governed and monitored environment.

In simple terms, shadow IT is the enabler and shadow data is the outcome. While they are often discussed as distinct concepts, in practice they are closely interconnected. Shadow IT often creates the conditions for shadow data to emerge.

The 2023 incident involving a Samsung employee's use of ChatGPTs highlights the link between shadow IT and shadow data:

1. Use of shadow IT: An employee used ChatGPT, a GenAI tool, without formal organizational approval or governance.
2. Data leaves the perimeter: During routine work, the employee entered sensitive internal information into the platform, causing data to leave Samsung’s controlled environment.
3. Creation of shadow data: Once a copy of this information existed in an unmanaged third-party system, it became shadow data, outside established security, compliance, and retention controls.

Shadow data risks

A lack of complete oversight over organization data, with limited visibility into where it resides, or how it is being managed creates significant security risks. A few of which include:

• Data fragmentation: Shadow data encourages decentralized and inconsistent storage practices where employee convenience outweighs standard data storage and management protocols. This leads to operational inefficiencies, data duplication, and skewed analytics, resulting in unreliable business insights.
• Data breach and leaks: Shadow data is often inadequately protected or entirely unmanaged because it exists outside approved systems and security controls. This increases the risk of accidental exposure, insider misuse, and exploitation by external threat actors.
• Compliance violation: Regulations such as GDPR and HIPAA require organizations to maintain visibility into where sensitive data is stored and how it is accessed. Shadow data undermines this visibility, increasing the likelihood of non-compliance, regulatory penalties, and significant financial and reputational damage.

Shadow data incidents

One in every three data breaches in the past year stemmed from shadow data. This shows that as the avenues in which data is stored increase, so does the risk of data breaches. The following incidents highlight how shadow data has contributed to major security failures:

• Toyota's shadow data ordeal: In 2022, Toyota disclosed that a database access key for its T-Connect service had been inadvertently exposed by a subcontractor. For nearly five years, this key remained in a public GitHub repository, providing a unauthorized access to email addresses and management numbers of 296,019 users. It represents a classic case of shadow data because this ghost repository existed entirely outside Toyota’s IT governance and monitoring.
• Nissan's shadow data roadblock: In 2022, Nissan North America disclosed that a third-party service provider had exposed the personal information of approximately 18,000 customers. This incident illustrates the test-data trap in which sensitive production data, including vehicle financing account numbers and dates of birth, was extracted for software testing but stored in a public cloud repository without access controls. As this repository was created outside Nissan’s central IT governance, it became shadow data, housing highly sensitive information living in an unmanaged blind spot that was completely invisible to corporate security monitoring.
• Roblox's shadow data mishap: In 2023, it was revealed that a sensitive data set from the Roblox Developer Conference was leaked, affecting roughly 4,000 creators who attended events between 2017 and 2022. This incident represents stale shadow data, where a data graveyard of names, emails, and home addresses was maintained on an unmanaged vendor system long after the data was needed. This archive sat in the shadows of the supply chain for years, therefore the leak went undetected until the data was found circulating on a hacking forum in 2023.

How to mitigate the threat of shadow data

A holistic approach to curbing shadow data involves managing data throughout its life cycle and in each of its states, including data at rest, data in use, and data in motion. Here's how you could do that:

• Gain visibility into shadow data: Mitigating shadow data starts with knowing where it resides. Continuous data discovery across your storage environment maintains an up-to-date record of your data and tracks its movement into unmanaged locations such as cloud storage, databases, and forgotten backups. By classifying data at rest, organizations can identify redundant or orphaned data and enforce appropriate controls or securely remove it before it is accessed or misused.
• Prevent shadow data creation: Shadow data is often created during everyday work. Employees may export reports, download sensitive files, or paste information into unapproved tools. Monitoring data in use enables organizations to enforce usage controls, such as restricted exports and governed access, preventing unmanaged copies while allowing users to work efficiently.
• Regulate data transfers: Sensitive data frequently leaves approved environments through email, cloud uploads, and removable media such as USB devices. Monitoring data in motion allows organizations to detect and restrict unauthorized transfers, enforce sharing policies, and maintain control over how data moves beyond trusted systems.

Best practices for managing shadow data

As cloud adoption accelerates and users increasingly migrate to cloud applications, shadow data production is bound to increase. However, following these best practices can help

• Discover and classify sensitive data: Adopt a data-centric security approach by identifying where sensitive data resides across cloud services, endpoints, and on-prem repositories. By classifying data into categories such as Public, Internal, Confidential, and Restricted, organizations can enforce consistent security policies regardless of where the data resides.
• Implement and review access controls: Apply the principle of least privilege and routinely audit permissions to prevent excessive or unnecessary access, a common driver of shadow data proliferation.
• Continuously monitor user activity: Monitor data access patterns, downloads, and exports to identify anomalous behavior that may indicate unauthorized copying or the creation of unmanaged data stores.
• Maintain an allow-list of applications: Define and enforce approved applications for handling corporate data. This reduces shadow IT and prevents sensitive information from being uploaded to unsanctioned or unmanaged SaaS platforms.
• Prevent shadow copies through controlled sharing: Monitor and enforce data loss prevention policies across email and cloud-based sharing channels to stop sensitive data from being distributed beyond approved environments.
• Enforce encryption: Protect sensitive data at rest and in transit through encryption, ensuring data remains protected even when stored or transmitted outside primary systems.
• Educate employees: Promote secure data-handling practices by training users on approved tools and the risks associated with convenience-driven workarounds that lead to shadow data.
• Establish incident response processes: Define clear procedures to identify, remediate, and eliminate shadow data while addressing causes to prevent recurrence.

Frequently Asked Questions