What is shadow data, and why is it more relevant now?
Shadow data is any data your employees create, store, or share outside of your organization's formal IT environment and management policies. This includes data originating from cloud services, applications, or personal devices that your organization doesn't directly control.
Unlike official data, which you can track and govern through security protocols, shadow data often goes undetected and remains unmanaged, posing data security risks.
Why does the rise of AI and multi-SaaS fuel shadow data accumulation?
The rise of AI-powered tools and multi-SaaS environments enables developers to rapidly generate, share, and store data across various platforms, often bypassing IT oversight. Third-party AI tools used for workflow automation frequently evade controls, leading team members to unknowingly store sensitive data in external systems or personal cloud accounts. These actions create pockets of unclassified, vulnerable, and unmanaged data.
Similarly, multi-SaaS setups fragment organizational data across different cloud services with distinct storage and security protocols. This complexity makes it difficult to consistently track and secure data, creating data governance gaps that fuel shadow data accumulation across environments.
The business impact of unmanaged data sprawl
Shadow data creates sprawl, complicating data governance and compliance efforts, particularly when it involves sensitive customer data. This data sprawl hides unauthorized access points, leading to system or data breaches. Additionally, limited visibility into data origins and the lack of protective measures allow cybercriminals to exploit shadow data to disrupt business operations.
Shadow data also poses significant compliance risks. Regulatory frameworks like GDPR and CCPA require strict oversight of data management practices. Failing to monitor shadow data results in fines and legal consequences. Finally, untracked datasets limit your organization’s ability to make informed, data-driven business decisions.
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Where does shadow data come from?
Shadow data originates from several overlooked or unmanaged sources within your organization. Common sources include:
Unsanctioned SaaS and shadow AI tools: Developers often use SaaS applications or AI tools without their IT team's direct approval. These tools store data in unauthorized cloud services, bypassing data security controls and creating hidden data stores outside your monitoring.
Test or dev databases: Test or development environments generate data on demand, such as database copies, backups, and code forks, which usually remain unmanaged. If developers fail to secure or take ownership of these sensitive data copies, they become a source of shadow data.
Personal devices and mobile or cloud sync leaks: Team members who sync work data to personal devices or cloud services create disconnected data repositories. These untracked repositories often lack the security controls of official data stores, creating security vulnerabilities through unauthorized access or potential data breaches.
Forgotten artifacts, archives, and legacy apps: Unused logs, abandoned cloud storage, and live legacy applications often store data your team no longer needs. This surplus data may contain sensitive information and remain accessible long after assets become obsolete, creating hidden data security risks for your business.
Over time, these unmanaged data sources accumulate into a reservoir of shadow data outside official IT systems. Unlike managed data, which flows through controlled channels, shadow data is harder to track and more prone to exposure. You need to implement robust data governance and security policies that address official data flows and identify where data assets traverse unchecked.
What’s the difference between shadow data and shadow IT?
Shadow data and shadow IT are related but pose different risks requiring different mitigation controls. Understanding their differences helps security teams address both issues effectively.
Compare shadow IT and shadow data side-by-side based on key factors like visibility, risk type, and blind spots:
| Criteria | Shadow data | Shadow IT |
|---|---|---|
| Primary focus | Unmonitored or uncontrolled data | Unauthorized tools and services |
| Ownership | Anyone who generates or stores data outside formal IT systems | Employees or teams that adopt tools without approval |
| Visibility | Low visibility into where data lives and who can access it | Low visibility into which apps and services are in use |
| Risk type | Data exposure, compliance violations, and leaks | Tool sprawl and unapproved integrations |
| Typical blind spots | Backups, test databases, cloud buckets, and personal device syncs | SaaS signups, AI tools, and browser extensions |
For example, a team uses an unapproved analytics SaaS tool to speed up reporting. The SaaS tool itself is shadow IT. When developers upload customer information into the tool, the SaaS platform copies it to its external data stores, creating shadow data. Even if security teams block the tool, the data remains unmanaged and exposed.
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What are the security risks of shadow data?
Shadow data poses significant security risks, including compliance issues, data breaches, and challenges in data security posture management (DSPM). These risks escalate as shadow data continues to proliferate across on-premises and hybrid cloud environments.
Here's a breakdown of the common security risks shadow data poses to organizations:
Regulatory and compliance exposure
Shadow data often contains sensitive information, such as personal or financial data, subject to strict regulatory frameworks. Failing to identify and secure personal information in shadow assets risks non-compliance during audits, leading to fines and legal consequences.
Breach cost, detection, and containment delays
The lack of visibility into shadow data makes it difficult to detect data breaches early. This delays containment, driving up the cost of a breach. Moreover, delays in breach containment lead to financial losses and erode trust among customers and stakeholders.
Lateral movement, supply chain compromise, and data poisoning
Shadow data enables lateral movement within your network by allowing attackers to exploit unmonitored data access points to escalate privileges or compromise other systems. This danger escalates in supply chain attacks, where attackers target third-party systems to access sensitive data.
Data poisoning poses another significant risk. This occurs when threat actors introduce vulnerabilities that compromise downstream systems, leading to system failures, data corruption, or unauthorized access. By enabling security risks like lateral movement and data poisoning, shadow data can spread vulnerabilities across systems, disrupting the supply chain and critical business services.
Loss of visibility, governance, and modern workload risk
The rise of multi-SaaS platforms, AI, and advanced developer tools directly contributes to shadow data. These technologies create blind spots in data inventory and asset flows, complicating effective data tracking and management for security teams. Without centralized visibility, teams lose control over data governance and leave modern workloads vulnerable to security breaches.
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What are the best practices for managing shadow data?
Effectively managing shadow data requires a combination of proactive visibility, access controls, regular audits, and robust data security policies. The following best practices ensure a comprehensive approach:
Maintain visibility and awareness
To gain proper visibility into shadow data, first conduct a baseline scan of all your environments, including your SaaS applications, on-premises systems, development environments, cloud storage, and employee devices. Then leverage data-centric tools, such as DSPM solutions, to automatically discover and classify data across both structured and unstructured storage. Regularly update the visibility scan and perform more detailed reviews to account for changes in your data environment.
Beyond discovery, continuously monitor changes in data access and usage patterns to classify, track, and assess newly created data for sensitivity. Frequent scans can help you catch new instances of shadow data.
This proactive approach keeps shadow data in check and minimizes vulnerabilities that fall outside traditional security controls.
Control data access privileges
Once you have proper visibility, enforce data security best practices like the principle of least privilege to limit data access to actual demand. This minimizes exposure to shadow data and reduces the potential attack surface. Automate access reviews to regularly check dormant accounts and revoke file shares that exceed policy thresholds, keeping unnecessary access to a minimum.
Additionally, by integrating identity and access management logs into DSPM solutions, you can continuously detect over-permissioned roles and unauthorized sharing. Security teams should implement stronger protections, such as multi-factor authentication, encryption at rest, and export restrictions, for sensitive data under strict regulations. These measures prevent unauthorized access and ensure compliance with data protection laws.
Implement regular auditing and monitoring
Set a schedule for auditing and monitoring enterprise data to maintain ongoing visibility into your data security posture. Running automated scans detects new or modified shadow data, while a more detailed quarterly review can uncover data usage, access trends, and compliance violations. Tools like Wiz DSPM simplify scheduling to ensure regular, consistent oversight.
When monitoring shadow data, focus on these key metrics to measure your progress:
Percentage of unclassified data: Track unclassified data volume over time to measure how successfully IT teams categorize data assets. Reducing this percentage improves your ability to identify and secure high-risk data.
Percentage of open shared folders: Monitor shared folder permissions to confirm that only authorized users have access. Keeping this percentage low minimizes the risk of unauthorized access and data leaks.
Percentage of unmanaged apps with data: Identify applications storing sensitive data outside your organization's control. Reducing this percentage to zero ensures all data flows through approved, secure channels.
These metrics clarify your data security status and help you track improvements in managing shadow data.
Employ essential security measures
It’s crucial to implement industry-standard security measures across all data types. With the rise of AI, it’s essential to prioritize AI-related data, including training datasets and cached information in cloud services. Protect this data by enforcing access controls, encrypting data at rest and in transit, and ensuring developers store AI data only in compliant, secure environments. These practices help prevent unauthorized access and minimize the risk of data leaks or breaches.
By maintaining strict controls over AI data, you reduce the risk of shadow data spreading through AI-driven processes and tools.
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How does Wiz help organizations identify and eliminate shadow data?
Wiz delivers comprehensive solutions for identifying and managing shadow data across complex cloud and on-premises environments. Leverage Wiz DSPM capabilities to gain complete visibility, assess shadow data risks, and automate security controls that protect sensitive business information.
Wiz DSPM safeguards your business from shadow data exposure through the following features:
Unified visibility across apps, services, and AI workflows: Wiz delivers unified visibility across all data assets, including multi-cloud, SaaS, and AI workflows. It scans cloud environments and on-premises data stores to discover and classify data by sensitivity, including shadow data. This in-depth data mapping ensures your team manages all data assets properly and enables security teams to implement robust security measures across the entire data landscape.
Contextual risk assessment and prioritization: Our platform provides contextual risk assessments to help your team prioritize security efforts based on data sensitivity and exposure potential. By identifying sensitive data across various environments, our DSPM capabilities allow IT teams to allocate appropriate security resources to protect the most valuable and at-risk data assets. This approach enables more proactive and efficient decision-making to address critical risks first.
Continuous monitoring and compliance automation: Wiz continuously monitors data access and activity patterns, flagging unauthorized data access points and vulnerabilities in real time. It automates compliance checks and vulnerability scans to ensure data governance meets industry standards. With automated insights into your organization's data security posture, our platform helps you detect and mitigate risks from shadow data before they lead to business or compliance issues.
Want to see for yourself how Wiz protects your organization from shadow data? Schedule a demo today to learn how Wiz identifies and secures sensitive data, pinpoints potential exposure risks, and accelerates remediation. For immediate insight into your organization's data exposure, run Wiz’s free Data Risk Assessment to uncover hidden shadow data in your cloud environments.
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