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What Is Shadow Data and How Can You Minimize the Damage?

You cannot rest assured that your organization’s data is hidden from prying eyes even after implementing the latest data security solutions. Threat actors can target shadow data in your company to cause data breaches, wreaking havoc on your company’s reputation and financials.

But what exactly is shadow data, and how can you minimize its risks? Let’s find out.

What Is Shadow Data?

Shadow data (also known as a “data shadow”) refers to data that is not visible to you or your organization’s centralized data management framework.

Organizations use various data security solutions to discover, classify, and protect data. Shadow data, being outside the view of tools you use to monitor and log data access, poses many severe compliance and security issues.

Examples of shadow data include:

  • Development teams often use real customer data for testing, which can be risky, as improper security can lead to leaks or misuse.
  • A company may have old software it doesn’t use anymore, possibly holding important data that is left unmanaged (and therefore an exposure risk).
  • Apps create log files that can contain sensitive information that could be exposed if left unmonitored or unchecked.
  • Companies often use third-party services for different tasks, and sharing data with these services can be risky if they don’t have strong security measures.

So, let’s discuss the ways in which shadow data is different from shadow IT.

How Is Shadow Data Different From Shadow IT?

Shadow IT refers to unauthorized hardware and software used within an organization. This could be an employee using a non-approved messaging app or a project team using third-party software without the knowledge of your IT department.

Shadow data, on the other hand, is data that is not visible to your data security tools or data that is outside your company’s data security policy.

As your IT team doesn’t know what shadow IT is, the data processed on unauthorized hardware and software will be unknown to your data security solutions. As a result, information saved or shared on the shadow IT becomes shadow data.

So, if an employee saves company files in personal cloud storage, that’s shadow data.

While both pose risks, the nature of such risks varies. Shadow IT exposes the organization to potential network vulnerabilities and compliance issues. Shadow data specifically risks unauthorized access to sensitive files and information.

Shadow IT is the vehicle for the risk, while shadow data is the actual payload that could be compromised.

How Is Shadow Data Different From Dark Data?

Dark data is information your company gathers during normal business operations but isn’t used for other purposes. A business will keep such information for legal reasons, and it’s stored across different departments. This idle data could be a security risk.

Examples of dark data can include information about your past employees, internal presentations, old customer surveys, email archives, etc.

The main difference between dark data and shadow data is that your company generates dark data within your company’s IT infrastructure during regular business operations. You don’t use this data for other purposes. And you may consider it outdated, redundant, or insufficient to be valuable over time.

By contrast, shadow data is created in two ways:

  • Purposely generated by shadow IT outside your IT infrastructure.
  • Unknowingly caused by your company’s over-sharing.

Dark data can be a subset of shadow data. For example, irrelevant output from an application is both dark data and shadow data.

How Does Shadow Data Occur?

There are some key reasons why shadow data crops up.

Firstly, your DevOps team, under pressure to work fast, may skip security steps. This can lead to shadow data risks. The team might quickly activate and deactivate cloud instances, leaving unnoticed data that IT or data protection teams are unaware of.

Secondly, the rise of remote work culture has fueled the use of specialized tools for tasks like communication and screen sharing. Your employees may use third-party services for these, unknowingly creating shadow data.

On top of this, shadow IT involves the use of unauthorized tech tools by employees. When they store or share data using these tools, it becomes shadow data, existing outside your company’s approved systems and oversight.

If your company works in multi-cloud environments, monitoring data effectively in different cloud environments can be challenging. This can also lead to shadow data accumulation.

Lastly, your employees may save sensitive files on their hard drives or personal cloud data storage (like Google Drive or OneDrive) accounts without authorization, keeping these files outside your data management system.

How to Minimize Shadow Data Risks

An Index Finger Pointing at a Padlock

The occurrence of shadow data cannot be stopped entirely, as it is often the byproduct of an organization’s regular operations.

However, the following methods can mitigate the security risks shadow data poses to your company.

1. Detect and Protect Your Data

Your security and compliance teams must check all data repositories, data lakes, cloud-managed environments, and SaaS (Software as a Service) applications that may have valuable data.

Once you have identified the data in all your data depositories, you need to classify data to implement the proper security controls. When discovering and classifying your data, ensure you can include semi-structured and unstructured data in the data security management system besides structured data.

Ideally, you should use a tool that can roll your data repositories into a single source and provide you with dashboard access. This will help you quickly detect anomalous behavior.

It also helps to limit data permissions and access to avoid shadow data falling into the wrong hands. Only necessary staff should have access to certain information, especially that which is of a sensitive nature. Enabling access barriers ensures that only the required individuals can see or use certain data.

2. Manage Shadow IT Occurrence and Accumulation

Mac Laptop Placed Beside Mac PCs

Managing shadow IT effectively can reduce the risks associated with shadow data. When you have control over the software and platforms in use, it’s easier to safeguard the data within those systems.

Providing your employees with the right tools to do their jobs efficiently, simplifying the vetting and approval process for adopting a new tech tool, and making your employees aware of shadow IT risks can help you manage shadow IT.

As a result, you can control the volume of shadow data generated by shadow IT in your company.

3. Implement Security-First Policies

Ensure cybersecurity is a fundamental component of your company’s software development lifecycle (SDLC). Compliance and security teams should have complete visibility of DevOps and developers’ actions in relation to data.

The right security and compliance rules in place from the beginning of SDLC can help minimize the volume of shadow data created by DevOps teams and developers.

Also, you should make policies to delete shadow data regularly.

4. Train Your Employees

Your employees are the first defense against any shadow data or cybersecurity risks. Consider creating a solid cybersecurity employee training program to educate your employees about shadow data risks and how they can avoid creating shadow data.

Also, ensure that cybersecurity programs are not an annual affair in your company. Try planning multiple small training sessions throughout the year, covering how to identify shadow data, store data securely, and protect sensitive data assets.

Shadow Data Is a Big Security Risk

Minimizing the risks associated with shadow data is crucial for safeguarding sensitive information. Data outside the company’s control is vulnerable to unauthorized access, data breaches, and leaks. This can lead to legal consequences, reputational damage, and the loss of customer trust.

Therefore, managing shadow data is vital for overall cybersecurity.

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