Shadow Data: The Threat You Don’t Know About (But Should)

As digital transformation and cloud migration become more commonplace in all industries, the amount of data businesses must store, process and manage skyrockets worldwide. While external threats like hacking, phishing and ransomware get most of the attention, how companies manage data internally is equally critical for data protection.

You’ve probably heard about Shadow IT, but shadow data may be more insidious. We’ll compare the two later in the article, but let’s define shadow data first.

What is shadow data?

Shadow data refers to any information within an organization that lacks formal approval or oversight. Shadow data can be considered a feature (or is it bug?) of the modern workplace, in which employees use applications, services, or devices for convenience or efficiency that haven’t been approved by IT. Shadow data can stem from personal cloud storage accounts, unofficial collaboration tools, or unsanctioned SaaS applications. The biggest challenge with shadow data is that it’s not typically accounted for in an organization’s security and compliance frameworks, which creates a glaring blind spot in data protection strategies.

Here’s an analogy: think of your organization’s data landscape as a thriving city, where all the official buildings and structures represent approved IT systems and data storage, well-mapped and governed by city planners (your IT team). Think of shadow data as a network of hidden alleyways and underground tunnels created by the city’s residents. While useful for getting around, these unofficial pathways are not on official maps and are not maintained or monitored for safety. Much like how city planners must be aware of these hidden routes to ensure the overall safety and efficiency of the city, organizations must protect and manage shadow data to maintain the security of their entire data infrastructure.

Shadow data challenges and risks

The primary challenge of shadow data is its invisibility to IT administrators, security teams and security protocols. Since it exists outside of approved networks and systems, shadow data can easily bypass security measures put in place to protect sensitive data. While unmonitored data increases the risk of breaches and leaks, it also complicates compliance with data protection regulations like GDPR or HIPAA. This lack of visibility inhibits an organization’s ability to effectively manage its data assets, which leads to inefficiencies and potential data redundancies.

Security risks of shadow data include unauthorized access, data breaches, and potential exfiltration of sensitive information. From a compliance standpoint, shadow data can lead to violations due to inadequate data protection measures.

The additional risk of data loss cannot be overlooked, as data stored in unofficial locations may not be backed up or protected against accidental deletion. These risks can have serious repercussions for an organization, including financial penalties, reputational damage, and operational disruptions.

Comparing Shadow IT with Shadow Data

While closely related (think of them like “black sheep of the family” twins), shadow IT and shadow data differ slightly in scope and impact. Shadow IT refers to the hardware, software, or systems used within an organization without IT approval: unauthorized software, applications, and devices. On the other hand, shadow data is the result of these practices — data generated, stored, and managed through these unapproved technologies.

The key difference between the two is that shadow IT is about tools and technologies, while shadow data is about the information assets these tools create and handle.

While both pose significant security and compliance risks, addressing them requires distinct strategies. Shadow IT solutions are about controlling and monitoring the use of unauthorized technologies, whereas managing shadow data involves identifying and protecting the data itself, regardless of its source or storage location.

Understanding the nuances between shadow IT and shadow data can be crucial for developing effective governance and security strategies.

Managing shadow data with Concentric AI

The TLDR: Concentric AI’s Semantic Intelligence offers a robust approach to protecting data by autonomously detecting and protecting sensitive data across varied environments — including shadow data. Concentric AI continuously monitors and analyzes data activities to help organizations stay ahead of potential risks, ensuring compliance and enhancing data security.

Here’s how we do it.

Autonomous, Semantic-Based Data Discovery

Concentric AI leverages advanced deep learning technologies for language analysis, which helps understand the context and semantics of the data. Our AI goes beyond mere pattern matching or rule-based methods.

Speaking of rule-based methods, they can be cumbersome to maintain and may not identify all potential threats. With our semantic-based approach, organizations get more comprehensive and up-to-date data protection.

Plus, Concentric AI can effortlessly identify business-critical and sensitive data, even if it’s not explicitly labeled or categorized (and in the case of shadow data, authorized). As business data evolves and grows, our solution adapts and learns on the go, ensuring that new types of sensitive data are promptly identified and protected.

Automated Data Risk Identification and Remediation

Concentric AI compares each data element against baseline security practices used by similar datasets, which can identify deviations or anomalies that might indicate a security risk.

What’s important here is that Concentric AI understands the dynamic nature of data threats. Our solution operates in real-time, identifying potential data threats as they emerge. When a potential threat or unauthorized data access is identified, Concentric AI takes proactive measures to remediate the issue — whether that’s changing access permissions, notifying security teams, or other appropriate actions to remediate risk.

Leveraging advanced deep learning technology, Concentric AI offers insights that go beyond traditional rule-based solutions, considering each data element’s unique risk profile.

Effortless Implementation

Concentric AI’s solution is designed for seamless integration. With our agentless solution, there’s no need to install software on individual devices. This means no more compatibility issues and a streamlined deployment process.

Whether data resides in the shadows, in the cloud, on-premises, in structured databases, or unstructured repositories, Concentric AI ensures secure access and monitoring of all data in all locations.

Finally, organizations can say goodbye to cumbersome rule and regex-based systems. Our autonomous capabilities allow for minimal end-user involvement, streamlining the data protection process.

Concentric AI is easy to implement and designed to deliver value quickly. Organizations can see tangible benefits in days, not months, without any significant upfront work.

Want to see how Concentric AI can help you find shadow data in your organization? Book a demo today.