Shadow IT vs. Shadow AI: Understanding the Key Differences

What Is Shadow IT?

シャドーIT is a broad category that describes any unauthorized technology, hardware, endpoint devices, or cloud applications that an employee uses. Whether it’s connecting a non-company laptop to business networks or simply using a SaaS app without permission, everything falls into the category of shadow IT.

Often, people may not even realize they’re bringing shadow IT into an organization. Using shadow solutions doesn’t necessarily have to be malicious, as employees might not know a particular app or device isn’t registered with the company. However, even when done innocently, shadow IT expands a company’s attack surface, creating numerous new points of entry and potential vulnerabilities that engineers don’t know they need to monitor.

When shadow IT scales, it can create a major issue for engineers, impacting system visibility and making an organization’s overall security posture more challenging to manage.

Real-World Examples of Shadow IT

Shadow IT is far from a theoretical problem, being a common occurrence that most security engineers will come across. Part of the reason shadow IT is so common is that it reaches across different technologies. 

Here are some broad examples of shadow IT:

• Shadow Applications: If an employee downloads a task-managing platform or a browser extension that tracks their performance without admin oversight, they’re engaging in shadow IT.
• Shadow Devices: Personal laptops, phones, tablets, or IoT devices that connect to company networks without proper authentication and authorization count as shadow devices. 
• Shadow Infrastructure: If individuals provision additional virtual machines or unapproved cloud instances, they create shadow infrastructure that admins may not be aware of.
• Shadow AI: As we’ll discuss shortly, shadow AI is the use of any AI assistants or tools without permission from IT admins.

Any IT solution, whether it’s physical, in the cloud, or software-based, falls under the shadow IT umbrella. Due to this, it’s a continual uphill battle for security engineers to comb through active presences in business networks and identify which entities are authentic and which are shadow IT.

Risks of Shadow IT

Although not all shadow IT is malicious, every iteration does create problems that businesses will need to address either in the short or long term. The basis of these issues stems from a lack of visibility – if a security team doesn’t know something exists, they can’t apply the proper protections to keep it safe.

Below are the main risks of shadow IT:

• Limited Visibility: As previously suggested, the limited visibility that unapproved devices or software solutions create is the central problem with shadow IT. Unknown entities within a network are nearly impossible to monitor and extremely difficult to apply security regulations to, leading to potential vulnerabilities and weak links in organizational security postures.
• Compliance Issues: Businesses must follow regulatory compliance frameworks to protect company and customer data. Without the ability to see the tools in their business, engineers are unable to apply compliance standards to each platform, creating compliance gaps that could lead to financial penalties if they were to come to light.
• Data Breaches and Exfiltration: Potentially, the most dramatic impact of shadow IT is the arrival of a breach scenario, where one of the shadow IT solutions or devices becomes the source of a breach. Without data loss protection (DLP) strategies in place, businesses may see significant portions of their sensitive data exfiltrated by malicious threats.
• Advanced Persistent Threats: Engineers need to be on the lookout for APTs, using monitoring tools to detect when a malicious threat is lurking within company systems. If teams don’t have visibility into all the components that are active in their business, it becomes more challenging to identify anomalous traffic and mitigate APTs.
• Data Silos: When different departments begin to rely on shadow IT and move away from the centralized solutions your business offers, data silos start to form. When different teams all use distinct software solutions, it becomes much harder to readily share information and ensure everyone has access to the content they need.

What Is Shadow AI?

Shadow AI is a subsection of shadow IT that has emerged alongside the increasing popularity of AI assistants and applications. When an employee uses an AI application or a SaaS product that has AI capabilities from a wrapper, they create a potential vulnerability as company data moves outside the protected walls of the organization.

For example, if a user were to upload an internal spreadsheet to an AI platform to get insights from the file, they would be sharing that data with the third party that provides the AI model. Whether the input is analytical data, source code, or intellectual property, sharing it with these models creates a major privacy and compliance risk.

As more businesses implement AI into their products, shadow AI is becoming a much more prominent subsection of shadow IT.

Examples of Shadow AI

Shadow AI, much like shadow IT itself, is a broad category within which engineers can find several different versions. Most of the time, employees resort to AI tools as they promise to help expedite work or automate tedious portions of work. If businesses don’t have a clear process to submit new tools for verification, then employees might just use them anyway.

Here are some general examples of shadow AI:

• Public Generative AI Models: Employees might use public GenAI models, like ChatGPT or Gemini, without permission. As these are third-party tools, pasting sensitive data into them is a major data leak risk that businesses need to defend against.
• Code or Work Assistants: Some employees might use software that promises to speed up aspects of their work, like generative AI code assistants that may scrape proprietary data from your code without permission.
• Privately Hosted AI Models: If an employee downloads and builds their own AI model, its visibility into company data and software may create a vulnerability risk for your company.

Although AI systems can help employees out, when used without company knowledge, they only serve to decrease visibility and potentially leak sensitive data.

The Risks of Shadow AI

Shadow AI poses many of the same threats that shadow IT as a whole does, like reducing security engineer vulnerability and expanding a company’s attack surface. However, the main problem with shadow AI is the accidental data leaking and exposure that can occur when using these tools.

Many leading AI providers have fairly opaque privacy practices, with leading models like ChatGPT still receiving media attention for not disclosing where their training data comes from (and whether their models are harvesting user data). For businesses, these points of uncertainty are data security risks that cannot be taken lightly.

Equally, by relying on third-party platforms, employees open their company up to any breaches that may happen to those platforms. For example, OpenAI recently experienced a Mixpanel hack, which led to some organizations having data exposed when they connected to OpenAI through APIs.

Without knowledge of these active AI systems in a business, engineers won’t be able to apply security configurations that prevent the exposure of sensitive data.

Best Practices to Keep Shadow AI and IT Under Control

As shadow AI is a subsection of shadow IT, many of the best practices that keep the latter under control also help to eradicate the former.

Here are the top practices that your business can follow to prevent shadow IT and AI:

• Implement clear governance policies that employees read during onboarding to outline acceptable AI and IT usage.
• 使う network monitoring tools to identify unauthorized apps, devices, and signals that may point to AI software.
• Educate employees on the dangers of shadow IT.
• Embed AI-powered security to detect sensitive data within AI tools and prevent it from leaving your organization.
• Outline data protection standards and data tagging structures to create risk-based defenses.
• Regularly review compliance obligations and company IT usage to ensure you abide by regulations.

Stay Protected with Check Point GenAI Security Solutions

Shadow IT, and increasingly the presence of shadow AI within that umbrella, is a major blind spot for organizations, reducing visibility and undermining active security systems. As employees turn to shadow solutions, businesses need to understand how to detect and mitigate them.

Check Point’s GenAI Security Solutions are custom-built for the identification and management of shadow AI systems. Get full visibility into both authorized and unauthorized GenAI usage in your company and automatically apply compliance standards to protect your company data. Prevent accidental data exposure and ensure your security engineers have the visibility they need to keep your business safe with Check Point.

Embrace AI while protecting against its potential downsides by getting started with a Check Point GenAI Protect demo 今日。