Shadow AI vs Shadow IT: What’s the Difference?

Although shadow AI and shadow IT are terms that are sometimes thrown around interchangeably, they’re not quite the same thing. While there is some overlap between shadow AI and shadow IT, each represents a unique problem and should be treated as such in order to ensure optimal network security.

This post breaks down what each term actually means, the key differences between shadow AI vs shadow IT, and what your team can do to get ahead of both.

What Is Shadow AI?

What Is Shadow IT in Cybersecurity?

Shadow AI vs. Shadow IT: What’s the Difference?

Why Shadow AI Creates New Cybersecurity Risks

Common Examples of Shadow AI in the Workplace

How to Detect Shadow AI

Best Practices for Managing Shadow AI and Shadow IT

Why Visibility Is the Foundation of AI Security

Final Notes

Frequently Asked Questions

Key Takeaways

Shadow AI is the unauthorized use of artificial intelligence (AI) tools, copilots, and large language models (LLMs). Shadow IT is the broader, longer-running problem of unapproved hardware or software on your network.

Shadow AI introduces new risks, such as prompt leakage and model exposure, alongside usual data governance concerns.

You can’t secure an AI tool your security team doesn’t know exists. Continuous visibility is the starting point for managing both.

While banning AI outright may be tempting, it rarely works in practice. Employees adopt these tools because they solve a real problem, so governance needs to address that problem rather than ignore it.

What Is Shadow AI?

Shadow AI generally refers to the unapproved or insufficiently governed use of AI applications, models, assistants, application programming interfaces (APIs), embedded AI features, or AI-enabled software as a service (SaaS). The risk may arise from an entirely unapproved service or from an approved service being used with an unapproved account, data type, integration, or workflow. For example, this may include someone pasting a contract into ChatGPT to get a quick summary or a developer running code through an AI assistant that was never vetted. While these actions are not inherently malicious, none of them goes through procurement or appears on an inventory.

Shadow AI can be a difficult problem because employees typically are just trying to do their jobs more efficiently, not cause a breach. Tools like ChatGPT, GitHub Copilot, and Otter.ai are easily accessible and, in many cases, genuinely useful. The pressure to perform plus easy access is a reliable recipe for shadow AI showing up in your environment, whether it’s explicitly approved or not.

The risks of shadow AI can occur behind the scenes. Prompts, uploads, and generated outputs may cross organizational boundaries, although the exact data path and level of security-team visibility depend on the deployment model and controls in place. Network-level monitoring can provide useful visibility into destinations and application identities. It can also show session metadata, traffic volumes, and transfer patterns. Identity, endpoint, cloud, and application logs can add context where available.

Data could be used to train a third-party model or sit in a vendor’s logs indefinitely. You don’t know because the tool was never brought into your governance process in the first place.

What Is Shadow IT in Cybersecurity?

Shadow IT is the use of hardware, software, cloud services, SaaS applications, or other technology for organizational work without appropriate authorization, inventory coverage, or security governance. Because users may access these services from corporate, remote, or unmanaged environments, discovery should combine identity, endpoint, cloud, application, and network telemetry. Shadow IT has been around for a while, long before anyone worried about employees copying and pasting sensitive data into a chatbot.

Common examples of shadow IT still look familiar:

File-sharing apps used to move documents outside approved channels

Personal cloud storage accounts syncing work files

Collaboration platforms adopted by a team without a security review

SaaS subscriptions expensed on a personal card and never logged anywhere

Shadow IT continues to happen because approval processes interrupt workflows and delay delivery. If a team needs a tool today but IT review takes a few weeks, it can be tempting to start using the tool now rather than later.

Shadow AI vs. Shadow IT: What’s the Difference?

Shadow AI often overlaps with shadow IT, but the categories are not identical. Shadow AI can involve an unapproved application, account, or integration. It can also involve unapproved use of an otherwise sanctioned platform. This makes application identification, traffic analysis, and metadata-based monitoring important complements to asset inventories.

But AI tools carry risks that a rogue file-sharing app or unsanctioned project management platform simply doesn’t.

With shadow IT, concerns mostly have to do with where the data lives and who can access it. With shadow AI, you’re also worried about what happens to the data once it’s fed into a model, whether prompts are logged or retained, and whether an AI system generates factually inaccurate outputs. It’s a different, and in some ways deeper, kind of exposure.

Both stay out of range of IT oversight and create blind spots. Shadow AI, however, adds complexity around prompts, model behavior, and AI-generated content that traditional shadow IT policies weren’t designed to handle.

Why Shadow AI Creates New Cybersecurity Risks

Employees don’t always realize what they’re handing over when they use an AI tool. Someone drops a customer contract into a public LLM to get a faster summary, not thinking that they’ve just shared proprietary information with a system outside the company’s control. Multiply that across a workforce and you’ve got a steady drip of sensitive data heading to places it was never supposed to go.

The risks include:

Sensitive data leakage

Compliance violations in industries with strict data-handling rules

AI outputs that sound authoritative but are wrong

Intellectual property that leaves through a chat window

Newer concerns are piling on, too. Shadow AI can also increase exposure to distinct AI-related threats and governance concerns, including prompt injection against AI applications or agents, unsafe tool or data integrations, unintended model use, and AI-assisted phishing. These issues should be assessed separately. Network traffic intelligence and metadata can improve detection and response. So can segmentation and downstream security-tool integration.

Common Examples of Shadow AI in the Workplace

Some examples of shadow AI in the workplace include:

Employees tasking public LLMs with summarizing confidential documents

Developers using AI coding assistants that haven’t undergone a security review

Marketing teams uploading customer data into AI content-generation tools

Sales teams running AI meeting assistants that connect directly to customer relationship management (CRM) data

Employees building no-code AI agents or workflows without first consulting IT

How to Detect Shadow AI

Traditional asset inventories were built for a world of installed software and known vendors. AI tools are different because they’re browser-based, they update constantly, and a new one can launch and be adopted by a team before your asset management process even knows it exists.

Detecting shadow AI takes a different approach:

Network traffic analysis: Spot connections to known AI services and APIs

Domain name system (DNS) and SaaS discovery: Catch new AI domains as they show up on your network

AI application identification: Recognize AI and LLM traffic patterns, not just generic SaaS usage

Cloud access security monitoring: Track data moving to and from AI platforms

User behavior analytics: Flag unusual patterns that might indicate unsanctioned AI use

The real shift is moving from a point-in-time inventory check to continuous monitoring. Approved software lists describe what the organization intends to permit, while continuous monitoring helps reveal what is actually being used. Network-level visibility and traffic intelligence can provide out-of-band evidence across observed traffic paths. Effective discovery should also correlate network metadata with endpoint, identity, cloud, application, procurement, and user-behavior data.

Best Practices for Managing Shadow AI and Shadow IT

Consider these practices to manage shadow AI and shadow IT:

Create clear AI tool policies: Build specific acceptable-use policies for AI tools instead of a vague AI-is-not-allowed memo that nobody reads

Maintain consistent visibility: Maintain continuous visibility across users, applications, and network traffic rather than relying on an annual audit

Protect sensitive information: Classify sensitive data before it reaches an AI tool, so a guardrail is in place before the data leaves

Offer AI guidance to employees: Educate employees on how to use AI safely instead of just telling them not to use it

Apply Zero Trust principles: Apply Zero Trust principles alongside AI-aware monitoring so unsanctioned tools get flagged early

Organizations should also regularly review a cloud security checklist to keep AI adoption in line with existing best practices.

Why Visibility Is the Foundation of AI Security

Every strategy above depends on the idea that you can’t secure what you can’t see. If your security team doesn’t know an AI tool is running, no policy, monitoring, or classification can address the issue.

Deep network visibility closes that gap. It helps security teams discover AI applications nobody signed off on. It also shows how data moves through the network and reduces the blind spots where shadow AI thrives.

Final Notes

Shadow IT taught security teams that unmanaged technology is a risk even when nobody intends harm. Shadow AI is the next step, with higher stakes attached to every prompt and AI-generated output. Organizations that get ahead of it build the visibility to monitor shadow AI rather than prevent AI tool usage altogether.

Combining visibility with AI tool policies, employee education, and tools like Gigamon AI can help organizations manage AI use as it evolves. The Gigamon Deep Observability Pipeline helps security teams discover AI applications that no one has approved.

Want a better idea of how organizations can use tools to identify shadow AI and reduce blind spots? Schedule a live demo today.

Frequently Asked Questions

Is Shadow AI Always a Security Risk?

Not necessarily. Not every instance of shadow AI leads to a security incident. Unapproved AI use, however, creates blind spots that make it difficult for security teams to assess risk.

The risk in a given case depends on the data being shared, the AI tool involved, and governance controls in place.

Why Are Employees Using Shadow AI?

Employees turn to AI tools to boost productivity, cut down on repetitive work, or access features their approved enterprise tools don’t yet offer. Understanding those motivations matters because it points organizations toward governance strategies that work rather than restrictive policies that don’t address the root of the problem.

Can Shadow AI Impact Regulatory Compliance?

Yes. Unauthorized AI use can lead to compliance problems if employees submit regulated, confidential, or customer data to external AI services that were never vetted for that kind of information. Organizations in regulated industries should evaluate how their employees’ AI use aligns with internal policy and government or industry regulations.