April 18, 2026

Shadow AI: The risks it poses to your environment

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AI is here to stay, for better or for worse.

AI can be used by security teams in predictive models, behavior analytics, and to detect polymorphic malware and uncover deepfakes. But as AI becomes more advanced, it can also put more weapons in the hands of attackers—as described by the Claude Mythos Preview.

Time will tell how soon businesses will overcome their growing pains and settle into a new world of promising automation opportunities and unnerving disruptions. Regardless of where your business currently finds itself in its AI adoption journey, it's almost certain your employees are using AI tools, whether you’re ready or not.

Shadow AI, the unmanaged, unmonitored use of AI tools is on the rise in every industry, but that doesn’t mean you have to fall victim to its risks. With appropriate management and Zero Trust security, businesses can take advantage of the AI boom before they are undone by it.

What is shadow AI?

Shadow AI is an evolution of shadow IT. Where shadow IT refers to the use of unapproved applications, hardware, or technology, shadow AI is instead the use of unapproved AI tools.

More specifically, it’s the use of AI models, assistants, or integrations without formal approval or management by internal IT, governance, and security teams, nor support informal employee policy.

This is where the “shadow” in shadow AI comes from. It’s not necessarily employees operating in the shadows, using AI surreptitiously or nefariously; you’ll probably observe employees openly using a browser-based AI chat tool. Rather, it’s the darkness cast over your IT, security, and governance teams’ visibility into their own environment. Without understanding and controlling the scope of AI tools your employees use, there can be no effective risk prevention should they be misused.

Unfortunately, it’s not just a matter of blocking ChatGPT’s domain in your network’s web filter. AI tools are more than just chatbots or image creators, like Microsoft’s own Copilot.

A few common, visible, and easy-to-use examples of AI tools include:

AI-enabled note-taking, audio recording, and transcribing tools used to capture and summarize live meeting notes. (examples: NotebookLM, Notion)
Browser extensions that summarize email messages. (examples: Superhuman, Mapify)
Embedded productivity bots in internal instant messaging applications that integrate with other SaaS resources. (examples: Teams, Slack)
Software application development and reasoning models that write code on behalf of engineers. (examples: Claude, Grok)

AI is not always visible on user desktops and mobile phone screens, though. Countless systems and services now use AI behind the scenes, whether users realize it or not, contributing to AI’s meteoric rise.

Why shadow AI is accelerating in adoption

AI is a new means to an end, and employees will naturally gravitate to what they perceive as the quickest means to their work goals. These new tools are universally helpful. Every industry, from tech to retail to healthcare, can leverage AI to innovate, automate, or enable employees to solve problems more easily and quickly.

While AI offers significant productivity gains, employees may also feel more pressure than ever to meet new standards. In an economy that values constant growth above all else, the drive to move fast to stay competitive is driving the adoption of AI before the guardrails around its risks are fully developed and implemented.

Organizations that do not already have strong controls against shadow IT may find themselves behind the curve in managing the massive influx of shadow AI.

The biggest shadow AI risks for SMBs

Reports show that the smaller the business, the more likely it is to have shadow AI use among its employees. This might be an organic consequence of the entrenched patterns of behavior, culture, and policies in larger, longer-lived organizations, leaving employees disincentivized to reach for tools outside their typical workflow.

In contrast, smaller businesses often offer employees the freedom to achieve goals with whatever means they see fit, though this may be out of necessity to make do with limited resources at the cost of security.

Whatever the reason employees use AI, they likely aren’t waiting around for your security teams to implement proper controls. A lot of AI tools are easy to find, easy to use, and (for now) mostly free, requiring only an account to sign up.

Being so accessible and all but completely omnipresent, the risks posed by shadow AI spread across all domains of information security.

Those risks include:

Employees pasting sensitive content into public chatbots: This can expose confidential customer, financial, HR, or operational data outside approved business systems, where it may be retained, mishandled, or leaked if the provider is breached or insecure.
Using submitted data to train AI models: Some AI providers may use user prompts, files, or outputs to improve their models, which can create a risk that sensitive business information is retained beyond its intended use or incorporated into future training datasets.
Increased phishing risk via employees using malicious tools: Fake or malicious AI apps can trick employees into surrendering credentials, downloading malware, or sharing sensitive business data.
AI tool integrations with more permissions than necessary: Some AI tools request excessive access to email, cloud storage, messaging platforms, or internal systems, creating unnecessary exposure to sensitive data.
Insecure third-party data handling: AI tools often rely on external vendors or other parties to store or transform data, which can increase the risk of improper storage, transmission, or use of business data.
Hallucinations: AI can generate confident but incomplete, misleading, or inaccurate outputs that cause employees to make poor technical, operational, or security decisions.
Creating bad and vulnerable code: AI-generated code may include insecure logic, weak validation, exposed secrets, or flawed assumptions that introduce vulnerabilities into the environment.
Input obfuscation and poisoning: Attackers can embed hidden or misleading instructions in content processed by AI tools, causing unsafe behavior, corrupted outputs, or improper disclosure of information that may be then used by your employees unwittingly.

Practical steps businesses can take today

Shadow AI risk cannot be reduced through technical controls alone. Employee behavior, business pressure, and unclear expectations all play a major role in how these tools are used.

The most effective starting point is to combine practical policy, approved alternatives, and leadership signals that make security part of productivity, rather than a barrier to it.

Consider these steps to start taking a stand against shadow AI use:

Write a clear AI use policy

Even if AI capabilities evolve faster than policy, a written standard gives employees a baseline for what tools are allowed, what data can never be entered, and what review is expected before use.

It should also be reinforced through mandatory compliance training, since strong guidance on sensitive data handling and hygiene already covers many of the same risks AI introduces. At the very least, if employees fail to follow this guidance the business will be indemnified from AI misuse through this policy, albeit still vulnerable to other risks.

Implement an approved AI tool

Research increasingly shows that outright blocking AI is often ineffective because employees will still seek out other tools and workarounds that help them work faster.

Providing a sanctioned, approved option channels usage into a more manageable, visible, and governable surface area. Many well-known tools offer subscriptions that guarantee segregation and exclusion from AI-model training over any data or text employees input into them.

Do not push employees for more productivity at the cost of security

When leadership rewards speed without acknowledging risk, employees are more likely to use unapproved AI tools to keep up with expectations.

How Zero Trust controls illuminate shadow AI

Zero Trust products and strategies ensure no user, application, or device is trusted by default, even after it is already inside a network environment. In its simplest form, this manifests as denying everything by default and permitting only actions, executions, and access through routinely revalidated exceptions.

Restricted by these controls, employees are prevented from uploading sensitive data or allowing unnecessary privileges.

The following ThreatLocker products can help businesses immediately apply Zero Trust controls against shadow AI: