Shadow AI Is Already in Your Environment. Here’s How to Find It & Control It. 

You didn’t approve it. Your security team didn’t evaluate it. And yet, right now, employees across your organization are almost certainly using AI tools to do their jobs. 

Some are using ChatGPT to draft emails. Others are running sensitive documents through AI summarization tools. Some have connected AI agents to internal systems without telling IT. None of this was sanctioned. Most of it is invisible to your security stack. 

This is Shadow AI, and according to Gartner, 69% of organizations suspect or have evidence that employees are using prohibited public generative AI tools. The question isn’t whether it exists in your environment. It’s whether you have any visibility into it. 

This post breaks down what Shadow AI is, why it’s a serious security risk, and, most importantly, how to start finding it. 

What Is Shadow AI? 

Shadow AI refers to any artificial intelligence tool, service, or agent being used within an organization without the knowledge, approval, or oversight of IT or security teams. 

The term is a natural evolution of “Shadow IT,” which includes the unauthorized apps and devices that security teams have been fighting for over a decade. But Shadow AI introduces a new layer of risk that Shadow IT never did. 

Traditional Shadow IT meant an employee was using Dropbox instead of the approved file share. Annoying, but relatively contained. Shadow AI means an employee is feeding customer data, financial records, or proprietary source code into a third-party large language model, and that data may be leaving your environment permanently, with no audit trail, no DLP policy triggered, and no alert fired. 

Shadow AI isn’t a future risk. It’s a present one. And for most security teams, it’s already operating outside their field of view. 

Why Shadow AI Is Different from Other Security Risks 

Most security threats come from outside the organization. Shadow AI comes from inside, from well-intentioned employees trying to do their jobs faster and better. 

That distinction matters for a few reasons. 

It’s hard to detect with traditional tools 

Legacy DLP rules were built for email, file transfers, and USB devices. They weren’t designed to monitor what’s being typed into an AI prompt field. An employee copying and pasting a client contract into ChatGPT won’t trigger most existing data loss prevention policies. 

It operates across boundaries security wasn’t built for 

AI agents can connect to external APIs, process data in cloud environments, take autonomous actions, and write back to internal systems, all without generating the kinds of logs that traditional security monitoring tools are looking for. A single misconfigured AI integration can create an exposure that ripples across your entire environment before anyone notices. 

Banning it doesn’t work 

Organizations that try to block AI tool usage often find that it simply pushes the activity outside the corporate network entirely. Employees switch to personal devices or personal accounts. Your visibility drops to zero while the risk remains exactly the same. 

Blocking sanctioned tools often makes this worse, not better. 

The Three Risks Shadow AI Introduces 

Shadow AI doesn’t create one problem, it creates three distinct risk categories that security teams need to address separately. 

1. Data Exfiltration 

When employees enter sensitive data into unsanctioned AI tools, that data leaves your environment. Depending on the tool’s terms of service, it may be stored, used to train future models, or accessible to third parties. PII, intellectual property, financial data, legal documents, and source code are all at risk. Most organizations have no visibility into how much of this is happening or what’s been exposed. 

2. Unmanaged Access and Identity Risk 

AI agents are increasingly being given access to internal systems — calendars, email, code repositories, customer databases. When these integrations are set up without IT oversight, they create access paths that bypass your identity and access management controls entirely. These agents have no MFA enforcement, no least-privilege policy, and no offboarding process if the employee who set them up leaves the organization. 

3. Accidental Destruction 

AI automation tools can take actions at machine speed. When they’re misconfigured or behave unexpectedly, the damage can propagate faster than any human can intervene. Files deleted, systems misconfigured, bulk changes pushed to production, these aren’t hypothetical scenarios. They’re the emerging category of AI-driven incidents that traditional incident response playbooks weren’t designed to handle. 

How to Find Shadow AI in Your Environment 

The good news: Shadow AI is findable. It requires a deliberate approach across a few different discovery methods, but organizations that commit to visibility can get a meaningful picture of their exposure relatively quickly. 

Start with network and DNS traffic analysis 

AI tools make outbound connections to identifiable external domains, like api.openai.com, claude.ai, gemini.google.com, and dozens of others. A review of DNS query logs and firewall traffic will reveal which AI services are being accessed from your network, how frequently, and by which devices or user groups. This is often the fastest way to surface unsanctioned usage at scale. 

Audit OAuth and third-party app connections 

Many AI tools integrate with enterprise platforms like Microsoft 365, Google Workspace, Slack, and Salesforce via OAuth. Review the list of third-party apps authorized to access your core platforms. Any AI tool with access to email, calendar, files, or CRM data that wasn’t formally evaluated represents a live exposure. 

Review endpoint activity 

Endpoint detection tools can flag browser activity, installed extensions, and application usage patterns that indicate AI tool adoption. Browser extensions in particular are a common Shadow AI vector. Employees install AI writing assistants, summarization tools, and meeting transcription services that sit inside the browser and have access to everything the user sees. 

Survey employees directly 

This sounds low-tech, but it’s often surprisingly effective. A brief, anonymous survey asking employees which AI tools they use for work (with a commitment that the purpose is to evaluate and approve tools, not punish usage) frequently surfaces tools that technical discovery methods miss. Employees will tell you what they’re using if they believe the goal is governance, not policing. 

Assess your AI governance policy baseline 

Per IBM’s 2025 Cost of a Data Breach Report, 63% of organizations lack AI governance policies to manage AI. If you don’t have one, the absence of policy itself is a Shadow AI risk. Employees have no guidance on what’s acceptable, which means every individual is making their own judgment call about what data is safe to share with AI tools. 

Discovery isn’t a one-time exercise. Shadow AI is dynamic. New tools launch constantly, employee behavior changes, and integrations proliferate. Continuous monitoring is the standard to aim for, not periodic audits. 

What to Do Once You’ve Found It 

Visibility is the first step. Once you have a picture of what’s in your environment, the goal is governance. This means bringing AI usage under control without shutting down the productivity gains that are driving adoption in the first place. 

The organizations getting this right are doing three things: 

1. Establishing a formal AI acceptable use policy that defines what tools are approved, what data can be shared with AI services, and what the process is for evaluating new tools before adoption. 

2. Extending existing security controls, like DLP, Zero Trust access policies, identity and access management, to explicitly cover AI tools and the data flows they create. 

3. Building response capabilities for AI-driven incidents, including the ability to detect anomalous AI activity, contain it quickly, and reverse automated actions when they cause unintended damage. 

None of this requires blocking AI. It requires governing it, which means your organization can keep the productivity benefits while reducing the exposure. 

Where Does Your Organization Stand? 

Most security teams we talk to know Shadow AI is happening in their environment. What they lack is visibility into the scope, and a clear picture of which controls are missing. 

We built an AI Security Readiness Checklist to help security leaders answer that question quickly. It covers 20 controls across five risk categories:  

1. Visibility and discovery 

2. Data security and governance 

3. Access and identity controls 

4. Detection and response  

5. Policy  

Score yourself as you go, and you’ll have a clear picture of your posture in about five minutes. 

→  Download the free AI Security Readiness Checklist 

If you’d rather talk through your environment with a cybersecurity expert, we offer a free 30-minute AI Risk Review. No prep required, no obligation. Just a focused conversation about where you stand and what to do about it. 

→  Book a free AI Risk Review with an Arraya expert