Shadow AI: What it is and how to manage the risk

Shadow AI has become one of the biggest visibility gaps for IT and security teams at enterprise organizations. It's hard to detect, difficult to stop outright, and the risks from data leaks to compliance violations are real. But before you can address it, you need to understand what's driving it.

This post breaks down what shadow AI is, how it shows up in enterprise environments, and what IT and security teams can do to manage it.

It's likely that your employees are already using AI. The question is whether they're using the tools you approved or the ones they found on their own.

What is shadow AI?

Shadow AI refers to the use of AI tools and systems within an organization without the knowledge or approval of IT or security teams. Think employees using consumer-grade generative AI to summarize internal documents, teams running their own machine learning models, or departments subscribing to AI-powered apps outside the official procurement process.

The problem is widespread. According to Microsoft WorkLab research, 75% of employees now use AI tools not officially sanctioned by their IT or security team.

Shadow AI is growing fast. Any organization that has invested in an AI strategy while still struggling to drive consistent adoption of approved tools is at risk.

The term 'shadow' reflects that this AI usage is happening out of sight of the people responsible for managing data security, software governance, and compliance.

Shadow AI is often a symptom of an adoption gap. When organizations deploy AI tools but don't enable employees to use them effectively, people find their own alternatives.

Shadow AI vs. Shadow IT: What's the difference?

Shadow IT, the use of software or services without IT approval, has been around for decades. Shadow AI is a newer and more dangerous version of the same problem.

What makes shadow AI different:

• Scale of risk. AI tools process, generate, and sometimes store sensitive data in ways traditional shadow IT does not. A rogue spreadsheet tool is not the same as an AI model ingesting confidential contracts.
• Speed of spread. AI tools are proliferating faster than IT teams can evaluate them. Employees can spin up an AI workflow in minutes with a free account.
• Data handling. Many consumer AI tools use input data to train their models. When employees paste internal documents or customer data into these tools, that information may be handed to a third party with no visibility or consent.
• Approved tools are not being used. If your organization has rolled out Microsoft Copilot but employees have not been properly enabled on how to use it, they will find ChatGPT instead.

Why shadow AI happens in enterprise organizations

Shadow AI does not happen because employees are trying to cause problems. It happens because they are trying to get their jobs done faster and they are not getting what they need from the tools they have been given.

This is important for IT and security leaders to understand: shadow AI is often a symptom of an adoption gap, not a security culture problem. Employees who are properly enabled on approved AI tools are far less likely to go looking for alternatives.

Common causes include:

• No clear AI policy. Without guidance on what is allowed, employees default to whatever works.
• Procurement is too slow. By the time IT evaluates and approves a new tool, the team that needed it has already found a workaround.
• The productivity pressure is real. Employees are under constant pressure to work faster. AI tools that promise to save hours are hard to resist.

The same drive that leads employees to shadow AI can be channeled into adoption, when the organization provides the right tools with the right support.

The risks of shadow AI

The risks of shadow AI fall into a few categories, ranging from immediate technical and security concerns, such as data exposure, uncontrolled model access, and lack of monitoring, to longer-term business, compliance, and reputational impacts. Some of the risks include:

Data security and breaches

When employees use unauthorized AI tools, data leaves your control quickly. Customer records, financial information, and proprietary research can end up in a third-party model without anyone realizing it happened. Consumer AI tools may retain and use input data in ways that violate your data policies, often without the employee knowing.

According to UpGuard research, more than 80% of workers, including nearly 90% of security professionals, use unapproved AI tools in their jobs.

Regulatory and compliance violations

Organizations in regulated industries, including financial services, healthcare, and legal, face strict requirements around data handling. Using an unapproved AI tool to process that data can trigger violations of GDPR, HIPAA, SOC 2, and other frameworks, even when the employee never intended to cause harm.

Unverifiable AI outputs

AI tools can generate biased, inaccurate, or misleading outputs. When those tools operate outside your governance framework, there is no audit trail, no quality control, and no accountability.

Reputational damage

A data leak tied to an unauthorized AI tool does not just cost money. It costs customer trust, and that is hard to earn back.

Examples of shadow AI

Shadow AI shows up differently across business functions, but the pattern is consistent: employees reach for the fastest available tool, regardless of whether IT approved it.

Customer support. A support agent pastes a customer's complaint, including account details, into ChatGPT to draft a response instead of using the approved knowledge base. The customer's data now sits in a third-party system with no retention controls.

Marketing. A campaign manager uses an unauthorized AI image generator to create ad visuals, bypassing the approved creative workflow. The generated assets may incorporate copyrighted material, and the organization has no license trail.

Finance. An analyst uploads quarterly revenue data into an unapproved AI model to run forecasting scenarios. Confidential financial data is now processed outside the organization's security perimeter.

Engineering. A developer pastes proprietary source code into a public LLM to debug a function. That code may be stored, indexed, or surfaced in responses to other users of the same model.

HR. A recruiter uses an AI tool to screen resumes and rank candidates without a data governance review. The model's training biases go unaudited, creating legal and ethical exposure.

How to detect shadow AI in your organization

Detecting shadow AI starts with visibility. Most organizations discover it through one of these approaches:

• Network monitoring. Analyzing outbound traffic can surface connections to AI platforms like ChatGPT, Midjourney, or Hugging Face that are not in your approved stack.
• SaaS spend audits. Reviewing software expenses across teams often surfaces AI subscriptions IT never approved.
• Endpoint monitoring. EDR tools can flag downloads and installations of AI clients or browser extensions.
• Employee surveys. Sometimes the simplest method works best. Asking teams directly what tools they are using can surface shadow AI faster than technical detection.

Detection is step one. Once you know what is out there, you need a plan.

Shadow AI management best practices

Managing shadow AI effectively is less about locking everything down and more about creating conditions where employees do not need to go around IT in the first place.

1. Build a clear AI acceptable use policy. Define what employees can and cannot use, what data is off-limits for external AI tools, and how to request approval for a new tool. Keep it simple enough that people actually read it.

2. Create a fast-track approval process. If it takes six months to get an AI tool approved, people will not wait. Set up a lightweight review process that can evaluate low-risk tools quickly and give employees a clear path forward.

3. Enable employees on approved tools. This is the most underrated step. If you have invested in an enterprise AI platform like Microsoft Copilot, make sure employees actually know how to use it. Proper enablement reduces the need to look elsewhere. When people understand what Copilot can do and how to do it, they are not reaching for ChatGPT.

BrainStorm's AI adoption platform is built for exactly this problem. It delivers adaptive learning Flows that adjust to each user's role and readiness, reaches employees through the channels they already use (email, Teams, portal), and provides adoption analytics that connect learning activity to actual tool usage so you can see whether enablement is working, not just whether it was completed.

How to increase AI adoption among employees:

1. Deploy adaptive, role-based learning paths that meet employees where they are, not where you assume they are.
2. Deliver enablement through channels employees already use, so training does not require them to seek out a separate destination.
3. Track behavior change, not just course completions. Completion data tells you who clicked through. Usage data tells you who changed how they work.
4. Connect adoption data to business outcomes so leadership sees the return on enablement investment.

4. Build continuous monitoring into your security posture. Shadow AI is not a one-time problem to solve. New tools launch every week. Build monitoring into your ongoing security posture, not just your quarterly audits.

5. Treat employees as partners. Blanket bans tend to drive shadow AI underground rather than eliminate it. Engage employees in building AI policy, understand why they are reaching for unauthorized tools, and close the gaps causing it.

The shadow AI fix starts with adoption

Most shadow AI conversations focus on detection and governance. Those things matter. But the most effective long-term fix is making sure approved AI tools are actually being used the way they were designed.

The most effective fix for shadow AI is adoption. When employees are genuinely enabled on tools like Microsoft Copilot, they gain productivity benefits without the risk. BrainStorm helps organizations close that gap, not with a one-time push, but with sustained behavior change that shows up in actual usage data.

BrainStorm's approach is grounded in the ADOPT™ framework, five deliberate stages that move users from initial awareness to durable, repeatable behavior:

• Awareness. Understanding why the change is needed and what's at stake.
• Desire. Building motivation to explore, not fearful compliance.
• Orientation. Learning how the tool works in the context of the user's actual role and workflows.
• Participation. Starting the new behavior with measurement so progress is visible.
• Transformation. Making the behavior stick as part of daily work, not a one-time event.

This is what separates an AI adoption platform from a training tool. BrainStorm is an AI adoption platform built for sustained behavior change. While competitors track completions, BrainStorm tracks behavior change.

How to drive AI behavior change in the workplace:

1. Start with Awareness: make the case for change specific to each role, not generic to the organization.
2. Build Desire by showing employees what they gain, not just what they risk by not complying.
3. Deliver Orientation through adaptive learning that connects tool capabilities to real workflows, so employees practice in context.
4. Measure Participation and Transformation by tracking actual tool usage alongside learning engagement, so you know when behavior changes and when it sticks.

If your team has invested in AI tools and isn't sure it's sticking, let's talk!