Artificial intelligence is everywhere, but not always where it's supposed to be.

As AI tools become more accessible, employees are increasingly using them without approval or oversight.

This is what we call shadow AI, and it’s a growing problem with serious consequences.

Let’s break down what shadow AI really is, why it’s risky, and how to get ahead of it.

What is Shadow AI?

Shadow AI refers to the use of artificial intelligence tools or systems within an organization without the knowledge, approval, or control of the IT or security teams.

Think: employees feeding confidential data into ChatGPT, developers automating decisions with unvetted models, or marketers using AI to draft campaigns via unsecured third-party platforms.

It’s the AI version of shadow IT, and it’s often invisible until it causes damage.

What Are the Most Common Examples of Shadow AI?

Here’s what shadow AI typically looks like in practice:

• Employees using public AI tools like ChatGPT, Gemini, or Claude for sensitive tasks
• Teams automating processes with AI code assistants such as GitHub Copilot without security reviews
• Unofficial ML models trained on company data and used in production
• Departments integrating AI features into workflows through no-code platforms or APIs without notifying IT
• AI-driven decision-making in HR, finance, or legal without governance or documentation

Why is Shadow AI a Concern for Organizations?

Shadow AI can expose sensitive data, introduce bias, and lead to bad decisions without anyone being accountable.

The bigger issue is that these tools evolve rapidly.

A tool that’s safe today could roll out a risky update tomorrow.

If IT doesn’t even know it exists, they can’t monitor or mitigate it.

What Are the Risks of Shadow AI?

The risks aren’t hypothetical.

They’re real, and they're already happening:

• Data exposure: Employees might input proprietary data into external AI platforms with unclear data retention policies
• Compliance violations: Shadow AI can breach GDPR, HIPAA, or internal data policies without anyone realizing it
• Security gaps: Many AI tools don’t meet enterprise-grade security standards
• Bias and ethical issues: Unvetted AI models may produce discriminatory or flawed outputs
• Lack of accountability: When something goes wrong, there’s no paper trail, no ownership, and no way to fix it fast

How to Detect Shadow AI

You can’t fix what you can’t see. Detection starts with perimeter awareness. Here’s how to shine a light on shadow AI:

• Network monitoring: Look for traffic to AI domains and APIs like OpenAI, Anthropic, Hugging Face
• Endpoint security tools: Use EDR and DLP to flag unauthorized software or unusual file transfers
• Browser activity analysis: Detect frequent access to AI platforms or plugins through browser extensions
• Employee surveys and shadow audits: Ask departments what tools they’re using
• Cloud app discovery tools: These can reveal hidden SaaS usage, including AI-powered ones

How to Prevent Shadow AI

Once you’ve spotted it, the goal is to manage, not just block, AI use. Here's a smarter path forward:

1. Set an AI policy: Be clear on what’s allowed, what’s not, and where approvals are needed
2. Offer sanctioned tools: Give employees approved AI tools that meet security standards
3. Educate your workforce: Most people aren’t malicious, they’re just trying to get work done. Train them to use AI responsibly
4. Build an AI risk governance team: Involve IT, legal, compliance, and business leads
5. Use access controls and logging: Limit who can use AI tools, and keep detailed usage records
6. Automate monitoring: Deploy tools that alert when unapproved AI usage happens

The Bottom Line

Shadow AI isn’t just a buzzword. It’s a business risk. It’s fast, it’s silent, and it’s already inside your organization whether you know it or not.

But this isn’t a call to ban AI. It’s a call to take control.

Recognize the threat, set the guardrails, and give your teams the tools they need to use AI safely and effectively.

FAQ

How does shadow AI differ from shadow IT?

Shadow IT refers to any unauthorized software or system used outside of IT’s control, like file sharing apps or unsanctioned messaging platforms.

‍Shadow AI is a subset, specifically involving AI tools. It’s newer, faster-moving, and can process and expose data at much larger scales than traditional shadow IT.

Can shadow AI usage be helpful?

Yes. Many employees turn to shadow AI because it boosts productivity. The key is to bring it out of the shadows, vet the tools, and support safe experimentation with proper oversight.

What industries are most at risk from shadow AI?

Industries handling sensitive data such as healthcare, finance, legal, government, and tech face the highest risks.

But the truth is every organization that uses data is exposed.