Shadow AI

What is Shadow AI?

Shadow AI is when employees use generative AI tools like ChatGPT for work without approval from IT or security teams. Because it happens “in the shadows,” the enterprise cannot see what is being done or what data is being sent externally.

In a nutshell: Employees secretly using generative AI for work without company permission.

Key points:

• What it does: Using ChatGPT, Claude, and similar tools for work without authorization
• Why it’s risky: Risk of confidential data leaks, security breaches, and regulatory violations
• Why it happens: AI tool convenience, governance gaps, lack of official tools

Why it matters

Shadow AI is widespread in modern organizations. Research shows that 74% of employees use some form of AI tool for work, with many doing so without authorization. Even more concerning, 38% of employees admit to sharing confidential business data with AI tools.

This is extremely dangerous. For example, if an engineer pastes source code into ChatGPT to fix bugs, that code (a company’s most critical asset) gets exposed externally. If a lawyer uses ChatGPT to research case law, they might cite non-existent precedents in court.

How it works

Understanding Shadow AI’s mechanisms leads to prevention strategies:

Accessibility: Tools like ChatGPT and Claude are available in browsers immediately. Account creation is simple—no need to wait for IT approval.

Governance gaps: Many enterprises haven’t yet established AI usage policies. It’s unclear to employees what is permitted and what is prohibited.

Innovation hunger: When official tools are insufficient, employees find solutions themselves. To improve efficiency, they turn to unauthorized tools.

Embedded AI: AI features come built into widely-used applications like Office, Salesforce, and Notion, often enabled by default. Users don’t recognize data leakage risks.

Real incidents

Samsung code leak: Engineers pasted proprietary source code into ChatGPT for debugging. Later, the code may have been included in training data.

Lawyer’s hallucination: A New York lawyer cited ChatGPT-generated fictional case law in court submissions, facing fines and reputation damage.

Risks and challenges

Data leaks: Employees unintentionally share confidential information (customer data, financial data, IP) with AI. Once sent, retrieval is impossible.

Compliance violations: May violate regulations like GDPR, HIPAA, and PCI DSS, resulting in fines.

Quality and reliability: AI “hallucinations” (generating false information) can lead to incorrect analysis and poor decisions.

Detection difficulty: Unlike Shadow IT, browser-based usage is typically harder to detect through standard monitoring.

Detection and management best practices

Establish AI usage policies: Clearly define what is permitted and prohibited. Specify data handling rules explicitly.

Deploy technical monitoring: Implement AI-specific security tools (AI-SPM) to detect unauthorized AI usage.

Employee training: Educate employees about AI risks, especially data leaks, bias, and regulatory violations.

Provide official AI tools: Offer security-approved AI tools employees can safely use.

Regular audits: Periodically audit browser plugins, applications, and logs to detect unauthorized usage.

Related terms

• Generative AI — The technology Shadow AI targets
• Data Security — The risk at stake
• AI Governance — The framework for risk management
• GDPR — Regulation that may be violated
• Shadow IT — Similar unauthorized IT usage

Frequently asked questions

Q: Shouldn’t we just ban AI completely? A: No. Bans drive employees to unauthorized tools, making things more dangerous. A balanced approach is needed.

Q: Isn’t it unfair to regulate honest employees? A: Yes. Clear policies and training allow employees to use AI safely.