Shadow AI
The unauthorised or undisclosed use of AI tools by employees — often outside IT oversight — creating data security, compliance, and operational risks.
What Is Shadow AI?
Shadow AI is the use of AI tools — chatbots, AI-powered applications, browser extensions, or coding assistants — by employees without the knowledge or approval of the IT or security team. It is the AI-era evolution of "shadow IT" (the use of unauthorised SaaS tools), but with amplified risks because AI tools often process and learn from the data they're given.
Why Shadow AI Happens
Employees adopt AI tools because they're genuinely useful. ChatGPT, Claude, Gemini, GitHub Copilot, and hundreds of AI-powered productivity tools offer tangible productivity gains. When organisations don't provide approved AI tools, employees find their own — often using consumer-grade services with different data handling policies than enterprise tools.
The Security and Compliance Risks
Data exposure: Employees paste customer data, financial information, source code, legal documents, or personally identifiable information into AI chatbots. Some consumer AI services use inputs to train future models.
Intellectual property leakage: Confidential business strategy, product plans, or proprietary algorithms shared with external AI services.
Compliance violations: Sending personal data (under GDPR) or regulated data (under HIPAA, PCI DSS) to unapproved third-party services may violate data processing agreements and regulatory requirements.
Unvetted outputs: AI-generated content used without review — incorrect legal clauses, faulty code, or hallucinated facts — creates operational and reputational risk.
Account security: Personal AI accounts used for work purposes operate outside organisational access controls and offboarding processes.
Managing Shadow AI
- Conduct an AI inventory: Understand what AI tools employees are already using
- Develop an AI use policy: Define what's permitted, what's prohibited, and handling rules for sensitive data
- Provide approved alternatives: If employees need AI tools, give them approved, enterprise-grade versions
- Implement technical controls: DLP and web filtering can detect or block unapproved AI services
- Train staff: Educate on what data is appropriate to share with AI tools
- Create a reporting channel: Let employees flag AI tools they want approved, rather than driving usage underground