Shadow AI

What is Shadow AI?

Shadow AI refers to the use of artificial intelligence tools, applications, and models within an organization without the knowledge, approval, or oversight of IT and security teams. The term extends the concept of Shadow IT into the AI domain. As consumer AI tools like ChatGPT, Claude, Gemini, Copilot, and hundreds of specialized AI applications became widely accessible, employees began integrating them into daily work — often submitting sensitive business data to external AI services without understanding the privacy, security, or compliance implications of doing so.

Description

Shadow AI creates multiple distinct risk vectors. Data leakage occurs when employees paste confidential documents, source code, customer PII, financial data, or trade secrets into AI interfaces that may use submitted content for model training or retain it on third-party servers. Compliance violations follow when regulated data — covered under HIPAA, GDPR, PCI DSS, or SOC 2 — is processed through unsanctioned AI services without appropriate data processing agreements. Security vulnerabilities emerge when AI-generated code is deployed without review, AI-written content contains hallucinated facts presented as authoritative, or when AI tools are granted access to internal systems through browser extensions or integrations that bypass security controls. IBM's 2025 Cost of a Data Breach Report found that 63% of breached organizations lacked AI governance policies. Evolve Security's blog post on how CISOs can find LLMs they don't know about details a practical approach to inventorying unauthorized AI usage across an enterprise. The challenge is compounded by agentic AI tools that operate autonomously and may take actions — sending emails, modifying files, querying APIs — without user awareness.

Usage and Examples

A law firm's associates use ChatGPT to draft legal briefs, pasting case details and privileged client communications into the interface to accelerate their work. Simultaneously, an engineering team uses an unauthorized AI coding assistant that has been granted access to the company's GitHub repositories via a browser extension, periodically syncing code to an external service. Neither use case appears in IT inventory, neither has a data processing agreement, and neither has been evaluated for supply chain attack risk in the AI toolchain. These scenarios represent the current reality at most organizations. Addressing shadow AI requires a combination of technical controls (network monitoring, DLP, browser policy), governance (an approved AI tool catalog and acceptable use policy), and employee awareness training to redirect AI use toward sanctioned alternatives.

How Does This Relate to Penetration Testing?

Shadow AI surfaces as a finding in several penetration testing contexts. During external network or application assessments, testers may identify browser extensions with broad permissions, unauthorized API connections to AI services, or AI-generated code with exploitable vulnerabilities that bypassed standard code review. Red team engagements increasingly incorporate AI phishing and social engineering vectors, and the presence of shadow AI tools — which employees may trust implicitly — creates new pretexting opportunities. Governance assessments as part of Advisory services can help organizations build the AI governance framework needed to bring unauthorized AI usage under control. Evolve Security's Advisory and AI Penetration Testing services help organizations identify shadow AI exposure and establish security governance for AI usage.