What Is Shadow AI? Risks and examples for businesses

Shadow AI is not necessarily malicious. It often begins when employees or developers use accessible AI tools to solve real productivity problems, while the organization lacks visibility into what is being used, what data it can access, and what controls apply.

Shadow AI is AI use that operates outside an organization's established oversight and governance processes. The defining issue is not a particular model or product, but whether responsible teams can identify, assess, and control how AI interacts with company data, systems, and workflows.

An employee using an unapproved chatbot is one example, but the term is broader. Shadow AI can also include an AI feature enabled inside an existing SaaS platform without review, an external model connected to internal applications, or an AI agent that has access to company systems but no clearly defined owner or governance process.

Some of this activity may happen without users realizing that they are bypassing organizational controls. The risk is not simply that AI is being used, but that the organization cannot properly see, evaluate, or govern that usage.

Shadow AI usually appears when access to useful AI capabilities moves faster than an organization's approval, procurement, or governance processes.

Employees may turn to public AI tools because approved software does not meet a specific need or because obtaining a new tool takes too long. Developers can connect an external model through an API with relatively little effort, while SaaS products may introduce AI features into tools teams already use.

Internal policies can also lag behind adoption. If employees do not know which tools are approved, what information can be shared with them, or how to request a new AI capability, they may make those decisions independently.

In many cases, the problem is a gap between demand for AI and the organization's ability to provide clear rules and practical alternatives.

Shadow IT covers software, SaaS services, hardware, cloud resources, and other technology used without appropriate organizational approval or management. Shadow AI is the AI-specific part of this broader problem.

AI introduces additional concerns around prompts and uploaded data, model providers, generated outputs, integrations, and agents that may act across connected systems. These concerns can create risks that differ from those associated with unmanaged technology more broadly.

The concepts overlap, but Shadow IT describes the broader visibility problem around unmanaged technology, while Shadow AI focuses on ungoverned AI use and the data, model, output, and agent-related risks that come with it.

Shadow AI can appear anywhere AI is introduced into work without the appropriate review or visibility.

Common scenarios include:

• Unapproved chatbot use. An employee pastes customer records, financial information, or internal documents into a public AI assistant to summarize or rewrite them.
• AI coding assistants. A developer sends proprietary source code to an AI coding tool that has not been reviewed for company use.
• Embedded AI features. A team enables an AI feature inside an existing SaaS platform without checking how it processes or accesses organizational data.
• External model integrations. A developer connects an external LLM or AI API to an internal workflow or application without security or architecture review.
• Unmanaged AI agents. An internal agent receives access to systems such as a CRM, collaboration platform, or database without clear ownership, permissions, or operating controls.

Shadow AI creates risk because the organization may not know what AI systems are operating, what information they receive, what they can access, or how their outputs are being used.

Employees may submit customer information, source code, internal documents, financial data, credentials, or other sensitive material to AI services that the organization has not reviewed. Without visibility into the service and its configuration, security teams may not know how that information is stored, processed, retained, or protected.

Ungoverned AI can make it difficult to determine what information was shared, which service processed it, who authorized the activity, and whether sufficient records exist for an audit or investigation.

The exact consequences depend on the data, jurisdiction, industry, and service involved, but a lack of records and ownership makes compliance questions harder to answer.

The risk increases when AI moves beyond text prompts and receives access to enterprise systems. An unreviewed integration or agent may have permissions to retrieve data, call APIs, update records, or interact with multiple services.

If ownership and access boundaries are unclear, the organization may also struggle to determine what the AI system can do or remove access when it is no longer needed.

An unmanaged AI tool may use incomplete context, generate inaccurate information, or influence decisions without suitable review. Different teams may also adopt different tools and workflows for the same task, making results and operating practices inconsistent.

Shadow AI can therefore affect more than cybersecurity. It also creates risks for data management, compliance, software architecture, operational accountability, and the reliability of business processes.

Managing Shadow AI starts with visibility into which AI tools, integrations, features, and agents are already in use and what business data or systems they interact with.

A high-level approach includes:

1. Identify AI usage. Build visibility into AI applications, integrations, embedded features, and agents used across the organization.
2. Understand data and access. Determine what information they receive and which applications, APIs, or resources they can reach.
3. Assess risk. Evaluate use based on factors such as data sensitivity, permissions, business impact, and regulatory requirements.
4. Define clear rules. Specify which uses are permitted, which require review, and what data should not be provided to particular tools.
5. Provide sanctioned alternatives. Give teams approved options that address the needs driving adoption of other tools.
6. Review usage over time. Revisit AI systems, permissions, ownership, and controls as tools and workflows change.

Blanket blocking alone does not address the underlying demand for AI or guarantee visibility into how employees use it. Practical alternatives and clear review rules are part of maintaining control.