Shadow AI

Fulcrum Digital

October 5, 2026

Quick Answer

Shadow AI is the use of artificial intelligence tools, models, features, or agents for work without sufficient organizational approval, review, or visibility. It can include employees using public AI assistants through personal accounts, teams adopting AI applications independently, developers connecting to external models through APIs, or AI capabilities being used inside existing software without appropriate review.

Shadow AI is closely related to shadow IT, but AI introduces additional concerns because information may be entered into models, generated outputs may influence business decisions, and AI agents can access enterprise systems or data. The underlying issue is whether the organization knows how AI is being used and can apply the appropriate controls.

What is shadow AI?

Shadow AI occurs when AI is used for business purposes outside an organization’s established approval, security, governance, or oversight processes. The technology itself does not have to be unsafe or prohibited. Even a widely used commercial product can become shadow AI when it is used in ways the organization has not reviewed or authorized.

A common example is an employee using a personal generative AI account to summarize an internal document. A developer might connect an external large language model to an internal application through an API, while a licensed software platform might introduce an AI feature that accesses enterprise information before its permissions and data handling have been reviewed. AI agents can create another form of shadow AI when they operate without the controls required for AI agent governance.

This means an organization cannot identify shadow AI solely by maintaining a list of unauthorized applications. They also need visibility into how approved applications are being used, which AI capabilities are enabled, and what information or systems those capabilities can access.

Why do employees use shadow AI?

Employees usually turn to shadow AI because an accessible AI tool helps them complete a task more quickly or effectively than the approved options available to them. Consumer AI services can often be used immediately, without requesting new software or waiting for formal approval.

Shadow AI may appear because no approved option exists for a particular task. It can also emerge when sanctioned tools are difficult to access, poorly suited to the work, or more restrictive than alternatives employees can use independently. Curiosity and experimentation also play a role when people are testing whether AI can reduce repetitive work or help with an unfamiliar task.

The reason behind the use matters when organizations respond. A policy may prohibit unsafe behavior, but it does not necessarily remove the problem that caused an employee to seek another tool. At an enterprise level, widespread shadow AI can also indicate an early point on the AI maturity curve, where employee experimentation is moving faster than formal programs and visibility.

What risks does shadow AI create?

The most significant shadow AI risks come from losing visibility into what AI is being used, what information it can access, and how its outputs affect business processes. Sensitive or regulated information may enter services whose retention, processing, or contractual conditions have not been reviewed, creating concerns over data security, privacy, intellectual property, and AI compliance. Activity through personal accounts may also sit outside enterprise logging and access controls.

Risk also extends beyond the data entering the system. Employees may rely on inaccurate or incomplete AI-generated outputs without appropriate review. An unofficial AI agent can introduce greater exposure if it holds credentials, accesses enterprise applications, retrieves data, or performs actions using permissions that were never evaluated for that purpose.

Nevertheless, shadow AI can still reveal useful information about how employees work. Repeated use may expose an unnecessarily difficult process, an unmet technology need, or an approved tool that people are avoiding. That does not make the unofficial use safe, but it can help the organization understand why the behavior emerged and whether the underlying workflow deserves attention.

Shadow AI vs shadow IT: what is the difference?

Shadow IT covers technology used outside approved IT processes, while shadow AI refers specifically to unmanaged or unapproved use of artificial intelligence. Shadow AI overlaps with the broader shadow IT problem, but the way AI processes information, generates outputs, and increasingly takes action creates additional considerations.

Area	Shadow IT	Shadow AI
Scope	Unapproved software, devices, services, or infrastructure	Unapproved or unmanaged AI tools, models, features, APIs, or agents
Data concern	Where information is stored, transferred, or accessed	What information is provided to AI and how it may be processed or retained
Output	Usually provides a conventional software function	Can generate new content, recommendations, analysis, code, or decisions
Embedded use	Often identifiable as a separate application or service	Can exist as an AI feature inside software the organization already uses
Autonomous activity	Usually depends on direct human operation	AI agents may be able to access systems or perform actions on a user’s behalf

Existing shadow IT discovery and security practices can identify part of the problem. They may not, however, reveal an AI feature inside an approved platform, an external model invoked through application code, or an agent operating with enterprise credentials.

How can organizations identify and manage shadow AI?

Effective shadow AI governance starts with visibility into how AI is actually being used, followed by controls that distinguish acceptable use from activity that introduces unacceptable risk. Blocking well-known AI websites alone will not reveal AI embedded in existing software, accessed through APIs, or deployed as an agent.

Shadow AI detection can draw on network and browser activity, identity and access information, SaaS administration, endpoint monitoring, procurement records, API usage, and inventories of internally developed AI systems. No single source captures every form of AI use, so organizations need enough visibility to understand which capabilities are active, who is using them, and what systems or information they can reach.

A clear AI governance policy can define permitted uses, information-handling requirements, approval processes, and accountability for exceptions. Organizations also need practical approved alternatives when legitimate work repeatedly depends on an unofficial tool. Once AI is formally in production, enterprise AI monitoring can maintain visibility into its behavior, while agents require additional controls around permissions, credentials, actions, and oversight through AI agent governance.

Continue Exploring

Shadow AI becomes difficult to control when adoption moves faster than the organization’s ability to see and govern it. Establishing what is already being used can help separate immediate risks from legitimate business needs, while a readiness assessment can show whether the organization has the data, process, technology, and talent foundations to support a safer path forward.

If you need help assessing existing AI use, identifying governance and readiness gaps, or establishing the controls needed to support enterprise AI more responsibly, Fulcrum Digital would be happy to connect.

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