Shadow AI: definition, risks, and enterprise controls

Shadow AI refers to the use of artificial intelligence tools by employees without approval, supervision, or sufficient visibility from IT, security, privacy, or procurement teams. It can involve a public generative assistant, a browser extension, a SaaS tool with embedded AI, or an agent connected to internal systems.

The phenomenon extends Shadow IT, but it raises the risk level. Data entered into a prompt can include code, personal information, contracts, screenshots, or strategic information that is difficult to recover once transmitted.

Why Shadow AI spreads

Shadow AI rarely starts with malicious intent. It usually appears when business needs move faster than internal approval processes.

Employees adopt these tools to:

• Summarize documents or meetings.
• Generate code, scripts, or queries.
• Draft emails, sales proposals, or marketing assets.
• Analyze files, contracts, or data tables.
• Automate tasks with AI agents.

When the company does not provide a clear approved alternative, teams often choose the most accessible tool, even if it has not been reviewed.

Shadow AI vs Shadow IT

Criteria	Shadow IT	Shadow AI
Object	SaaS apps, storage, collaboration, messaging	AI assistants, generative models, agents, AI extensions
Exposed data	Files and data stored in an application	Prompts, files, screenshots, code, personal data
Detection	Spend, SSO, SaaS logs, network traffic	Harder: personal accounts, browser usage, APIs, extensions
Key risk	Loss of visibility and expanded attack surface	Data leakage, prompt reuse, hallucinations, non-compliance
Framework	GDPR, NIS2, DORA depending on context	GDPR, EU AI Act, NIS2, DORA, internal AI policies

The central difference is the nature of the data. An AI tool can absorb unstructured and sensitive information in a very short interaction, without a purchase order, contract, or trace in the usual systems.

Main risks

1. Confidential data leakage

A prompt can contain source code, a contract clause, customer information, an HR file, or a commercial strategy. If the tool has not been approved, the company may not know where the data is processed, how long it is retained, or whether it can be used to improve the service.

2. Regulatory non-compliance

Shadow AI complicates GDPR and EU AI Act obligations. Without a usage register, it becomes difficult to prove the processing purpose, legal basis, data minimization, human oversight, or risk classification. For organizations subject to NIS2 or DORA, the lack of control over digital suppliers also becomes a resilience issue.

3. Poorly controlled decisions

When AI helps rank candidates, analyze customer files, or produce recommendations, the company needs to know whether the decision remains human, whether bias is monitored, and whether outputs can be explained. Shadow AI weakens that traceability.

4. Cyber risk and browser extensions

AI browser extensions can read the content of web pages, internal interfaces, or SaaS tools. Some request very broad permissions. Without an inventory or clear policy, they create a security blind spot.

Examples of Shadow AI

• A lawyer copies a confidential contract into a chatbot to obtain a summary.
• A sales team uses a personal AI tool to enrich prospect files.
• A developer sends proprietary code to a debugging assistant.
• A manager uses AI to compare candidate profiles without HR approval.
• An AI extension automatically summarizes CRM pages containing customer data.

These use cases can be useful. The problem is the absence of a framework, not AI itself.

How to regain control

An effective strategy avoids blanket blocking. It follows three steps.

1. Discover

Map the AI tools actually used: visited domains, OAuth connections, extensions, spend, tools declared by teams, and applications detected in the browser.

2. Classify

Assess each use case based on the data processed, department involved, process criticality, vendor, hosting location, and EU AI Act risk level.

3. Govern

Create a clear usage policy: prohibited data, approved tools, use cases requiring validation, human oversight rules, user training, and incident processes.

This approach aligns with enterprise AI governance: make usage visible, assign responsibilities, and allow innovation within a controlled framework.

Avanoo's role

Avanoo helps enterprises discover the SaaS and AI tools actually used across the organization, including those that bypass procurement or SSO. Teams can identify risky usage, prioritize actions, engage employees, and build an approved tool catalog.

Shadow AI is not only a security problem. It is a signal: teams need AI, but they also need a simple framework to use it without exposing company data, compliance, or trust.