What Is Shadow AI And How To Stay In Control

What is Shadow AI? 

Shadow AI refers to the unsanctioned use of artificial intelligence tools by employees without IT approval or security oversight. Unlike traditional software, AI tools are unusually easy to access, employees can use ChatGPT, Claude, or Gemini through a simple browser tab, requiring no technical expertise or installation.  

The Zendesk CX Trends Report 2025 reveals that nearly 50% of customer service agents use Shadow AI, and usage has increased by 250% year-over-year in some industries. This includes unsanctioned generative AI tools and software, unauthorized AI service assistants or unapproved AI-powered productivity tools. 

Shadow AI vs. Shadow IT 

Shadow AI and shadow IT both describe technology that enters organizations without formal approval, but they differ in fundamental ways. Shadow IT is the broader, older pattern: any software, device, or service that teams adopt without the IT department signing off.  

Shadow AI is a newer, narrower trend inside that bigger story. It is about employees using AI tools such as ChatGPT, Claude, or embedded GenAI features without oversight from IT or data governance teams. 

Shadow IT is mainly an access and infrastructure issue. Shadow AI is a generative AI security risk that focuses on how models handle data and influence outcomes, often in ways that are harder to predict or audit. 

 

Aspect	Shadow AI	Shadow IT
Definition	Use of AI tools and technologies without approval from IT or data governance teams	Use of unapproved IT software, hardware, or infrastructure on the enterprise network
Typical technology	Public chatbots, GenAI features in SaaS, AI assistants, AI plug-ins	File sharing, messaging apps, personal SaaS, unmanaged devices
Adoption pattern	Often adopted by individual employees to boost productivity and convenience	Often adopted by teams to solve IT bottlenecks or fill tool gaps
Governance and compliance	Lacks model, data, and usage oversight from IT or data teams	Lacks central IT or organizational oversight for the tools themselves
Primary risks	Data privacy, AI bias, opaque decisions, compliance violations, unexpected security gaps	Data breaches, regulatory non-compliance, network and access security issues
Cultural impact	Encourages innovation but can create inconsistent data usage and decision-making	Promotes agility but can fragment the IT environment and deepen silos
Example	Support team uses an unapproved AI tool to analyze customer sentiment	Employee uses an unapproved cloud storage service to share work files

Common Sources of Shadow AI 

Shadow AI rarely appears as one big project. It usually enters organizations gradually through everyday tools and shortcuts. IBM’s 2025 Cost of a Data Breach report found that 13% of organizations have already experienced breaches linked to AI models or applications, and 97% of those did not have proper AI access controls in place, which shows how quickly unofficial AI usage can turn into real incidents.  

Shadow AI often lives in the browser, inside plug-ins, or hidden in “smart” features inside tools that staff are already using. As a result, data can flow to external AI services without ever passing through the checkpoints that IT and security teams rely on. In practice, some of the most common sources of Shadow AI include: 

• Generative AI tools such as ChatGPT, Copilot, or Gemini used for drafting content, coding assistance, or quick summarization.  

• Browser extensions and plug-ins that automatically send text, screenshots, or page content to third-party AI APIs.  

• Embedded AI features in SaaS platforms that transcribe meetings, summarize chats, or “auto-draft” responses in external clouds.  

• Automated code assistants that learn from private repositories and may reproduce proprietary snippets in other contexts.  

Shadow AI thrives wherever productivity demands move faster than official AI governance, and where employees can adopt powerful AI tools long before secure, sanctioned alternatives are available. 

Top 3 Risks of Shadow AI 

Shadow AI often starts as small productivity shortcuts, but the way these tools handle and generate data creates a different risk profile than traditional Shadow IT. Understanding these impacts is essential for building the business case for comprehensive Shadow AI governance. 

Data leakage and security breaches 

When staff paste customer records, internal documents, or source code into unapproved AI tools, that data may be stored, logged, or used for model training outside the company’s control. This significantly increases the chance of data breaches and exposure of sensitive or proprietary information.  

Regulatory and compliance violations 

Unsanctioned AI use can easily breach GDPR, sector regulations, NDAs, or internal policies, because confidential data is processed by external services with no formal review, contracts, or audit trail. When incidents occur, the lack of logging and governance makes it hard to show compliance or investigate what went wrong.  

Inconsistent customer experience 

Outputs from unvetted models can be biased, inaccurate, or manipulated, yet employees may trust and reuse them in emails, reports, hiring, or customer replies. This undermines decision quality, creates inconsistent service, and can damage brand trust if customers are affected by poor or opaque AI-generated responses.  

How to Manage and Reduce Shadow AI? 

Shadow AI will not disappear simply because organizations ban it. AI has become part of everyday work, so the only realistic way to reduce risky behavior is to provide people with safer options and clear guidelines. A practical approach combines better tools, clearer rules, and a healthier culture around AI: 

• Offer sanctioned AI tools: If you want employees to stop relying on risky, unapproved tools, you need to give them secure, useful alternatives. Enterprise AI copilots or licensed platforms typically include stronger security, logging, and admin controls than consumer apps. 

• Set clear, simple AI usage guidelines: Publish concise rules that explain which AI tools are approved, what kind of data can be used, and where the red lines are.  

• Build an AI governance framework and Center of Excellence: Create a small cross-functional group (IT, security, legal, business) to own AI policies, review new use cases, and track risks such as bias and misuse. This Center of Excellence should guide, not block, adoption. 

• Invest in AI education and a “safe to ask” culture: Train people on both the power and the risks of AI: data privacy, hallucinations, compliance, and how your approved tools work. Make it clear that using AI is supported, as long as it follows policy, and that questions are welcome. 

• Provide safe environments for experimentation: Stand up sandboxes or test tenants where teams can explore new AI workflows using non-sensitive data. This directs experimentation to controlled environments instead of uncontrolled public tools. 

• Monitor, learn, and integrate good ideas: When you find genuinely useful Shadow AI use cases, bring them into the official stack instead of shutting them down, and strengthen data access restrictions so that even if someone uses an external tool, exposure is limited. 

organizations that succeed with Shadow AI are not the ones that pretend it is not happening, but the ones that meet employees where they are, offer better tools, and make responsible AI use the easiest path.