What is Shadow AI?
December 11, 2025
Table of contents
Key takeaway
Shadow AI (also known as Shadow Artificial Intelligence or BYOAI) refers to the unauthorized use of AI tools, models, and features within an organization without IT approval or oversight. While often driven by employees aiming to increase productivity, it introduces significant risks—specifically data leakage and compliance violations—because security teams lack visibility into where sensitive data is flowing.
Video Overview
Shadow AI occurs when employees turn to external AI tools to work faster, leaving security teams unaware of where data is going or how the tool handles it. Unlike traditional software adoption, this often happens instantly inside the browser or via personal accounts.
The scale of the problem is outpacing governance:
- 91% of AI tools used in companies are completely unmanaged.
- 75% of employees now bring their own AI (BYOAI) into work (Source: Microsoft Work Trend Index).
As the name implies, Shadow AI grows in quiet corners. It could be a marketer plugging brand data into a new copy generator, an engineer testing a personal code assistant, or a team enabling AI features hidden inside a SaaS app. None of it is malicious. However, all of it attracts consequences.
In this article, you’ll learn what shadow AI is, how it differs from shadow IT, why it happens, the risks it introduces, and how organizations can manage AI use safely without slowing innovation.
Shadow AI vs Shadow IT: What's the Difference?
Shadow AI and shadow IT both describe technology used without approval, but Shadow AI introduces new risks that traditional security programs were never designed to catch.
For most organizations, Shadow IT is familiar territory (unapproved software/hardware). Shadow AI is the new frontier—harder to see, harder to control, and deeply tied to the way modern employees work. The core difference is simple: Shadow AI involves unauthorized AI tools, models, and embedded AI features, while shadow IT involves unauthorized software, hardware, or cloud services.
Below is a clear comparison to ground the distinction.
Understanding the difference matters. Shadow IT can be tackled with long-standing visibility tools and network controls. Shadow AI, however, needs a new approach because what employees type into an AI model can instantly become training data.
Platforms like Cyberhaven help by showing exactly where data comes from, how it is used, and where it goes (Data Lineage). This makes it easy to see if someone is sharing sensitive customer information, private company code, or public content, giving security teams clear visibility and control while allowing employees to work without unnecessary interruptions.
Is ChatGPT Shadow AI?
A common question appears here. The answer depends on governance:
- It is Shadow AI when employees use the free, public version of ChatGPT without approval, or paste sensitive data into it outside of sanctioned policies.
- It is Sanctioned AI if the organization has reviewed, approved, and set guardrails around its use (e.g., ChatGPT Enterprise).
How Does Shadow AI Happen?
Shadow AI rarely begins with a dramatic misstep. It starts with small choices that feel harmless in the moment. Employees want to move faster, reduce manual work, or solve a problem independently.
Imagine a product manager rushing to prepare a board update. She drops a spreadsheet of customer feedback into a public chatbot to rewrite the insights in a cleaner format. It works, so she does it again the next quarter. A teammate notices and starts using the same tool to summarize support tickets. Within weeks, half the team is quietly using unapproved AI tools to speed up routine tasks.
This behavior has become common because AI tools are everywhere now. Many are free, run in the browser, or sit inside the SaaS platforms employees already rely on. Traditional monitoring often misses the subtle ways these tools weave into daily workflows.
1. Using Unauthorized SaaS AI Applications
One of the most common paths is simple: employees adopt AI apps the company has never reviewed. ChatGPT, Claude, Gemini, Midjourney, and countless niche assistants become personal productivity shortcuts. Without visibility, security teams can’t know what was uploaded, exported, or used to train an external system.
2. Enabling AI Features in Approved Apps
Shadow AI also happens inside the tools employees already use. Modern SaaS platforms quietly ship AI features that can be toggled on with a single click (e.g., Copilot inside Microsoft 365, AI summarization in Slack, Notion AI). These features often blend into the interface, making them hard for IT to detect. What feels like a harmless enhancement may result in sensitive documents being processed by third-party models without governance.
3. Creating Personal AI Instances
A growing number of employees are experimenting with local LLMs, personal cloud deployments, or lightweight AI agents. Engineers spin up open-source models; analysts host personal prompt tools. This emerging “shadow AI economy” removes all organizational visibility. When these models ingest source code or customer records, security teams have no way to trace where that data goes next.
4. Lack of Awareness and Governance
Finally, shadow AI thrives when guardrails are unclear. Employees often don’t know that copying data into an AI model can violate GDPR or create new compliance obligations. They may assume that if a tool is free, it’s safe. Good intentions become unmanaged risk. This is where data-centric platforms like Cyberhaven help, giving teams visibility into how data flows into AI tools and where exposures begin.
What Are the Risks of Shadow AI?
The risks of shadow AI come from one core problem: organizations cannot protect the data they cannot see. When employees use unmanaged AI tools, sensitive information moves into systems with unknown retention policies and unknown training pipelines.
Data Leakage and Exposure
Data loss is the most immediate risk. When employees paste sensitive information into prompts, that data may be stored, logged, or used to train external models.
- Real World Example: This happened to Samsung, where developers inadvertently leaked source code into ChatGPT while seeking debugging help.
- The Fix: Cyberhaven closes this gap by tracing data flows in real time, flagging when sensitive files reach external AI tools.
Compliance and Regulatory Violations (GDPR, EU AI Act)
Shadow AI can trigger compliance issues instantly. Sensitive data shared with unvetted models may violate GDPR, HIPAA, SOC 2, or contractual obligations. Under GDPR alone, exposure can lead to fines of up to €20 million. With new regulations like the EU AI Act, organizations must demonstrate visibility and governance over their AI use. Shadow AI removes that visibility, creating regulatory blind spots.
Security Vulnerabilities and Attack Surface
Unauthorized AI tools often come with OAuth permissions and API access that IT never reviews. Over-permissioned OAuth scopes are a known problem; AI tools frequently request broad access to files, mailboxes, or shared drives. Unmonitored endpoints create entry points for attackers.
Information Integrity and AI Hallucinations
Shadow AI introduces risks tied to integrity, not just confidentiality. AI tools generate outputs that can be biased, inaccurate, or entirely fabricated.
- Real World Example: Two New York lawyers submitted a court filing containing fake ChatGPT-generated citations, resulting in public sanctions and a $5,000 fine.
Reputational Damage
Companies rely on trust. The Sports Illustrated controversy, where AI-generated authors were presented as real journalists, triggered backlash. When employees quietly experiment with AI in customer-facing contexts (like Uber Eats using AI images that misrepresented food), brands risk long-term damage to credibility.
Increased Costs and Resource Waste
Shadow AI fuels unnecessary spending through the "Shadow AI economy." Teams adopt redundant tools, resulting in duplicate subscriptions. IT then spends resources investigating incidents and remediating exposures. Visibility and governance help organizations consolidate tools and reduce waste.
How to Detect Shadow AI
Detecting shadow AI starts with improving visibility. You cannot control what you cannot see. The fastest path to clarity is combining network visibility, SaaS discovery, usage monitoring, and data-centric protection.
- Cloud Access Security Brokers (CASB): Useful for monitoring cloud traffic and flagging access to unapproved AI services. However, they often miss AI features embedded within approved SaaS platforms.
- Network Traffic Analysis: Reveals patterns like repeated outbound requests to AI domains. While useful, this struggles with encrypted traffic and browser-based tools.
- SaaS Discovery Tools: Platforms (like Grip) identify new apps based on OAuth grants and login patterns. Essential for spotting when AI services request broad access to documents.
- Data Detection and Response (DDR) / Modern DLP: This is the most effective method because it focuses on the data itself. Traditional DLP often misses context, but Cyberhaven traces data paths in real time, showing exactly when sensitive content is pasted into an AI model or uploaded to an unapproved service.
- User Behavior Analytics (UBA): Spots unusual patterns, such as sudden spikes in clipboard usage or repeated uploads to new AI domains.
- OAuth Application Monitoring: Monitors permissions. When combined with DLP visibility, it provides a complete picture of how shadow AI begins.
- Regular AI Audits and Surveys: Anonymous surveys reveal where teams rely on personal tools, guiding approval processes.
How to Prevent and Manage Shadow AI
Shadow AI is avoided not only by restriction but also by clarity, education, and visibility. The most effective strategy blends governance with enablement.
Establish Clear AI Governance Policies
Governance is not about creating a rulebook nobody reads. It is about offering practical direction. Policies should clarify:
- Which AI tools are approved.
- Which data types (e.g., PII, Code) are strictly off-limits for AI ingestion.
- When humans must remain in the loop (Human-in-the-Loop).
Provide Approved AI Alternatives
Shadow AI thrives where sanctioned options are missing. Give teams vetted AI tools (e.g., Microsoft Copilot, internal secured LLMs). When "official" tools are available and effective, the temptation to use shadow tools declines.
Educate Employees on AI Risks
Employees rarely intend to create risk; they simply underestimate it. Training should include real examples (Samsung leaks, hallucinated citations) to make the risk tangible. When employees understand why a policy exists, they are more likely to follow it.
Implement AI Discovery and Monitoring Tools
Visibility is the foundation. Start with SaaS discovery and endpoint telemetry. Look for prompts sent to public LLMs and OAuth grants to unvetted apps. Tag AI-powered features separately from standard app functionality to distinguish legitimate use from hidden GenAI capabilities.
Create an AI Approval Process
Create a lightweight intake form for requesting new AI tools. Ask for the tool’s purpose and data requirements. If the process is fast and transparent, fewer users will bypass security.
Use Data Lineage to Control Data Exposure
Instead of blocking every model, control the data. Cyberhaven tracks data lineage in real time, showing exactly when employees copy sensitive information into an AI prompt. This allows you to stop the specific risky action (e.g., pasting source code) without blocking the tool entirely for safe uses.
Deploy Secure AI Sandboxes
Secure AI sandboxes let teams test prompts and evaluate models without exposing enterprise data. This balances safety and freedom, allowing for innovation without the risk of unmanaged cloud accounts.
Foster an Open Communication Culture
Replace fear with openness. Encourage teams to share which AI tools help them work better. When communication is open, shadow AI surfaces earlier, allowing security teams to guide employees toward safe alternatives.
Shadow AI Examples Across Teams and Workflows
Shadow AI doesn’t appear as dramatic, high-tech rogue systems. It starts with everyday tools.
Marketing Teams Using AI Writing Tools
Marketing teams rely on tools like ChatGPT, Claude, Jasper, or Gemini for content production.
- The Scenario: Copywriters pasting draft content containing confidential client details or uploading customer persona data.
- Real World Context: Thousands of ChatGPT conversations unexpectedly became visible on Google search due to the "share" feature, exposing internal project notes.
- Primary Risks: Data retention, IP exposure, compliance issues, and hallucinated outputs shaping public messaging.
Developers Using Code Completion (The "Copilot" Risk)
Tools like GitHub Copilot, CodeWhisperer, and TabNine are game-changing but risky if unauthorized.
- The Scenario: Copilot auto-suggesting code snippets that include leaked API keys, or developers pushing AI-generated code with hidden vulnerabilities.
- Primary Risks: Leaky code, insecure dependencies, hallucinated logic, and exposure of internal secrets to the model provider.
Customer Service Teams Using Unauthorized Chatbots
Support staff test AI assistants to speed up ticket replies.
- The Scenario: Support reps pasting customer PII (Personally Identifiable Information) or order data into public chatbots to generate summaries.
- Primary Risks: PII exposure, social engineering vulnerabilities, and inconsistent remediation actions that never get logged.
Sales Teams Using AI for Prospecting
Sales reps use tools like Apollo AI or LinkedIn AI drafts for outreach.
- The Scenario: Uploading CRM data to generate personalized outreach or using unapproved enrichment bots.
- Primary Risks: Regulatory violations (GDPR, CCPA), poor data hygiene, and inaccurate insights impacting deals.
HR Using AI for Resume Screening
Recruiting teams test AI tools to evaluate applicants.
- The Scenario: Feeding resumes or interview transcripts into consumer-grade AI tools to generate fit scores.
- Primary Risks: Bias, discrimination claims, improper PII processing, and decisions made on hallucinated assessments.
Finance Teams Running Unapproved ML Models
Analysts run predictions through cloud notebooks.
- The Scenario: Uploading sales forecasts or payroll data into external ML tools for anomaly detection.
- Primary Risks: Revenue model leakage, regulatory breaches, and decisions driven by unverifiable models.
AI Browser Extensions
Extensions like GrammarlyGO or Perplexity seem harmless but have broad access.
- The Scenario: Extensions capturing internal dashboards or sending browser content to third-party APIs.
- Primary Risks: Silent data capture, session hijacking, and bypassed network controls.
Unapproved RAG Pipelines (Retrieval-Augmented Generation)
Developers embed LLMs into apps without security review.
- The Scenario: Vector databases storing sensitive embeddings or engineers connecting AI agents to internal knowledge bases.
- Primary Risks: Embedding leaks, unsecured vector stores, and ungoverned access paths.
The Future of Shadow AI Management
Shadow AI is not slowing down. As AI tools integrate into everyday work, security teams need more than a "lock and key" approach. The future belongs to organizations that treat AI governance as a living system.
The regulatory landscape is changing quickly. The EU AI Act is setting the tone, and similar frameworks are surfacing globally. Compliance will no longer be a box to tick; it will shape how companies build and monitor AI systems.
We will also witness the rise of a true shadow AI economy. Employees will experiment, and creative uses of AI will appear before IT can catch up. Instead of fighting this, smart organizations will study it, map the curiosity, and design safer paths.
Cyberhaven’s role sits at this intersection. As companies balance bold ideas with responsible guardrails, visibility becomes the foundation. When you can see what data moves where, who is touching it, and how AI tools interact with it, the future feels manageable.
Conclusion
Shadow AI is the natural outcome of employees trying to move faster. The challenge is not the curiosity itself, but the lack of visibility and the quiet data exposure that happens when AI slips into workflows without guidance.
Prevention is not a single tool. It is a combination of governance, education, monitoring, and a culture that invites people to ask before they experiment. With the right guardrails, AI becomes an advantage instead of a liability.
If you want to understand where your data is going and how AI tools interact with it, explore how Cyberhaven’s data protection solutions help organizations manage shadow AI safely. You can also continue learning through our Infosec Essentials series, including guides on AI in cybersecurity and modern access control.
Frequently Asked Questions About Shadow AI
What is the problem with shadow AI?
Shadow AI creates silent risk. Employees use AI tools without oversight, which means sensitive data can be exposed, stored externally, or used to train third-party models. The danger is not only the data leaving the organization, but the fact that leaders often discover it long after the exposure has happened.
How common is shadow AI in organizations?
Very common. Studies show that more than 90% of AI tools used inside companies today are unmanaged, and three out of four employees already use "Bring Your Own AI" (BYOAI) tools. Most companies only uncover shadow AI after an incident or during audits.
Can shadow AI be permanently eliminated?
Not realistically. Employees will always explore faster tools. The goal is not elimination; it is safe enablement. Clear policies, strong discovery capabilities (like Data Lineage), and approved alternatives help keep curiosity inside safe boundaries without stifling innovation.
How does shadow AI affect data security?
It increases the likelihood of data leakage, unintentional sharing of sensitive information, and unmonitored model training. Once information enters an external model, it may be stored, logged, or used to improve that model. This creates exposure pathways that traditional security controls cannot see.
What industries are most at risk from shadow AI?
Highly regulated sectors feel the pressure first (Healthcare, Finance, Legal, Government). However, fast-moving fields like Marketing, Engineering, and Product Development are equally vulnerable because employees in these roles adopt new tools most aggressively.
What role does DLP play in preventing shadow AI?
Data Loss Prevention gives security teams visibility. However, traditional DLP struggles with AI context. Modern Data Detection and Response (DDR) solutions like Cyberhaven extend this by tracking data lineage, user intent, and AI usage patterns. This makes it easier to detect risky behavior—like pasting code into a chatbot—and intervene before an exposure becomes a breach.