Shadow AI Explained: Risks, Examples, and How Businesses Can Stay Safe

Shadow AI Explained: Risks, Examples, and How Businesses Can Stay Safe

Shadow AI refers to employees using AI tools at work without approval or oversight from their organization's IT, security, or compliance teams. It can happen when someone pastes a customer contract into a chatbot for a summary, a developer uses a personal AI coding account, or a marketing team signs up for an AI image generator without going through the company's normal approval process. These actions may feel like harmless productivity shortcuts, but they can create visibility, data-protection, and security gaps for an organization.

This article explains what shadow AI actually is, why it has spread quickly the real risks it creates some documented examples and practical steps businesses can take to manage it without simply banning AI outright.

What Is Shadow AI

Shadow AI means using AI tools, applications, or AI-powered features without the organization's approval or appropriate oversight. It is closely related to shadow IT, where employees use unapproved software or services to get their work done.

One important difference with shadow AI is how information may be processed by the AI service. When an employee submits a file, prompt, or other company information to an external AI tool, the data may be processed and retained according to that provider's terms and settings. The exact handling varies by provider and plan, which is why organizations need to understand the data practices of the tools they approve.

• Common examples of shadow AI, in a company include:
• Employees putting internal documents into free chatbot tools to get summaries or rewrites
• Developers using AI coding helpers on personal accounts instead of the ones the company says are okay
• Sales or HR workers putting spreadsheets into AI tools for analysis
• Teams linking AI browser extensions to their email or calendar without checking security

Departments signing up for special AI apps using a company credit card with no one looking at it through procurement or legal processes

Why Shadow AI Has Spread So Fast

AI tools are easy to sign up for often free to try and immediately useful. An employee does not need approval from IT to open a browser tab and start using a chatbot. That low barrier to entry is why adoption has outpaced governance.

IBM's 2025 Cost of a Data Breach Report found that many organizations were still developing their approach to AI governance. The report also examined security incidents involving shadow AI, highlighting how unauthorized AI use can create additional risks when organizations lack clear policies and controls. Employees are often motivated by productivity rather than by an intention to bypass security, which makes clear guidance and approved alternatives important.

The result is a gap. Security teams are still working out how to evaluate and approve AI tools while employees have already moved on to using several of them.

The Real Risks of Shadow AI

Data Leakage and Loss of Control

The biggest danger is that important information leaves the companys control the second it is typed into an AI tool that hasn't been approved. This can be customer records, source code, financial data, legal contracts or internal strategy documents. Soon as this data goes to a third-party AI system the company usually has no way to know what happens to it after that.

Higher Breach Costs

IBM's 2025 Cost of a Data Breach Report examined the costs and security implications associated with shadow AI. Its findings indicate that unauthorized AI use can increase the risk of sensitive data exposure and add to the challenges organizations face when responding to a breach. Because the report's figures are based on a specific research sample and methodology, businesses should not treat them as a universal cost for every shadow AI incident.

Weak or Missing Access Controls

A big part of the damage is caused by gaps, in oversight. IBM found that ninety‑seven percent of organizations that had a security incident did not have proper AI access controls. In words most companies that faced an AI‑related breach did not put basic safeguards in place before the incident occurred.

Compliance and Regulatory Exposure

Shadow AI can create additional compliance risk, particularly in regulated industries. Uploading personal, financial, health, or other protected information to an external AI service may conflict with an organization's legal, contractual, or internal requirements, depending on the circumstances. Regulations and industry rules such as the EU AI Act, GDPR, HIPAA, and financial-sector requirements can impose different obligations, so businesses should assess AI use with their legal, privacy, and compliance teams rather than assuming that a particular tool is automatically compliant or non-compliant.

Inaccurate or Unverified Outputs Being Used in Decisions

Shadow AI is not about data leaving the company. It is also, about unverified AI output coming in. If an employee uses a tool to draft a report write code or analyze numbers and no one checks that output carefully mistakes can quietly work their way into real business decisions, customer communications or shipped code.

Expanded Attack Surface

Unapproved AI tools can expand an organization's security and privacy exposure, particularly when they request access to email, files, calendars, source code, or other connected services. Security teams have less ability to assess, monitor, or restrict those integrations when they are not aware that employees are using them.

Real-World Examples of Shadow AI Problems

Samsung employees used ChatGPT for work tasks. In 2023, Samsung reportedly restricted employees' use of generative AI services after employees entered sensitive information, including source code, into ChatGPT. The incident became a widely cited example of how employees can unintentionally expose confidential company information when using consumer AI services without clear controls.

Italy’s data protection authority, the Garante temporarily restricted access to ChatGPT within the country in 2023 over concerns about how user data was being collected and processed. Access was later restored after changes were made. Italy’s case reminds us that shadow AI is not a company‑level risk; it can also become a regulatory and national‑level issue when data protection questions are not addressed upfront.

Engineering teams and AI coding tools: Developers are increasingly using AI coding assistants to generate, explain, and modify code. Organizations should make sure developers understand which coding assistants are approved and what information can be submitted. Source code may contain proprietary business logic, credentials, configuration details, or other sensitive information that should not be shared with an unapproved service.

These examples share a common thread. In each case, the AI tool itself was not necessarily the problem. The absence of a clear policy, review process, or approved alternative is what allowed the risk to grow.

How Businesses Can Stay Safe

A blanket ban on AI may be difficult to enforce when employees can easily access AI features through websites and everyday software. A more practical approach is to provide approved AI tools, establish clear rules for sensitive information, and put appropriate monitoring and security controls in place.

1. Find Out What Is Already Being Used

Before writing any policy businesses must first understand what AI tools employees are already using. This means looking at network and browser activity reviewing expense reports for payments related to AI subscriptions and talking directly with teams, about the tools they depend on. You cannot manage something you do not see.

2. Create a Clear, Practical AI Usage Policy

A useful AI policy explains what types of data can never be entered into external AI tools, which tools are approved, who to contact to request a new tool, and what the consequences are for violations. The policy works best when it is written in plain language, not legal jargon, so employees actually read and understand it.

3. Offer Approved Alternatives

Employees may turn to consumer AI services when their organization does not provide an approved alternative. Businesses should evaluate AI products that offer appropriate security, privacy, administrative, and data-governance controls. Depending on the provider and plan, business offerings may provide stronger controls than consumer versions. Giving employees a practical approved option can reduce the incentive to use unapproved services.

4. Put Technical Controls in Place

Policies alone are not enough. Companies can use security controls such as data loss prevention (DLP) tools and cloud access security broker (CASB) technologies to identify and, where appropriate, restrict the transfer of sensitive information to unauthorized services. These controls can supplement employee training and policy enforcement.

5. Train Employees, Don't Just Warn Them

A single email about risks of intelligence rarely changes how people act. Regular training that gives examples, like the Samsung situation helps workers see why the rules are there instead of thinking they are just random rules. The training should also teach how to recognize AI results that need checking before using them.

6. Review Vendor and Third-Party AI Use

Shadow AI does not only come from employees. Shadow AI can also come from vendors and software partners, who may embed AI features into tools your business already uses. Reviewing vendor contracts and asking questions, about how partner tools use Shadow AI and handle data is an important part of closing this gap.

7. Set Up Ongoing Monitoring and Audits

AI tool adoption shifts all the time one review does not suffice. Regular audits of AI tool usage along, with a process for employees to request new tools keep governance realistic and current rather than becoming out of date after only a few months.

Shadow AI vs. Shadow IT: What's Different

Shadow IT usually means an employee using an app to store or share files, such as a personal cloud drive. I see that Shadow IT is common. The data sat outside company control but the data stayed roughly the same. Shadow AI raises the stakes because the data does not just sit there. The data gets. In some cases the data is used to train or improve the underlying model. A leaked file, in a folder is a serious problem. A confidential document processed by a third‑party AI system with retention practices is harder to contain and harder to reverse.

Questions Businesses Often Ask

Should we just block all AI tools on the company network? Probably not. Blocking a few websites doesn’t stop people who are determined to use AI.. It ignores the fact that many AI features are already built into everyday software—like web browsers, email programs and productivity apps. A smarter strategy includes rules, approved tools and regular monitoring. Relying on blocks isn't enough.

Is shadow AI a problem for big companies? No. Mid-sized businesses face the same risks, maybe even more. They often don’t have security teams to watch what’s happening. A five-person marketing agency working with client data can be just as vulnerable to a data leak from an AI tool as a huge corporation. In fact they might be more at risk because no one is paying attention.

Does using shadow AI mean employees are doing something ? Not really. Most employees turn to these tools to save time or get work done faster. They may not know that their inputs could be stored, analyzed or shared in ways that matter. It’s better to see this as a gap, in communication and policy not a punishment issue. Handling it that way usually leads to outcomes over time.

The Bottom Line

Shadow AI exists because AI tools are useful and easy to access, while employees are under pressure to work efficiently. The main risk comes from using these tools without appropriate visibility, review, or security controls. Businesses can reduce that risk by providing approved AI tools, clearly explaining what information cannot be shared, training employees, and monitoring AI use where appropriate. The goal should be responsible AI adoption rather than simply blocking every AI service.

Sources

• IBM — Cost of a Data Breach Report 2025
• Garante per la protezione dei dati personali (Italy)
• NIST — AI Risk Management Framework
• European Commission — EU AI Act