Data Exposure Through AI Tools: A Growing Risk

Artificial intelligence tools have become part of daily work life. Employees use them to draft emails, summarise documents, write code, and analyse data. But this convenience comes with a hidden cost. Each time someone pastes internal information into a public AI tool, that data leaves the organisation's control.

The problem has grown rapidly. Studies show that 75% of employees use generative AI at work, yet only 26% of companies have an official policy in place to govern it. This gap between usage and oversight creates significant risk.

What Is Data Exposure Through AI Tools?

Data exposure occurs when sensitive information gets sent to an AI tool in ways that make it accessible to unauthorised parties. This differs from a traditional data breach where hackers break into systems. With AI tools, the exposure often happens through everyday actions: copying a customer list into ChatGPT to summarise it, pasting source code into an AI assistant to debug it, or uploading a contract to get a quick analysis.

The data can end up in several places. It might be stored on the AI provider's servers, used to train future models, or accessed by other users of the same tool. In some cases, data entered into public AI tools has been indexed by search engines, making it publicly available.

How Do Employees Accidentally Leak Data Through AI Tools?

The most common method is copy and paste. A 2025 report found that copy-and-paste actions have now surpassed file transfers as the leading method for corporate data leaving organisations. The same report showed that 77% of employees using generative AI tools paste data directly into prompts.

This happens for several reasons. Employees want to work faster. They may not realise that pasting internal data into a public AI tool puts that data at risk. The AI tool's privacy policy might allow data retention or training use. And because copy-paste leaves fewer traces than file transfers, security teams often miss these actions entirely.

When employees use personal accounts for AI tools, organisations lose all visibility. A staggering 82% of employees who use copy and paste for generative AI do so via unmanaged personal accounts.

What Real-World Incidents Have Occurred?

The most famous example involves Samsung. In 2023, employees at Samsung's semiconductor division leaked confidential data on three separate occasions while using ChatGPT. In one case, an engineer pasted source code for semiconductor equipment into ChatGPT to debug it. In another, an employee uploaded meeting notes and asked the tool to create minutes. That sensitive information became part of ChatGPT's training data, potentially accessible to other users. Samsung responded by banning ChatGPT internally, though it later lifted the ban with tighter controls.

More recently, in November 2025, OpenAI confirmed a data breach affecting its API users. The breach did not happen on OpenAI's own servers. Instead, attackers compromised Mixpanel, a third-party analytics provider used by OpenAI. The exposed data included names, email addresses, approximate location, and browser information for OpenAI API customers. Although no chat data or API keys were leaked, the incident demonstrated a critical point: even when you trust the AI tool itself, the third-party services connected to it can become weak points.

Microsoft 365 Copilot, widely adopted by businesses, has also faced security flaws. In 2025, researchers discovered a zero-click vulnerability called EchoLeak. Attackers could send a specially crafted email that tricked Copilot into exposing sensitive corporate data without any user action. Microsoft patched the flaw and assigned it CVE-2025-32711 with a critical severity rating of 9.3 out of 10.

A separate incident revealed that over 100,000 shared conversations from various AI platforms, including ChatGPT, Claude, and Copilot, had been indexed by search engines and archived. These conversations contained API keys, access tokens, personal identifiers, and sensitive business data.

Why Is Data Exposure Through AI Tools So Dangerous?

The risks extend beyond immediate data loss.

Intellectual property theft. When source code, product designs, or trade secrets get entered into public AI tools, they may be absorbed into training data. There is no way to retrieve or delete that information once it is in the model.

Regulatory penalties. Under UK GDPR and the Data Protection Act 2018, organisations must protect personal data. Sending customer information to an unapproved AI tool could constitute a data breach, triggering ICO investigations and fines. The Data (Use and Access) Act 2025, which received Royal Assent in June 2025, introduces further obligations for organisations deploying AI systems.

Reputational damage. A data exposure incident erodes customer trust. When clients learn their information was mishandled, they may take their business elsewhere.

Phishing and social engineering. Even seemingly harmless metadata, such as names and email addresses, gives attackers enough information to craft convincing phishing campaigns.

Shadow AI accumulation. The problem often goes undetected for long periods. One study found that shadow AI tools, unauthorised AI applications used within organisations, persist for an average of over 400 days. Small businesses face the highest risk, with 27% of employees using unsanctioned AI tools.

How Can Organisations Protect Themselves?

Protection requires a combination of policy, technology, and training.

Create clear AI usage policies. Define what data employees can and cannot enter into AI tools. Prohibit the use of public AI tools for confidential, customer, or regulated data unless explicitly approved.

Provide approved alternatives. If employees cannot easily use secure, approved AI tools, they will find their own. Invest in enterprise-grade AI solutions that offer data protection guarantees and do not use prompts for training.

Deploy browser-level protections. Use security tools that detect and control sensitive data before it leaves the environment. Monitor AI usage, including personal sign-ins, and restrict high-risk browser extensions.

Expand data loss prevention. Traditional DLP tools focus on files. Modern solutions must analyse copy-paste actions, screenshots, typed text, and prompt content.

Train employees without fear. Help staff understand the risks in practical terms. Explain why pasting internal data into public AI tools matters and what could happen if that data gets exposed. Make the training relatable, not technical.

Audit permissions and access. For tools like Microsoft 365 Copilot, review what data users can access. The AI respects existing permissions, so overly broad access rights increase risk.

Review third-party vendors. The Mixpanel breach showed that risks extend to any third-party service connected to your AI tools. Assess the security practices of all vendors, not just the AI provider itself.

What Does UK Law Say About AI and Data Protection?

The ICO provides guidance on AI and data protection, though this guidance is currently under review following the Data (Use and Access) Act 2025. Organisations deploying AI systems must ensure that any personal data is processed fairly, lawfully, transparently, and securely.

Key obligations under UK law include:

• Conducting Data Protection Impact Assessments for high-risk AI processing

• Ensuring transparency about how AI systems use personal data

• Providing individuals with rights to access, correct, and delete their data

• Implementing appropriate security measures to prevent unauthorised access

The ICO has the power to investigate and impose penalties for non-compliance. Organisations cannot outsource their data protection responsibilities to AI vendors. The responsibility remains with the organisation that controls the data.

What Should You Do Right Now?

Start with a simple audit. Ask your team: which AI tools are they using, and what data are they putting into them? You may discover unauthorised usage you did not know existed.

Next, establish clear rules. Communicate to employees what is and is not allowed. Make the rules easy to understand and remember.

Finally, provide secure alternatives. The goal is not to block AI adoption but to enable it safely. Organisations that strike this balance will protect their data while still benefiting from AI productivity gains.

The risk of data exposure through AI tools is real and growing. But with awareness and the right controls, you can manage that risk without sacrificing the benefits that AI brings.