Copy Paste Leakage

Copy paste leakage refers to the unintentional or unauthorized transfer of confidential information from a secure environment to an insecure one by a user manually copying text and pasting it into an external tool. In the context of e-signing, this most commonly occurs when a signer copies contract terms into public AI services (such as ChatGPT) to receive help with summarization or translation. This action causes sensitive data to leave the organization's control and become part of the external service's training data or logs.

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What is Copy Paste Leakage?

In today’s browser-based work environment, the web browser has become the primary control point for data flows—but also the most overlooked risk factor. Copy paste leakage represents a new frontier for data loss that traditional security solutions (DLP) often miss, as these are typically focused on file transfers rather than text-based inputs.

When sensitive information “leaks” via the clipboard, it creates an invisible channel for data exfiltration. This often happens in good faith by employees seeking to increase their efficiency by using AI to understand complex documents.

The problem is that once text is pasted into a public LLM (Large Language Model), the organization loses all ability to track, restrict, or delete that information. This is a core component of Shadow AI—the use of AI tools outside of corporate oversight and security protocols.

Why is Copy Paste Leakage a Critical Risk Now? (Facts & Statistics)

The scale of this risk is confirmed by real-time data from large-scale enterprises:

  • AI is the Primary Destination: A staggering 77% of employees copy and paste data into generative AI tools.
  • Lack of Control: 82% of all pasting into AI tools occurs via unmanaged (private) accounts that sit outside of corporate visibility.
  • Sensitive Information Volume: On average, an employee makes 14 pastes per day via non-corporate accounts, of which at least 3 contain sensitive data such as personally identifiable information (PII) or credit card data (PCI).
  • A Major Exfiltration Channel: Generative AI now accounts for 32% of all data exfiltration from corporate environments to private accounts, making it the single largest channel for data leakage.
  • The Education Gap: According to KPMG (2025), 61% of the global workforce lacks formal training in how AI tools handle data, directly driving this risky behavior.

Practical Scenario: M&A and Intellectual Property (IP)

Consider a Mergers and Acquisitions (M&A) process where a lawyer is reviewing a draft of a Share Purchase Agreement (SPA).

  • The Problem: The agreement contains details about the company’s intellectual property (IP) and future profit targets. To save time, the lawyer copies three key sections regarding patent rights and pastes them into a public AI with the prompt: “Summarize the IP risks for the buyer.”
  • The Leakage: At that moment, the lawyer has caused Copy paste leakage. The company’s most valuable trade secrets are now stored on an external server owned by an AI provider.
  • The Result: Even if the company's internal network is secure, the information has left the perimeter via the browser. This may constitute a breach of Non-Disclosure Agreements (NDA) and create regulatory risks under NIS2 or GDPR.

FAQ

Why doesn’t our antivirus stop Copy paste leakage?
Traditional security tools are built to scan files or network traffic for known malicious code. They are rarely configured to understand the context of text being copied between two windows in a browser, making these “file-less” transfers almost invisible.

Is it only public AI tools that pose a risk?
No. Data also leaks to other unsanctioned channels such as private chats (WhatsApp), public storage services (personal Google Drive), or career sites (LinkedIn). However, AI tools are the fastest-growing channel for this specific behavior.

How can this leakage be technically prevented?
The most effective method is to use a tenant-isolated signing environment where a built-in AI assistant offers the same functionality as public tools—but inside a locked document container where data is never used to train external models.