For many organisations, the most significant risk associated with Shadow AI is not the technology itself, but how information is entered into it. Generative AI tools are designed to respond to prompts, and prompts often contain far more information than users realise.
Employees regularly include data in prompts to improve output quality. They paste text, describe scenarios, or upload documents to provide context. In doing so, they may unintentionally share personal data, confidential commercial information, or sensitive operational details with external systems that sit outside organisational control.
This behaviour is rarely malicious. In most cases, employees believe they are using AI tools in the same way they would use search engines, spreadsheets, or word processors. The distinction between internal systems and external AI platforms is not always clear, particularly where tools are accessed through browsers and require minimal setup.
From a governance perspective, the risk is compounded by misunderstanding. Many employees assume that prompts are private, transient, or not retained. Others believe that anonymising names or removing obvious identifiers is sufficient to prevent data exposure. In reality, prompts can still contain enough contextual detail to reveal sensitive information.
The issue is further complicated by the diversity of tools being used. Employees may access multiple AI platforms, each with different terms of use, data handling practices, and retention policies. Organisations rarely have visibility into which tools are being used, let alone how information is being entered into them.
Data protection obligations do not change simply because a tool is easy to use. Personal data, confidential information, and regulated content remain subject to legal, contractual, and ethical requirements. Shadow AI introduces new pathways for data exposure that are not always captured by traditional controls.
Effective management of this risk does not begin with technology. It begins with clarity. Employees need clear guidance on what types of information must never be entered into AI tools, what requires approval, and what is acceptable in low-risk contexts. Managers need to understand where the highest exposure sits and how to challenge unsafe practices.
Organisations that treat data leakage through AI as a behavioural and governance issue are better positioned to reduce risk. Training, practical examples, and clear expectations are far more effective than relying on employees to interpret complex data protection rules on their own.
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