Bottom line: Employees unknowingly enter sensitive data into unauthorized AI services; traditional DLP solutions fail to capture these new data paths and require context-based risk analysis instead of blanket blocks.
Employees increasingly use unauthorized AI services and in doing so enter sensitive data such as customer information, internal documents and source code. Traditional data loss prevention systems do not adequately capture these new data paths.
Artificial intelligence has become a productive work tool in many organizations. At the same time, employees frequently resort to unapproved external AI services – whether for composing emails, document analysis or code optimization. This phenomenon is referred to as “Shadow AI” and is emerging as a challenge to data protection and information security.
The central risk lies in the fact that employees input confidential information into these uncontrolled systems in order to complete tasks more quickly. Information flow often occurs through simple copy-and-paste operations in the web browser or direct uploads into chat windows – data paths that conventional security solutions capture only inadequately. While established DLP systems can, for example, detect and block the transmission of sensitive files via email, inputs to external AI services typically remain unnoticed.
Classic data loss prevention solutions originally focused on email, file transfers and external storage media. However, the operation of modern AI applications differs significantly from these established communication channels and thus calls conventional security concepts into question.
Organizations therefore need a contemporary DLP approach with two focal points: First, complete visibility into all data movements, including browser activities, uploads and copy-and-paste operations. Second, context-based risk assessment that does not blanket-block every data transfer, but analyzes risk factors and enables graduated responses. In the case of low risk, employees can receive a warning; for critical operations, blocking or additional review is appropriate.
As AI systems become more widespread, uncontrolled data leakage in everyday business operations becomes the greatest threat – often not from external attackers, but from internal, well-intentioned sharing of sensitive information. Security strategies must therefore be more strongly aligned with the actual working practices of employees in order to leverage AI potential without compromising corporate data.
Source: www.it-daily.net · Published 26 June 2026
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