ChatGPT, Claude, transcription tools, writing assistants. Without a framework, without control over what data transits. This is not a discipline problem. It is an absent governance problem.
In most organisations, consumer AI tools were adopted by teams long before leadership defined a framework. Contracts were summarised, strategies drafted, client data analysed. All of this transited through external servers under conditions nobody read.
Banning does not work. Teams route around it. The only durable response is governance that distinguishes what is acceptable from what is not, with clear rules and approved tools.
We intervene to map real exposure, establish an acceptable AI usage policy and identify tools to approve, frame or prohibit. The goal is not to ban but to transform diffuse risk into a lever for governed transformation.
Shadow AI refers to the uncontrolled use of AI tools by employees within an organisation, outside any framework approved by leadership. This primarily concerns generative AI tools (writing assistants, transcription tools, chatbots) used with professional sensitive data without a formalised policy.
Shadow AI creates three types of risk. A data risk: information transmitted to external tools (client data, contracts, strategies) may be used to train models or exposed in case of breach. A regulatory risk: uncontrolled use of AI tools processing personal data can create GDPR violations or EU AI Act non-compliance. A competitive risk: generative AI models may use user inputs to improve general models, effectively feeding assets owned by the vendor.
The response to shadow AI is not prohibition but structured governance: identifying tools used, classifying by risk level, defining an acceptable usage policy with approved tools, and training teams. This approach aligns with overall organisational AI governance and responds to EU AI Act oversight requirements.
We make visible what is not : before the exposure becomes an irreversible constraint or an incident.
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