Izak-Jan van den Nieuwendijk Portfolio Manager Public Cloud
12 March 2026

Shadow AI: how much control does your organisation have over AI use?

Generative AI is rapidly becoming part of everyday work. Employees use chatbots and other AI applications to write text, analyse documents or prepare code. This development often moves faster than policy definition and technical setup within organisations. As a result, AI use takes place outside the view of IT and security. This phenomenon is known as Shadow AI. 

Public AI tools deliver immediate benefits for employees, while introducing new risks at the same time. Information moves beyond controlled environments, without insight or oversight. This article explains what Shadow AI entails, why organisations sometimes opt to prohibit AI use, and the consequences of such a decision

The phenomenon of Shadow AI

Shadow AI arises when employees use AI tools without formal approval or central governance. The pattern closely resembles Shadow IT, where departments deploy cloud solutions independently because existing facilities do not align with daily work. In the case of Shadow AI, the focus often lies on publicly accessible AI services that are readily available through a browser.

The barrier to entry remains low. Accounts are created quickly and technical restrictions are absent. As a result, internal data, sometimes unintentionally, ends up in external systems. This data falls outside established security measures such as data classification, logging and access control.

In practice, this happens in various ways. Internal meeting notes are submitted for summarisation, policy documents form the basis for new text, or snippets of source code are analysed by an external chatbot. The action feels harmless, yet context disappears. Storage location, processing methods and third-party access remain unclear.

The reflex to prohibit AI

Many organisations respond to Shadow AI with a blanket ban on generative AI tools. This decision rarely stems from resistance to technology, but from uncertainty about risks and accountability.

Data and privacy form a key argument. Public AI services offer limited assurances regarding data location and reuse of input. This conflicts with legislation such as the GDPR and with internal confidentiality guidelines. Without clear agreements, compliance becomes difficult to substantiate to auditors and regulators.

A lack of visibility also plays a role. Without a central provision, audit trails, monitoring and consistent policy enforcement are absent. Security teams lack information about who uses AI and which data is involved. In sectors with heightened regulatory requirements, this creates tension between innovation and risk management.

Organisational maturity also influences decisions. AI policy remains in an exploratory phase for many organisations. Concepts such as responsible use, ethics and data boundaries are taking shape but lack firm embedding. In that context, a prohibition feels like a manageable interim measure.

The results of a ban

An AI ban creates clarity on paper, yet leads to different outcomes in practice. Employees continue to seek ways to reduce workload and improve output quality. When an approved alternative remains unavailable, usage shifts to private accounts and personal devices. Shadow AI therefore persists, while visibility declines.

Friction also emerges in daily operations. Tasks for which AI already forms common practice demand additional time and manual effort. Productivity and consistency suffer, while expectations remain unchanged. The gap between policy and daily work widens.

At a strategic level, a ban limits experimentation and knowledge development. Teams gain less experience with AI support within controlled conditions. In markets where competitors build this experience, such restraint results in disadvantage.

From prohibition to control

The root of Shadow AI lies not in technology, but in a mismatch between demand and control. Employees seek support beyond existing systems. Organisations seek oversight of data and risk. A sustainable solution connects both perspectives.

A controlled AI provision brings usage back within defined boundaries. Generic access to public services gives way to a delineated environment where policy, security and compliance converge. AI use moves out of the shadows into a transparent domain.

What this looks like in practice: Secure Chat

Solvinity Secure Chat illustrates a managed AI environment. The solution makes generative AI available within a context aligned with the requirements of organisations operating in regulated sectors.

Information that normally enters public chatbots now undergoes processing within a controlled environment. Logging remains active, data export to external parties remains absent, and reuse for public model training does not take place. Data stays within agreed geographic boundaries. Authentication and authorisation follow established processes, while monitoring supports audits and internal controls.

Secure Chat functions as an instrument for risk management rather than a standalone AI tool. The solution addresses the same demand that drives Shadow AI, while operating within controlled conditions. Employees access the latest AI models from OpenAI and Anthropic, while IT and security retain visibility across the entire platform stack.

Final thoughts

Shadow AI represents a logical outcome of rapid technological adoption rather than a marginal issue. A ban offers temporary clarity, yet increases the distance between policy and execution. Effective control relies on a provision that aligns with daily work and meets requirements around data protection and compliance.

Secure Chat delivers this connection. AI availability within secure and controlled boundaries supports productivity and innovation without loss of oversight. Shadow AI gradually fades from view.

Ready to start with Solvinity Secure Chat? Contact us today.

Want to know more about Secure Chat?

Contact us today and find out more.

Also read

more articles

More