AI is entering organisations at a pace that is increasingly difficult to control. One department uses a general-purpose assistant to analyse documents. Another has purchased a specialist tool with embedded AI. A development team accesses several models directly through APIs. Marketing experiments with content generation. And individual employees use external services on their own initiative to perform everyday tasks.
Individually, many of these decisions make sense. The problem arises when nobody has a complete view of what is happening.
This is what we know as Shadow AI: the use of artificial intelligence models, tools or services outside the organisation’s established mechanisms for visibility, control or governance.
The first reaction may be to try to prevent it. That is probably the wrong response.
The challenge is not to stop an organisation from using multiple models. In a technology landscape moving towards greater specialisation and a growing number of providers, this will become increasingly common.
The challenge is how to enable that diversity without losing control.
AI has dramatically lowered the barrier to entry
Adopting new enterprise technology used to require relatively visible decisions. Infrastructure had to be purchased, software procured, integrations developed or the technology department involved. These created natural control points.
Generative AI has removed many of those barriers. An employee can start using a new tool within minutes. A developer can connect to an API with a few lines of code. A department can purchase a SaaS application containing AI without necessarily having visibility of the entire technology chain behind it.
This ease of access is one of the reasons AI is spreading so quickly. But it also makes some basic questions harder to answer:
- What artificial intelligence are we actually using?
- What corporate information are we sharing with it?
- Which decisions or processes are we using it for?
If the organisation cannot answer these questions, it has a governance problem.
Shadow AI is not just ChatGPT
Reducing Shadow AI to the unauthorised use of public AI assistants would oversimplify the issue.
AI can enter an organisation in many ways.
- It can happen directly when an employee uses an external model.
- It can arrive through an API integrated into an internal application.
- It can be embedded in a SaaS platform the company already uses when its provider introduces new AI functionality.
- It can form part of a specialist tool purchased by an individual department.
- There may even be a chain of several providers and models behind a single feature visible to the user.
Governing AI therefore requires looking beyond the interface.
The relevant question is not simply which applications we use, but which models are involved, what data they receive, where it is processed and what role they play within our processes.
The risk of losing sight of data
One of the most obvious Shadow AI concerns is information. When we use an AI system, data may move beyond the traditional boundaries of corporate infrastructure.
Internal documents, code, customer information, contracts, personal data or strategic knowledge may be processed by external services.
This does not mean that using external services is inherently unsafe. Many providers offer appropriate enterprise privacy and data protection mechanisms. The problem arises when the organisation does not know that this processing is taking place.
Without visibility, it becomes difficult to determine which information may be used, which providers meet corporate requirements, what retention policies apply or in which jurisdiction particular data is being processed.
An AI governance strategy therefore cannot be limited to publishing a policy stating which tools are permitted.
Those rules need to be translated into the technology architecture.
Multiple models can be an advantage
There is, however, another side to the issue. Attempting to centralise all of an organisation’s artificial intelligence around a single model does not appear to be a particularly robust strategy either.
Models differ. Some excel at reasoning. Others at coding. Some offer lower costs for simple tasks. Others can handle large amounts of context. Certain use cases may require specialist models, while others may need to run within private infrastructure.
And the market is changing extraordinarily quickly. The right model today may not be the right one six months from now.
A mature organisation should therefore be able to benefit from an ecosystem of models without requiring every application or department to solve the question of how to access them independently.
A multi-model approach can provide flexibility, negotiating power, resilience and technological freedom.
But that diversity requires a common control layer.
Decoupling applications from models
One of the most important architectural decisions is precisely to decouple applications from the AI provider. If every application connects directly to a specific model, the organisation gradually accumulates dependencies.
Changing providers requires applications to be modified. Security policies have to be implemented repeatedly. Observability becomes fragmented. And understanding how much each model is being used — and for what purpose — becomes considerably more difficult.
A corporate AI access layer allows the problem to be approached differently. Applications request a capability (classifying a document, summarising information or performing a reasoning task, for example) and an intermediary layer can determine which model should be used according to predefined criteria.
Those criteria might include capability, cost, latency, data sensitivity, processing location, availability or regulatory requirements.
Model selection is no longer scattered across individual applications. It becomes a governed architectural decision.
Governance does not mean blocking
There is a risk when organisations discover that AI adoption is growing faster than their control mechanisms: responding solely through prohibition. Certain uses may need to be restricted, particularly where sensitive data, significant risks or tools without sufficient safeguards are involved.
But a strategy based exclusively on blocking can have an unintended consequence. If employees find genuine value in artificial intelligence and the organisation does not provide appropriate ways to use it, they are likely to seek alternatives themselves.
The objective should be to provide a safe and straightforward route to the capabilities they need. That requires combining policy with technology.
Define which models are authorised. Establish what information can be sent to each one. Apply access controls. Record usage. Restrict certain behaviours. Manage credentials centrally. Measure consumption and cost.
Governance works better when it is part of the infrastructure rather than relying solely on each user remembering a list of rules.
From controlling tools to governing capabilities
This change in perspective matters. Organisations are accustomed to governing applications: which software is approved, who can install it and who may access it. AI introduces a different reality.
The same capability may be available through multiple models and applications. And the same application may change the models it uses without the user noticing.
Governance therefore needs to evolve from controlling tools towards governing artificial intelligence capabilities.
- Which systems may access confidential information?
- Which models may process it?
- Which uses require human oversight?
- What actions may an agent perform?
- What information must be recorded?
- Which providers may be involved?
Answering these questions enables organisations to establish policies that remain valid even as the underlying technologies change.
Visibility before control
There is an even more fundamental principle. We cannot govern what we do not know exists.
Before designing sophisticated policies, an organisation needs to build a reasonably complete view of its AI ecosystem: systems, models, providers, use cases, owners and the data involved. That inventory should not be treated as a static snapshot.
The speed of AI adoption means it needs to remain current. Once that visibility exists, risks can be classified, different levels of control established and decisions made about where stricter intervention is required.
Not every use of AI needs the same level of governance. Generating a first draft of an internal document and using a model to influence a critical decision do not carry the same consequences.
Control should be proportionate to context.
Technology freedom with control
The Shadow AI debate can be framed as a tension between two apparently conflicting objectives. On the one hand, organisations want to take advantage of the capabilities offered by new models and providers quickly.
On the other, they need to protect their data, control costs, meet their obligations and understand what is happening within their systems. They should not have to choose between the two.
The right architecture can allow organisations to experiment with new models, replace providers and select the most appropriate technology for each requirement while maintaining common rules for access, security, traceability and governance.
That balance is particularly important in a market that will continue to change. The answer to Shadow AI is not to artificially reduce the number of models an organisation uses. It is to ensure that technological diversity does not mean losing control.
Because true maturity will not come from using a single model.
It will come from being able to use many when they create value while always knowing what intelligence we are using, what we are using it for and under which rules.