Shadow AI Risks – When Good Intentions Create Real Risk

CYAN AI Governance in Practice:

Shadow AI Risks – When Good Intentions Create Real Risk

Artificial intelligence is becoming part of everyday work. People use it to draft emails, summarise notes, compare documents, analyse information and speed up routine tasks. In most cases, they are simply trying to get something done efficiently, using the easiest route available to them.

That is exactly why shadow AI can be difficult to spot. It rarely begins with someone deliberately ignoring the rules. More often, it starts with a small decision that feels practical at the time:

• One person uses a public AI tool to help finish a task.
• Another uploads an internal document to get a faster summary.
• Someone else switches on an AI feature in a platform they already use, without realising it can connect to internal data.

That is shadow AI: AI use that has not been formally approved, or is not governed by the organisation. The exposure of an organisation’s data is rarely malicious. The risk starts when that activity happens without clear rules around what data can be used, what the tool can access, what needs to be reviewed and who is responsible for the outcome.

In that sense, shadow AI is often less about individual behaviour and more about the process pressures people are trying to resolve. Employees see the potential to clear bottlenecks, and the organisation needs to keep pace with how they are using AI to do it.

Why Shadow AI develops

Shadow AI usually begins when people have a real problem to solve and can see that AI might help, but have no clear or practical way to use it within the organisation’s existing rules. People will find their own ways to use it, and many already are.

Employees are under pressure to work efficiently, and AI offers a quicker or easier way to get something done. Sometimes the rules are unclear. Sometimes approved tools are difficult to access, do not meet the need or feel less familiar than the tools people already use. Sometimes AI capability is already built into software people use every day, so the boundary between approved technology and unapproved AI is not obvious.

When that clarity is missing, decisions are left to individuals. One person may decide a tool is safe. Another may make a different choice. A third may not even realise that a new AI feature has access to more information than they expected. That is where inconsistency starts to create real risk. The Financial Stability Board’s sound practices for AI describe the same pattern: “Governance gaps may also enable the use of ‘shadow AI’”.

This is why shadow AI is rarely a story about bad actors. More often, it is a story about capable people trying to solve practical problems without a clear framework around them.

Data is often where the risk begins

Knowing how to write a good prompt can be useful. Knowing what should never be included in that prompt is more important. An employee may paste information into a tool without fully understanding how sensitive it is or where it will go. That could include:

• Client records
• Contracts
• Payroll information
• Employee data
• Financial forecasts
• Internal plans
• Legal documents

If the tool has not been approved, the organisation may not know where that information is processed, whether it is retained or who can access it. Using AI does not remove existing data protection responsibilities, but it can quietly obscure when those boundaries are crossed.

The risk also grows when a tool connects to live systems. Every new integration, plugin or permission can change the level of risk, which is why the question is never only what someone typed into a prompt. It is what the tool can access and do on its own.

What Shadow AI looks like in practice

Take an employee who needs to summarise a sensitive client issue quickly. They paste internal case notes and commercial details into a public AI tool.

Or take an employee who uses AI to benchmark salaries and includes identifiable pay information to improve the result. The output is then stored somewhere that a wider group can access.

In both cases, the intention is useful. The problem is that sensitive information has moved through a process without the right controls around access, storage and sharing.

The pattern is a familiar one. Someone is trying to solve a practical problem before the organisation has clearly defined how that use of AI should work.

Make the safe route easier

The answer to shadow AI is not to ban AI or make it so difficult to use that people find another way around the rules. The better approach is to make safe use easier.

People need to know which tools they can use and what they can use them for. They need to understand what information is safe to share and what needs to remain inside controlled environments. They also need a clear route for raising new ideas, because if that process is too slow or too difficult, people will continue to find alternatives.

The aim is not to stop experimentation or progress. It is to make sure useful experimentation happens in a way the organisation understands and can support.

Shadow AI is a useful signal

Shadow AI is not simply a case of someone using an unapproved tool or breaking rules that were never clearly defined. It is evidence that can tell an organisation something important.

It can show where people already see value in AI. It can reveal where current tools or processes are not meeting their needs. It can highlight where guidance is unclear or where approval processes are difficult to navigate. Handled well, those signals can help an organisation understand where AI is already changing the way people work and where more structure is needed.

Shadow AI grows in the gap between what people need to do and what the organisation has made possible. Closing that gap means making approved use clear, practical and proportionate to risk.

Good AI governance should do more than restrict behaviour. It should support the way people actually work.

Where are your people already using AI to get their work done, and how much of it could your organisation explain if it were asked?

Written by Simon Bond, Managing Director at CYAN

Link has been copied to the clipboard.

Published October 2026

Sources & Citations

Financial Stability Board. Sound Practices for Responsible Adoption of Artificial Intelligence (AI): Final report. FSB, 15 October 2026. https://www.fsb.org/2026/10/sound-practices-for-responsible-adoption-of-artificial-intelligence-ai-final-report/