Your Employees Are Already Using AI. Do You Know What Data They Are Sharing?

An employee needs to summarise a long document.

Someone in HR wants help drafting a communication.

A sales executive wants an AI assistant to analyse customer information.

A developer pastes some code into an AI tool to troubleshoot a problem.

A manager uploads a spreadsheet and asks for insights.

None of these people may think they are introducing artificial intelligence into the organisation.

They are simply trying to get their work done faster.

But from an AI governance perspective, an important question has already arisen:

What organisational data just went into the AI system?

For many organisations, the first AI governance challenge may not be the AI platform they are planning to implement.

It may be the AI tools their employees are already using.

AI Adoption May Be Happening Before AI Governance

Generative AI has made sophisticated technology accessible through an ordinary browser window.

Employees don't necessarily need an IT project, infrastructure deployment or formal procurement process to start experimenting with AI.

That accessibility is part of its value.

It also creates a governance challenge.

An organisation may believe it is still discussing its AI strategy while employees across different departments are already using public or commercially available AI services.

This is often described as Shadow AI — the use of AI tools or capabilities without appropriate organisational visibility, approval or governance.

The objective should not simply be to find people using AI.

It should be to understand how AI is being used and what information is being shared with it.

Start With the Data

Consider what employees work with during an ordinary day:

Customer information.

Employee records.

Contracts.

Financial information.

Internal reports.

Source code.

Business plans.

Meeting notes.

Presentations.

Intellectual property.

Some of this information may contain personal data. Some may be commercially confidential. Some may be publicly available and relatively low risk.

The problem isn't simply that information is being entered into an AI tool.

The problem is that users may not always distinguish between information that is appropriate to share with an approved AI service and information that should remain within controlled organisational systems.

That makes data awareness an essential part of AI awareness.

Not Every AI Service Handles Data the Same Way

Saying that information has been “shared with AI” does not by itself tell us what happens to it.

Different AI services, account types and enterprise arrangements can have different approaches to submitted data, retention, access, training and administrative controls.

Organisations therefore need to understand the services they permit employees to use and the arrangements under which those services are being used.

The relevant questions might include:

What information is submitted?

Where does it go?

How is it retained?

Can it be used for other purposes?

Who can access it?

What contractual and technical controls apply?

AI governance therefore cannot stop at approving the name of an AI product.

The organisation needs to understand the data relationship behind its use.

“Don't Use AI” Is Not Much of a Governance Strategy

One response to uncertainty is to prohibit employees from using generative AI.

For some particularly sensitive activities, restrictions may certainly be appropriate.

But a blanket prohibition can create another problem.

Employees may still see considerable productivity value in these tools and find ways to use them outside approved processes.

The organisation then loses visibility without necessarily eliminating the behaviour.

A more sustainable approach is to establish practical boundaries.

Which AI tools are approved?

What categories of information may be used with them?

What information must never be submitted?

Which use cases require additional approval?

When must AI-generated output be independently reviewed?

Where should employees go when they are uncertain?

Good governance should help people make better decisions, not simply produce another policy document they rarely consult.

Give Employees Practical Guardrails

AI guidance needs to make sense during an employee's working day.

A fifty-page policy may be important from a governance perspective, but it is unlikely to be what somebody consults before pasting information into an AI prompt.

Employees need simpler guidance.

For example:

Approved tools — which AI services may be used for business purposes.

Permitted data — what kinds of information may be submitted.

Restricted data — personal, confidential, regulated or otherwise sensitive information requiring additional controls.

Permitted use cases — activities where AI assistance is acceptable.

Human review — situations where AI output must be checked before it is relied upon.

Escalation — whom to ask when the employee is uncertain.

The exact rules will differ between organisations.

The important point is that employees should not have to interpret an abstract AI policy every time they use a tool.

Human Accountability Does Not Disappear

Protecting the information going into an AI system is only one side of the issue.

Organisations also need to think about what comes out.

AI-generated content can be inaccurate, incomplete, biased or inappropriate for the context in which it is being used.

If an AI assistant drafts a customer communication, analyses information, recommends an action or generates code, somebody still needs to understand when human verification is required.

AI can assist the employee.

It does not automatically inherit the employee's accountability.

This becomes increasingly important as AI moves from helping people draft content to influencing decisions and business processes.

Discover Before You Govern

An organisation beginning its AI governance programme may naturally start by writing an AI policy.

There may be an earlier step.

Discover how AI is already being used.

Which teams are using it?

Which tools are they using?

For what purposes?

What data is being shared?

Are AI capabilities already embedded within existing business applications?

Which uses present little risk, and which require closer assessment?

The answers provide a much stronger foundation for governance than assumptions about how employees might use AI.

From the AI Governance Desk

When organisations discuss AI governance, the conversation often begins with the AI systems they plan to implement.

A more useful first question may be:

“What AI are our people already using today?”

That question changes the conversation.

It moves AI governance from a future technology project to a current business reality.

Once organisations understand the tools, use cases and data involved, they can begin making informed decisions about approvals, controls, awareness, risk assessment and oversight.

Because there is a simple governance principle underneath all of this:

You cannot govern what you have not discovered.

AI Governance Starts With Visibility

Organisations should benefit from AI.

Employees should be able to explore tools that improve productivity, help them analyse information and make routine work easier.

But adoption and governance need to develop together.

The objective is not to stop every employee from experimenting with AI.

It is to ensure that experimentation does not unintentionally expose personal data, confidential information, intellectual property or other sensitive organisational information.

Before designing an elaborate AI governance framework, therefore, start with something much simpler.

Ask your teams:

What AI are you using?

Then ask the question that may matter even more:

What data are you sharing with it?