Shadow AI: The AI You Didn’t Approve Could Already Be Inside Your Business

AI adoption is moving faster than most organisations can govern it.

Employees are using AI tools to summarise documents, write emails, analyse data, generate code, create presentations and solve problems. In many cases, they are doing so because the technology makes their jobs easier and more efficient.

The problem is not necessarily that employees are using AI.

The problem is that security teams may not know which AI tools are being used, what information is being shared with them, or how that information is being handled.

Welcome to the growing challenge of Shadow AI.

What is Shadow AI?

Shadow AI refers to the use of artificial intelligence tools and services within an organisation without formal approval, oversight or security assessment.

It is an extension of a problem security teams have faced for years: shadow IT.

The difference is that AI can interact directly with some of an organisation’s most valuable information.

An employee might paste customer information into an AI chatbot to help draft a response. A developer might upload source code to troubleshoot an issue. A finance employee could ask an AI tool to analyse a spreadsheet.

Each action might appear harmless.

Collectively, they can create an unmanaged data security problem.

The real risk isn’t AI. It’s the loss of visibility.

It is tempting to approach Shadow AI by simply blocking access to consumer AI platforms.

But that can miss the bigger issue.

Organisations already have employees who want to use AI. If legitimate tools are difficult to access or there is no approved alternative, employees may look elsewhere.

That creates a familiar security cycle:

Technology becomes available → employees adopt it → security teams discover it later → controls are introduced → employees find another workaround.

The challenge for security leaders is therefore not simply deciding whether employees should use AI.

It is creating an environment where AI can be used productively, securely and with appropriate controls.

What happens to the information employees put into AI?

This is where organisations need to start asking better questions.

Before an AI tool is approved, security teams should understand:

• What data is being entered?
• Where is that data processed?
• Is it retained?
• Who can access it?
• Is it used to train models?
• What happens to uploaded documents?
• What authentication controls are available?
• Can activity be monitored?
• Can access be restricted based on user, device or data sensitivity?

These questions become particularly important when AI tools are being used with confidential business information, customer data, intellectual property, credentials, source code or commercially sensitive information.

Shadow AI creates a visibility problem

Traditional security controls were not necessarily designed around employees interacting with hundreds of AI services.

A business may have strong endpoint protection, firewalls, identity controls and email security.

But if an employee can access an AI service through an approved browser and voluntarily upload sensitive information, the traditional perimeter may not provide the visibility security teams need.

This is why Shadow AI should not be treated as a standalone AI problem.

It sits across identity, endpoint, network, data, cloud and user behaviour.

The answer therefore needs to be equally joined up.

Don’t just create an AI policy

An AI policy is useful.

But a policy alone will not necessarily stop Shadow AI.

Employees need to understand why certain AI behaviours create risk and what they should use instead.

Organisations should consider creating a clear framework covering:

Approved tools

Which AI platforms can employees use for business purposes?

Approved data

What information can and cannot be entered into AI systems?

Access controls

Who should have access to particular AI capabilities?

Monitoring

Can the organisation identify unusual or risky AI usage?

Data protection

Are sensitive documents and information appropriately protected?

User awareness

Do employees understand the risks associated with AI-generated content, data sharing and AI-enabled attacks?

Ongoing review

Are approved AI tools still appropriate as their capabilities and data practices change?

The last point is particularly important.

AI governance cannot be a one-off exercise.

The technology is changing too quickly.

Shadow AI is also a security opportunity

There is another side to the conversation.

Used correctly, AI can help security teams work more effectively.

It can support threat detection, vulnerability analysis, security operations, investigation and prioritisation.

The question is therefore not whether organisations should embrace AI.

It is how they can introduce AI without creating a new unmanaged attack surface.

A practical starting point

Organisations don’t necessarily need to begin with a major AI transformation programme.

Start with visibility.

Understand which AI services are already being accessed across the organisation. Identify where sensitive information could be entering those services. Review identity and access controls. Consider how network, endpoint, cloud and data security controls can provide greater visibility.

Then establish clear, practical guidance for employees.

Because if the organisation does not provide a secure way to use AI, employees may create their own.

And that is where Shadow AI becomes particularly difficult to control.

The next generation of AI security

AI is becoming embedded into everyday working practices.

That means Shadow AI is unlikely to disappear simply because organisations introduce another policy or block another website.

Security teams need to think about AI as part of the wider technology environment.

Who is using it?

What are they using it for?

What data is involved?

What controls are already in place?

And where are the gaps?

The organisations that answer those questions early will be better positioned to embrace AI without allowing it to quietly introduce another layer of unmanaged risk.

At ANSecurity, we take a practical, vendor-agnostic approach to cybersecurity. We look at how security controls, infrastructure, data, users and technology work together, helping organisations identify gaps and build security around the way they actually operate.

AI adoption doesn’t have to mean accepting uncontrolled risk. The goal is to make AI work securely within your environment, not outside it.