Shadow AI Is Telling You Where to Build Next. Build It Somewhere You Control.

An employee receives a long customer document late in the afternoon. Before the next morning, they need to find the relevant requirements, compare them with an internal checklist and prepare a summary for a colleague.

Doing it manually will take hours, so they open an AI tool they already know, upload the document and ask for help.

They are not trying to put the company at risk. They are trying to get their work done.

This is how AI often enters a business: not through an official strategy, but through employees looking for a faster way to handle repetitive tasks. In a regulated company, however, those tasks may involve contracts, customer records, financial information, technical specifications or other sensitive data.

Microsoft and LinkedIn's 2024 Work Trend Index found that 78% of people who already use AI at work bring their own AI tools into the workplace, and that the practice is more common still at small and medium-sized companies.

This does not mean that most SMB employees are using shadow AI. It does show that when employees use AI, the tool often has not been selected, provided or necessarily approved by their employer.

You cannot manage what you cannot see

The security cost of shadow AI is no longer hypothetical. IBM's 2025 Cost of a Data Breach Report found that unapproved AI tools were a factor in one in five breaches, and that those incidents added around USD 670,000 to the average breach while exposing customer records more often than other breaches. But shadow AI is not only an IT-security concern. It has also become a governance issue.

Since February 2025, Article 4 of the EU AI Act has required providers and deployers of AI systems to ensure a basic level of AI literacy among the people using those systems on their behalf. We break down what that means for regulated companies in our full guide to the EU AI Act. The short version: a company cannot provide meaningful guidance, training or oversight when it does not know which AI tools employees are using, or what they are using them for.

That does not mean every AI-assisted email needs to become a compliance project. But every company should at least be able to answer four basic questions:

• Which AI tools are being used?

• What type of information is being entered?

• Which business processes are they supporting?

• Does the output influence an important decision?

For regulated SMBs, this visibility is the starting point for responsible AI adoption.

A ban does not solve the underlying problem

Companies need clear rules about which data may be entered into external AI services. But blocking a tool does not remove the reason an employee turned to it.

The document still needs to be reviewed. The report still needs to be written. The customer is still waiting.

Instead of asking only, "Who is using an unapproved AI tool?", management should also ask: What are employees trying to accomplish with it?

Perhaps they are extracting dates from supplier certificates, comparing two versions of a contract, searching through internal procedures or turning notes into a structured report.

These are not vague ideas for future AI projects. They are existing workflows that employees already find frustrating enough to seek their own solutions.

Shadow AI can therefore be treated as a warning, but also as a useful signal. It shows the company where an approved, purpose-built application could provide immediate value.

Turn the workaround into a controlled application

Imagine a quality team that regularly receives supplier documentation. An employee needs to check whether all required files are present, extract certificate dates, compare the package with the current onboarding requirements and prepare a review for the quality manager.

A generic AI tool may help with part of that task, but each employee will use it differently. One uploads the complete package, another copies selected passages and a third uses an outdated checklist in the prompt. The company has little visibility into how the result was produced.

A purpose-built application can turn the same task into a consistent workflow.

The employee uploads the documents through an approved interface. The application checks them against the current requirements, extracts the relevant dates, identifies missing or contradictory information and links its findings to the source documents. The quality manager then reviews the result and makes the final decision.

The AI is not approving the supplier. It is preparing the case so that the responsible employee can review it faster and more consistently.

That is often where AI delivers the greatest value in regulated work: not by removing professional judgement, but by reducing the searching, copying and formatting that surrounds it.

How to build the business app with Understand Studio

Understand Studio allows a team to begin with the process it already understands rather than with a long technical specification.

1. Describe the workflow in plain language

Start with the person doing the work, the information they receive and the result they need.

For example:

When a quality coordinator uploads a supplier document package, the application should check it against our approved onboarding requirements, extract certificate expiry dates, identify missing information and prepare a structured review for the quality manager.

This is more useful than asking for a general "AI compliance assistant" because it defines a real job with a clear beginning and end.

2. Define the boundaries

Decide who may use the application, which information it may access and which company sources it should rely on.

The team should also define what the app must never decide by itself. In this example, it may prepare the supplier review, but final approval remains with the quality manager.

These boundaries are important for security, but they also make the application easier to test. A narrow app has a clear purpose; a general chatbot can be asked almost anything.

3. Generate and refine the first version

Understand Studio turns the description into a working web application. The team can review the screens, inputs, workflow and output, then refine the application conversationally.

The employees who understand the process can therefore participate directly in shaping the app. A quality manager knows which missing document should trigger an escalation. A compliance specialist knows which conclusions require evidence. An operations employee knows what information the next person in the process needs to receive.

4. Connect approved knowledge and systems

A Studio app does not stand alone. It is based on the same Understand Tech platform and APIs that manage the company's assistants, knowledge bases, workflows and data sources. Developers can also use the platform APIs to connect AI capabilities with existing applications and internal processes; API requests are encrypted.

This means the app can work with approved company knowledge instead of requiring employees to move sensitive information between unrelated tools.

The objective is not to give the application access to everything, but to give it access to exactly what it needs.

5. Test, assign ownership and deploy

Before the application is used in a real process, it should be tested with difficult examples: missing documents, conflicting information, poor-quality scans and cases where the correct answer is not available.

Sometimes the correct result should be: "There is not enough information to complete this review. Human intervention is required."

The application should also have a named business owner who is responsible for its sources, intended use, testing and future changes.

Finally, the deployment should match the sensitivity of the data. Understand Studio applications can run through secure SaaS, within a customer-controlled private cloud or VPC, or entirely on-prem. The same platform and APIs are available across these deployment models, allowing an application to move into a more controlled environment without being rebuilt from the beginning.

For organisations whose data cannot leave their premises, Understand Tech's AI-in-a-Box runs the platform locally and can operate in network-restricted or fully air-gapped environments. It includes local models, retrieval, workflows and APIs, together with role-based access, audit logs and encrypted local storage, without requiring a cloud connection.

This is a significant advantage for companies working with sensitive customer files, intellectual property, technical documentation or strict data-residency requirements.

No platform makes an organisation compliant automatically. The company still needs to assess its legal, privacy, security and sector-specific responsibilities. But building the application on a governed platform gives it far more control than leaving employees to assemble important workflows inside unapproved tools.

The approved alternative must be worth using

Employees will not abandon shadow AI for an official solution that makes their work harder.

A successful application needs to do more than satisfy a security checklist. It should organise the evidence, highlight exceptions and produce an output that fits naturally into the next step of the process.

Its value should be measured in practical terms: Did the review become faster? Were missing documents identified earlier? Did employees spend less time copying information? Could specialists focus on difficult cases instead of routine preparation?

The best response to shadow AI is therefore not another warning email, nor is it an unrestricted chatbot for the entire company.

It is an approved alternative that is useful enough for employees to choose. And secure enough for the business to govern.

For regulated SMBs, the first step is visibility. The next is turning the workflows employees already want to improve into applications that keep the company's data, rules and decisions under control.

Sources

• Microsoft and LinkedIn, 2024 Work Trend Index Annual Report: AI at Work Is Here. Now Comes the Hard Part (2024). https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part

• IBM, Cost of a Data Breach Report 2025. https://www.ibm.com/reports/data-breach

• European Commission, AI Literacy: Questions and Answers and guidance on Article 4 of the EU AI Act. https://digital-strategy.ec.europa.eu/en/faqs/ai-literacy-questions-answers