One of the first questions that comes up when an organization considers using AI is what happens to its data.
An AI feature may need information from customer records, internal documents, financial systems, support history, or other business data to provide a useful answer. That raises reasonable questions about what information leaves the organization, where it is processed, how long it is retained, and who can access it.
Using AI with business data does not necessarily mean giving an AI provider direct access to internal systems or sending large amounts of information outside the organization. How the application is designed determines what the AI can see and where different parts of the processing take place.
The following sections cover how to limit what the AI receives, where the AI model should run, how to protect access to the data, and how to choose an approach based on the data involved.
1. Send Only the Data the AI Needs
The simplest approach is for an existing application to send selected information to an AI provider to perform a specific task.
The AI provider does not need direct access to the systems where the data is stored. The application remains responsible for deciding what information is retrieved and sent. That makes it possible to minimize the data sent to the AI.
For example, an application might use AI to summarize a customer case. The application retrieves the relevant case notes from its own database, sends those notes to the AI provider, receives the summary, and displays it to the user. The AI needs the details of the case but may not need the customer’s name, account number, or contact information. Those details can be left out, or replaced with a placeholder such as “Customer A” before the request is sent.
This reduces how much information is sent and how much of it identifies a customer. However, the information that is sent is still processed by the AI provider, so the provider’s terms need to be reviewed before sensitive information is included (see section 5).
2. Retrieve Relevant Information Instead of Sending Everything
Some applications need to answer questions using a large collection of internal information, such as policies, contracts, procedures, or product documentation. In these cases, the application cannot simply retrieve the information it needs. It first has to find which documents are relevant to the question.
A common approach is retrieval-augmented generation, usually called RAG. Instead of sending all of this information to an AI provider, the application searches the organization’s information for content related to the user’s question. Only the relevant information is then sent to the AI provider.
For example, if an employee asks:
“What is our policy for reimbursing international travel?”
The application might find the relevant sections of the company’s travel policy and send only those sections to the AI provider.
This reduces the amount of information sent for each request. However, the selected information is still processed by the AI provider. RAG limits what is sent; it does not prevent the selected information from leaving the organization’s environment.
3. Keep Calculations Inside the Application
Some questions can be answered without sending the underlying records to the AI provider.
Consider a user asking:
“How much did Customer A spend last quarter?”
One approach would be to retrieve every transaction for that customer and send the transactions to an AI provider to calculate the total. That sends more information than necessary and uses AI for a calculation the database can perform directly and reliably.
A better approach is for the application to calculate the total itself. If an AI model is used to phrase the answer, it receives only the result, not the individual transactions behind it.
The same principle applies to account balances, pricing calculations, inventory levels, financial totals, eligibility rules, and other information that existing systems can determine reliably. AI does not need to perform every step simply because the application includes AI functionality.
This reduces what is sent, but it does not remove the AI provider from the picture. The user’s question itself, including the customer’s name, is still sent, along with the application’s result, which the AI uses to write its answer.
4. Let AI Interpret the Request Without Seeing the Results
In some cases, the AI model does not need to see the business data at all.
Suppose a manager asks:
“Show me customers with invoices more than 60 days overdue.”
The AI model does not need access to the customer records to understand what the user wants. It can translate the request into simple search criteria that the application already knows how to handle, such as:
- Invoice status: unpaid
- Days overdue: greater than 60
The existing application then checks the user’s permissions, validates the criteria, runs a search it already supports, and displays the results.
With this approach, the AI model helps interpret the user’s request, but the resulting customer records are never sent to the AI provider. This can be useful when an organization wants a conversational interface without sending that business data to an outside provider.
It also creates an important security boundary. The AI model is not given open-ended database access. It can request only operations that the application explicitly supports.
The tradeoff is flexibility. The application can answer only the kinds of questions someone has built support for in advance, so each new type of request is additional development work. The user’s question is also still sent to the AI provider.
5. Decide Where the AI Model Should Run
Another important decision is where the AI processing takes place.
Use a Business or Enterprise AI Service
Major AI providers offer business services with additional controls over how submitted data is secured, accessed, retained, and used. These services are available directly from AI providers and through cloud platforms such as Microsoft Azure and AWS.
Business terms differ from the consumer versions of the same products. A free or personal account may handle data very differently from a business account with the same provider, so it is worth confirming which version is actually being used.
When evaluating a service, organizations should understand what data is retained and for how long, whether submitted data may be used for model training, and what security and access controls are available.
Using a business AI service does not remove the need for good application design. Organizations should still determine what information the AI actually needs and avoid sending unnecessary data.
Run the Model in a Private Environment
For organizations with stricter data requirements, another option is to run an AI model within an environment they control.
For example, an organization could run an open-weight model, meaning an AI model that anyone can download and run, rather than one that is available only as an online service. It could run on the organization’s own servers or within a private cloud environment instead of sending requests to an outside AI provider. This gives the organization greater control over where its data is processed.
The tradeoff is that the organization takes on more responsibility for running and maintaining the model, including security, updates, monitoring, performance, and the servers it runs on. A model run privately may also be less capable or slower than those offered by the major AI providers.
For some organizations, the additional control may justify the added complexity. For others, a business AI service with appropriate security and data controls may be more practical.
6. Protect Access to the Data
Where the model runs is only one part of protecting business information. An AI feature can introduce security risks even when the AI provider has strong data protections.
Consider an internal AI feature that can search documents across the company. If an employee normally has access only to sales data, the application should not allow that employee to use the AI to access HR files, executive documents, or financial records they are not authorized to see.
The application should verify who is making the request and what information that person is allowed to access. It should also limit what information and systems the AI feature itself can access and avoid exposing sensitive data unnecessarily.
The same principle applies to any records the application keeps. If it saves questions and AI responses, those records contain sensitive information too and need the same protection as the original data.
Adding AI should not give users access to information they would not otherwise be authorized to see.
7. Choose the Approach Based on the Data
There is no single approach that is appropriate for every AI application. A system that works with public product information has very different requirements from one that works with customer financial data.
Questions to consider include:
- What information does the AI actually need?
- Is any of that information confidential, personal, financial, regulated, or contractually restricted?
- Does the AI need the underlying records, or can the existing application perform the sensitive operation?
- Can identifying information be removed?
- Where is the information allowed to be processed?
- How long may it be retained?
- Who should be able to retrieve it?
- What happens if the AI requests information the user is not authorized to access?
Those answers can lead to very different implementations, and several of the approaches above are often combined in a single application.
Using AI Does Not Have to Mean Giving Up Control of Your Data
The question “Can we use AI with our private data?” does not have a single yes-or-no answer.
In many cases, existing applications and databases should continue handling the business rules, calculations, and sensitive operations they already perform well, while AI is introduced only where it provides a specific capability.
That might mean sending a carefully selected piece of information to a business AI service, retrieving only a few relevant sections of internal documentation, using AI to interpret a request without exposing the results, or running a model in a private environment.
The right approach depends on the sensitivity of the data, the business requirements, and how much control the organization needs over where information is processed.
Sensitive data does not automatically rule out using AI. The goal is to give the AI only what it needs, in a place the business is comfortable with, while maintaining its security and privacy requirements.
For the broader process of adding AI to an existing application, see Adding AI to an Existing Application Without Rebuilding It.
If You Are Considering This
If you are considering using AI with internal business data, SCDI can help evaluate the use case, identify what data is actually needed, and design an approach that works with your existing applications and security requirements.