Artificial intelligence has rapidly integrated into the daily routine of companies. Employees use chatbots to write emails, analyze documents, summarize meetings, generate code, research information, and automate tasks that previously required more time.

For many organizations, this movement is positive. The problem begins when the technology arrives before the rules that should accompany it.

An employee might open a personal account on ChatGPT, Claude, or another tool, copy an internal document, and ask for help summarizing its content. A developer might send a snippet of proprietary code to a programming tool. A sales professional might input customer information to prepare a presentation.

None of this necessarily looks like a security incident at the moment it happens. Still, the company may have lost control over the data used in that interaction. This phenomenon is called Shadow AI.

What is Shadow AI?

The term describes the use of artificial intelligence tools in the shadows, without the organization's knowledge, approval, or governance. The concept is similar to the old Shadow IT, but it goes beyond installing software; the employee can send company information to an external system, receive AI-generated responses, and use them to guide decisions and tasks within the organization.

The situation is more common than it might seem. A Microsoft survey revealed that 71% of surveyed employees in the UK had already used unauthorized AI tools at least once, and more than half did so weekly.

This can happen because the tool authorized by the company does not meet a specific need, has access limitations, or is simply unavailable. The employee then turns to the alternative they already know, often without considering what information is being shared with the service or how that information will be used.

Where is the risk of Shadow AI?

It is very practical to treat Shadow AI as a list of prohibited tools. Blocking ChatGPT, Claude, or any other service might reduce some of the exposure, but it does not solve the problem alone. The risk lies in the behavior during use.

Among the data that may appear in this type of interaction are:

• customers' personal information;

• contracts and internal documents;

• proprietary source code;

• financial data;

• commercial strategies;

• information about products not yet launched;

• credentials and technical configurations;

• information protected by confidentiality agreements.

Microsoft itself defines Shadow AI as the use of AI tools without the knowledge, approval, or governance of IT and security teams, highlighting risks of data leakage, compliance issues, and reputational damage.

Even when no sensitive data is sent, another risk exists: losing visibility over how AI is influencing decisions and content.

If a report was written with the help of an unauthorized tool, who can verify what information was provided to the model? If a recommendation was generated by AI and was wrong, is there a record of that interaction? If code produced by the model introduced a vulnerability, is it possible to reconstruct how it was created?

The ripple effect of a wrong answer

Shadow AI also connects to another known problem of generative artificial intelligence: hallucinations.

A language model can produce an incorrect response that looks like reliable information. The text may be well-written, present coherent arguments, and even use specific numbers. None of this guarantees that the information is correct.

The risk increases when the response enters a larger process. An employee asks the AI something. The incorrect response becomes a report. The report is used by another professional. The information reaches a presentation. Then, it becomes part of a decision. The initial error was small. The problem grew because no one noticed where it started.

This type of effect is especially concerning in corporate environments because AI participates in support, development, financial analysis, customer service, research, and document production workflows. The more steps depend on an automatically generated response, the greater the impact of incorrect information can be.

A more consistent strategy is to provide the system with reliable sources and specific context to produce the response. This is where RAG comes in.

How does RAG connect AI to company knowledge?

RAG, or Retrieval-Augmented Generation, is an approach that combines language models with information retrieval mechanisms.

Instead of relying exclusively on the knowledge acquired during the model's training, the system searches for relevant information in a base defined by the organization before formulating the response.

In a company with thousands of contracts, manuals, internal policies, and technical documents, an employee can ask the AI what the correct procedure is for a given situation. In a system without access to internal knowledge, the model must respond based on what it has already learned and the information present in the conversation. In a system with RAG, the application can first consult the relevant corporate documentation and then provide that context to the model.

The process can be summarized like this:

Question → search in the knowledge base → retrieval of relevant documents → context sent to the model → response grounded in the sources found.

This does not eliminate hallucinations. A poorly configured RAG can also retrieve inappropriate documents or provide insufficient context.

The difference is that the application can provide the model with information selected by the organization itself as context for the response.

This approach is already adopted in corporate AI projects. CodeBit, for example, uses RAG in solutions developed to connect language models to specific document bases. In a virtual assistant project for physical security, the approach was used to combine contextual search in documents with natural language response generation.

For corporate environments, this architecture also allows establishing limits. The system can work with authorized documents, present the sources used, and restrict responses that are outside the defined scope.

Governance must accompany AI use

If the company simply bans AI tools, there is a good chance some employees will continue using them secretly. The challenge then becomes creating a framework where the authorized path is also the most convenient for the user.

This involves establishing clear rules on:

• which tools can be used;

• what information can be sent to the models;

• which data is prohibited;

• which models are available for each area;

• how interactions are recorded;

• who can access each resource;

• when a response needs to be reviewed by a human;

• how AI-related incidents should be handled.

Governance must also keep pace with the evolution of tools.

This is particularly important with the use of agents. A system that only answers a question presents one type of risk. An agent with access to email, code, documents, or internal systems presents another.

How to reduce Shadow AI without blocking innovation?

An effective AI policy must start from the reality that employees already have easy access to these tools. The company can try to prevent this use or create conditions for it to happen in a controlled manner. The second option tends to be more sustainable.

The first step is to understand the current scenario. Which tools are being used? Which departments use AI? For which activities? What types of data are being sent?

Next, the organization can establish a layer of approved tools that offers features close to those that employees seek in external alternatives. It is also important to create different access levels. Not every employee needs to have access to the same models, documents, or agents.

An internal policy can, for example, allow the use of AI for low-risk tasks while requiring corporate tools and human review for financial information, personal data, proprietary code, or decisions affecting clients.

The CodeAdvisor was developed precisely to centralize the corporate use of AI in a controlled environment, with governance, auditing, and models managed by the organization. The platform allows tracking interaction history, consumption, and usage per employee, in addition to offering integration with IDEs and secure environments on AWS.

When professionals find the tools and information necessary to work within the organization, reliance on external solutions tends to decrease.

For organizations that need to connect AI to their own knowledge, there is also a complementary architecture. CodeRag creates assistants based on the company's documents, videos, audios, and images, and presents the references used in the responses.

These are related but different problems. CodeAdvisor mainly addresses the governance and centralization of AI use. A RAG architecture helps connect the model to the organization's specific knowledge.

AI must be inside the company

The challenge of Shadow AI is to incorporate artificial intelligence into work without losing control over data, processes, and decisions. An organization can have excellent policies on paper and still not know how many employees use external tools, what information is being shared, or which decisions are being influenced by unmonitored systems.

Artificial intelligence has made this problem more urgent because the barrier to start using a new tool is very low. In just a few minutes, any professional can test a model, create an automation, or integrate an API.

The clearer, more accessible, and more suited to daily routine the corporate technology strategy is, the greater the chance that professionals will find the tools they need to work with AI within the company itself. The goal is to make this use happen in accordance with the organization's processes, with appropriate resources, reliable knowledge, and security.