Shadow AI: is your company secure?

The rapid adoption of Artificial Intelligence tools in companies has brought significant productivity gains — but it has also opened up space for new invisible risks. According to a Gartner survey, by 2026, more than 80% of companies will have used generative AI APIs or models in their processes. However, a significant portion of this use occurs outside formal technology controls.

This phenomenon has a name: Shadow AI.

Just as with Shadow IT in recent years, employees have begun to adopt AI tools on their own — whether to accelerate tasks, generate content, analyze data, or automate processes. The problem is that, often, this use occurs without validation from the technology department, without data governance, and without visibility for the organization.

According to recent analyses by Forbes, the growth of Shadow AI is directly linked to the ease of access to cloud-based tools and pressure for productivity. This creates a scenario where corporate data may be shared in external environments, without adequate security or compliance controls.

More than just a technological risk, Shadow AI reveals a change in behavior within companies — and exposes important gaps in digital strategy, employee experience, and information governance.

But what exactly characterizes Shadow AI? How does it differ from Shadow IT? And, most importantly, how can companies deal with this phenomenon without stifling innovation?

Throughout this article, we will explore the concept of Shadow AI, its risks and impacts, and how to structure a balanced approach that allows for the safe and controlled use of AI benefits.

Continue reading to understand how to turn the challenge of Shadow AI into a strategic opportunity for your organization.

What is Shadow AI?

The term Shadow AI refers to the use of Artificial Intelligence tools within companies without the knowledge, approval, or governance of the responsible areas, such as IT, information security, or data departments. In practice, this happens when employees adopt AI solutions on their own to optimize tasks, without following corporate guidelines.

This unauthorized use can involve everything from simple interactions, like generating text and analyses in generative AI tools, to more complex activities, such as uploading internal documents, using corporate data in prompts, or automating processes with external tools.

The main factor driving Shadow AI is the ease of access. Today, any professional can use cloud-based AI platforms without the need for installation, formal approval, or advanced technical knowledge. This reduces barriers but also eliminates important layers of control.

Unlike structured AI initiatives — which undergo validation, data governance, policy definition, and system integration — Shadow AI arises spontaneously, decentralized, and invisible to the organization.

It is important to highlight that the problem is not with the use of AI itself. On the contrary: Shadow AI usually arises from a legitimate intention to gain productivity. The risk lies in the absence of visibility, control, and clear guidelines on how these tools should be used.

In this context, Shadow AI represents less of an individual failure and more of an organizational symptom — indicating that the demand for AI solutions already exists but is not yet being met in a structured way by the company.

What are the risks that Shadow AI poses to the business?

As we saw earlier, Shadow AI arises largely due to the absence of governance, validation, and clear guidelines for the use of Artificial Intelligence within companies. However, more than a control problem, the greatest impact lies in the consequences that this unstructured use can generate for the business.

When AI tools are used outside a monitored environment, the risks shift from being merely technical to directly affecting security, compliance, reputation, and decision-making.

Exposure of sensitive data

One of the most critical risks is the improper sharing of information. By entering corporate data into external tools — such as internal documents, financial information, or customer data — there is a risk of exposure in environments outside the company’s control.

Depending on the platform used, this data may be stored, processed, or even used to train models, compromising the confidentiality of the information.

Compliance and regulatory risks

The use of Shadow AI may violate regulatory guidelines, such as the LGPD, especially when it involves personal or sensitive data.

Without control over where and how the data is used, the company may:

• Fail to comply with legal requirements
• Lose traceability over the use of information
• Expose itself to sanctions and penalties

Lack of consistency and control over data

Another relevant impact of Shadow AI is the fragmentation of information within the company. When different areas start using AI tools without standardization or integration, each may operate with distinct sources, criteria, and data.

This scenario compromises the creation of a single and reliable view of the business — which is essential for consistent analysis and strategic decision-making. Without a common base, the organization begins to deal with multiple “versions of the truth,” making alignment between areas difficult and increasing the risk of divergent decisions.

Furthermore, AI tools used without validation can generate inaccurate, biased, or outdated answers. When this information is incorporated into reports, processes, or critical decisions, the impact shifts from being merely operational to directly affecting the company’s strategy.

A practical example of the importance of this consistency is the CondoConta case, which structured a governed data lake with the support of Objective. The initiative allowed for the integration of different data sources and ensured a single and reliable base for analysis, reducing inconsistencies and increasing the predictability of decisions — a crucial factor for any strategy involving Artificial Intelligence.

Reputational and security risks

Finally, incidents related to the inappropriate use of AI can directly impact the company’s image. Data leaks, misuse of information, or even incorrect automated decisions can result in a loss of trust from customers, partners, and the market.

The central point is that Shadow AI is not just an isolated risk, but a cascading effect. The lack of governance and control, as mentioned earlier, translates into real impacts that can compromise everything from operations to the organization’s strategy.

How to identify and map the use of Shadow AI within the company?

The first step in dealing with Shadow AI is not to block tools or restrict access, but rather to gain visibility into what is already happening. In many cases, AI use is already widespread across areas — it just isn’t being monitored.

Mapping this scenario requires a structured approach that combines technology, processes, and an understanding of user behavior.

Survey of tools and accesses being used

The starting point is to identify which AI tools are being used within the organization — both the officially approved ones and those adopted independently.

This can be done through:

• Monitoring access to external applications
• Analysis of logs and network usage
• Application discovery tools (CASB, for example)

This survey helps to understand the true extent of Shadow AI and which areas are most exposed.

Understanding practical use cases

More than knowing which tools are being used, it is essential to understand how and for what purpose they are being used.

Some important questions include:

• Are users entering corporate data into prompts?
• Are they using AI for analysis, content generation, or automation?
• Is there recurring use in critical processes?

This mapping allows for differentiating real risks from isolated uses and identifying opportunities for formalization.

Risk classification by type of use

Not all Shadow AI use represents the same level of risk. Therefore, it is important to classify scenarios based on criteria such as:

• Type of data involved (public, internal, sensitive)
• Tool used
• Purpose of use
• Potential impact on the business

This prioritization helps direct efforts and avoid generic or overly restrictive approaches.

Involvement of business areas

Shadow AI should not be treated solely as a technical problem. It often arises from the need for areas to gain efficiency and agility.

Therefore, mapping should directly involve users, understanding:

• Operational pain points
• Unmet demands
• Processes that could be optimized with AI

As Jorge Sellmer, CRO of Objective, points out, “the uncontrolled use of technology usually does not arise from user failure, but from gaps in the companies’ digital strategy.”

Creation of a continuous monitoring base

Finally, identifying Shadow AI is not a one-time action. The scenario is constantly evolving, with new tools emerging and new behaviors being adopted.

It is essential to structure a continuous monitoring model that enables:

• Updating the mapping of tools
• Tracking new usage patterns
• Adjusting policies as needed

Mapping Shadow AI is, in practice, understanding how AI is already being used in the company — with or without control. And more than mitigating risks, this process opens space to structure a more efficient strategy, aligned with the real needs of the business.

How to structure an AI strategy that reduces Shadow AI and enhances productivity gains?

The growth of Shadow AI should not be combated with isolated restrictions, but with a structured strategy that balances innovation, governance, and user experience.

In practice, unauthorized AI use is a clear sign that the demand already exists — and that the company needs to evolve its approach to meet this need safely and scalably.

More than controlling use, the goal should be to channel this adoption into a structured model that maximizes productivity gains without compromising data, processes, and decisions.

Create a solid data and governance foundation

There is no reliable AI without reliable data. The first step is to structure an integrated, governed, and accessible foundation that allows for the safe use of information across different areas.

Environments such as data lakes and modern data platforms allow for the consolidation of information and the creation of a “single source of truth,” reducing inconsistencies and risks. As highlighted by Objective itself, building data lakes and integrating data are essential for enabling advanced analytics and scalable AI applications.

Offer official and business-oriented tools

If the company does not provide alternatives, users will seek them out on their own.

An effective strategy involves making approved, secure AI tools available, aligned with the real needs of the areas — be it for content generation, data analysis, or process automation.

This reduces Shadow AI while simultaneously enhancing productivity in a controlled manner.

Define clear AI use guidelines

The absence of rules is one of the main factors driving Shadow AI. Therefore, it is essential to establish clear policies on:

• Which tools can be used
• What type of data can or cannot be shared
• How to use AI in critical processes
• Best practices for responsible use

These guidelines should be simple, accessible, and applicable in daily operations.

Integrate AI into the actual workflow

AI needs to be integrated into the systems and processes already used by the company. When this does not happen, users tend to turn to external solutions.

By incorporating AI into corporate tools — such as data platforms, internal systems, and business applications — the company reduces the need for parallel use and increases structured adoption.

A clear example of how to structure this integration is the Arezzo & Co. case, which, with the support of Objective, implemented a data lake on AWS and expanded the use of machine learning at scale. The initiative allowed for automating reports, integrating data across areas, and accelerating decision-making, generating over 50% gain in agility and saving thousands of hours annually.

This type of approach shows that when AI is incorporated into real workflow and supported by a structured data foundation, it ceases to be an isolated initiative — and starts generating consistent and scalable value for the business.

Invest in culture and training

The responsible use of AI does not depend solely on technology. It is crucial to train employees to understand:

• The benefits of AI
• The risks involved
• Best practices for use

This transforms the employee from a risk point into an active agent of the strategy.

Connecting strategy with results

A clear example of how to structure this foundation is the Bauducco case, which modernized its data architecture with the support of Objective. Previously, the company faced a fragmented environment, with scalability limitations and high operational costs.

With the implementation of a more robust and governed data platform, it was possible to improve performance, reduce costs, and accelerate the development of Artificial Intelligence solutions.

This type of initiative shows that reducing Shadow AI is not about control — it is about creating the right conditions for AI to function in a structured way and generate real value.

At Objective, we support companies from maturity diagnosis to building data platforms, governance, and scalable AI solutions, connecting strategy, technology, and business in a structured journey.

If you want to reduce risks, increase productivity, and structure a consistent AI strategy, talk to our specialists and find out how we can help your company evolve safely and at scale.