In recent years, artificial intelligence has ceased to be a concept of science fiction and has become a daily tool. From optimizing delivery routes to generating texts and images, AI is everywhere. This ease of access, however, has created a new and risky phenomenon that most organizations still don’t know how to manage: Shadow AI.
But what exactly is it? In simple terms, Shadow AI refers to any AI system, application, or tool used by a company’s employees without the knowledge, approval, or supervision of the IT or data governance department. It is the modern version of “Shadow IT,” where employees used personal applications like Dropbox or Google Docs for work tasks, bypassing official corporate tools. Today, instead of cloud storage, we are talking about language models, data analysis platforms, and automation tools.
Shadow AI is not born out of malicious intent. On the contrary, it usually arises from proactivity. An employee wants to be more efficient, find a faster solution, or innovate on a project. They find a new AI tool online, often free or low-cost, and start using it immediately. The problem is that this agility completely ignores the company’s security, compliance, and ethical protocols.
Example
To understand the danger, let’s elaborate on a very common example. Imagine the marketing team of a mid-sized company. They have just completed a large customer satisfaction survey and have thousands of text comments to analyze. The manual process would take weeks. The internal data team is overloaded with other priorities and says they can only help next quarter.
A marketing manager, eager for results, searches and finds an online tool that promises “AI Sentiment Analysis in Minutes.” He signs up, perhaps using his corporate email, and to get the most accurate analysis, he uploads the complete survey file. This file contains not only the comments but also customer names, email addresses, and, in some cases, purchase histories. In ten minutes, he has a beautiful report. The problem is solved, right?
Wrong. What this manager failed to realize is that the terms of service of that free tool state that all uploaded data may be used to train its models. Now, the company’s confidential customer data is on a third-party server, outside the organization’s control. If this tool is hacked, or if it simply uses this data publicly, the company faces a massive data breach. This can result in heavy fines for violating privacy laws (such as the LGPD in Brazil or GDPR in Europe), loss of customer trust, and irreparable damage to reputation.
This example illustrates the core problem of Shadow AI. The search for efficiency creates significant vulnerabilities. Unverified tools may have security flaws, may “steal” intellectual property, or may operate with algorithmic biases that expose the company to legal risks. Furthermore, the company may end up with dozens of redundant tools, paid for by different teams, leading to unnecessary costs.
Combating Shadow AI is not about prohibiting innovation or blocking access to new technologies. That would only encourage employees to hide their tools even more. The true solution lies in governance and education. Companies need to create clear policies on the use of AI, offer quick channels for IT to evaluate and approve new tools, and, most importantly, educate their teams about the risks. The goal is not to eliminate AI, but to bring this innovation from the shadows into the light, ensuring it is used safely, ethically, and aligned with business objectives.
Strategies for Managing Shadow AI in Companies
The first step is Employee Awareness and Education. Employees cannot be expected to comply with rules they don’t know or understand. Companies need to invest in AI literacy, teaching everyone, from the executive body to the operational base, about the risks associated with using third-party tools, such as the leakage of sensitive data or intellectual property, and algorithmic bias issues. This training must go beyond security, explaining how AI works and why it is vital to use approved platforms. It is crucial for people to understand that Shadow AI thrives where the corporate solution is perceived as inefficient.
Next, it is essential to establish a Clear and Agile AI Governance Framework. This means the organization must define ethical principles and values that guide the use of artificial intelligence, specifying what types of data can be processed by AI, and in which environments. IT and Information Security must create a rapid process for the evaluation and approval of new AI tools requested by business teams. Instead of saying “no,” the focus should be on “let’s evaluate and find a safe way to use it, or provide a superior corporate alternative.”
Visibility and Technical Control are also crucial. The company needs tools capable of monitoring network traffic and identifying the use of unsanctioned AI applications. This goes beyond traditional software control, as many Shadow AIs are cloud-based services. IT must be able to detect what data is being input into external models to, if necessary, intervene or block access to platforms that pose an unacceptable risk, especially those that use confidential information to train their models.
Finally, the organization must focus on Offering Safe and Efficient Alternatives. To truly combat Shadow AI, it is necessary to eliminate the main reason for its existence: the search for faster, more productive solutions. IT should be seen as an innovation partner. This may mean creating a corporate “testing environment” (a secure sandbox) where employees can experiment with new AI tools using anonymized or simulated data, or providing internal and already validated generative AI tools that meet the needs of the teams safely and with higher quality, transforming Shadow AI into Innovative and governed AI.
Tools for Management
Managing Shadow AI requires the use of specific tools that transform invisible and uncontrolled usage into visibility and governance. The main solution categories focus on monitoring network traffic and enforcing security policies in cloud environments.
The most effective tools for detecting Shadow AI fall largely into the category of Cloud Access Security Broker (CASB), but are complemented by other network security and governance solutions.
1. Cloud Access Security Broker (CASB)
CASB is the central tool in the fight against Shadow AI, as it was originally developed to combat Shadow IT (unauthorized use of SaaS). It acts as a control point between users and cloud service providers, inspecting traffic and enforcing security policies.
- Main Function: CASB monitors all network traffic to the cloud. It can identify an employee accessing a new AI service, such as a content generator or a data analysis chatbot, even if the application is not officially sanctioned. The main CASB feature is Visibility, which allows listing all cloud applications in use and evaluating their risks.
- Action against Shadow AI: Upon identifying a new AI service, the CASB can apply Data Loss Prevention (DLP). For example, if an employee tries to upload a file containing financial information or personal customer data (detected through DLP patterns) to an unapproved AI tool, the CASB blocks the action, preventing leakage.
- Examples of Solutions:
- Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security – MCAS)
- Zscaler (through its SASE/Zero Trust solutions)
- Forcepoint, Check Point: Traditional security providers that also offer integrated CASB features.
- Cost Issue: CASB solutions are generally sold per user/month license or as part of larger security packages, such as SASE (Secure Access Service Edge). For large companies, the cost can be high, but it is justified by protection against compliance fines (LGPD, GDPR) and data loss, which can be financially catastrophic. The cost is typically customized based on the number of users and the necessary modules (DLP, threat protection, etc.).
2. AI Governance and Operations Platforms (AIOps/MLOps)
These tools are not for detecting Shadow AI, but rather to provide a safe and governed alternative that eliminates the need for external tools, becoming a proactive strategy. They allow internal teams to build and manage their own AI models centrally.
- Main Function: Offer an internal environment where corporate data can be used to train and run AI models securely, with logs, auditing, and access controls.
- Action against Shadow AI: By offering an agile platform, such as Azure Machine Learning or Google Cloud Vertex AI, the data scientist or business analyst has fewer incentives to resort to external and ungoverned solutions.
- Examples of Solutions:
- Google Cloud Vertex AI
- Microsoft Azure Machine Learning
- IBM Watson Studio
- Cost Issue: The cost is typically based on resource consumption (CPU/GPU processing time, data storage) and not per user license, which allows flexibility, but can have a high initial infrastructure cost and requires investment in specialized teams.
3. Endpoint and Browser Security Solutions
To capture the use of Shadow AI on end devices, endpoint and browser protection solutions have gained new AI-focused features.
- Main Function: Monitor what is being typed or copied and pasted into chatbot applications or browser-based code generators.
- Action against Shadow AI: Browser security products can detect an employee copying proprietary source code and pasting it into a ChatGPT prompt or similar. They can prevent specific data classified as confidential from leaving the secure endpoint environment.
- Examples of Solutions: Modern DLP and Endpoint Detection and Response (EDR) solutions, such as Trend Micro or Palo Alto Networks.
- Cost Issue: These functionalities are often included in existing EDR/DLP security packages, but may require the activation of advanced modules, which increases the total cost per user license.
The financial challenge of these tools lies in their Licensing Cost, Implementation, and Technical Specialization. While CASBs offer the best visibility, the price per user can be high. The key is to choose solutions that integrate with the existing security infrastructure, maximizing the value of the investment.