The adoption of Artificial Intelligence (AI) in companies is growing rapidly—and with it come new challenges. One of the most pressing is Shadow AI: the use of AI systems without the approval, oversight, or knowledge of the IT team or corporate governance.
The term draws from the concept of Shadow IT, where unauthorized tools or systems are used by employees outside of the company’s control processes.
In the case of Shadow AI, the risks multiply—especially because it involves generative models, the collection and processing of sensitive data, and large-scale automated actions.
With that in mind, in this article, we’ll explain what Shadow AI is, why it poses a real threat, the potential security impacts, and how to prevent it within your organization. Let’s dive in.
What is Shadow AI?
Shadow AI refers to the use of artificial intelligence tools, models, or functionalities within a company without the formal knowledge or authorization of the IT, compliance, security, or data governance departments.
This often happens when professionals rely on solutions like ChatGPT, Copilot, Gemini, and others to optimize tasks, automate workflows, or accelerate delivery—yet do so without following the organization’s established policies and procedures.
Examples of Shadow AI include:
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Inputting confidential data into generative AI tools without control
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Creating autonomous agents without technical validation
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Running AI-based automations via unofficial APIs
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Training models using unmasked corporate data
While the unauthorized use of these technologies may seem harmless at first, it brings significant risks.
How Does Shadow AI Spread Within Companies?
Several factors explain why Shadow AI is growing so rapidly:
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Easy access to tools: With just a few clicks, any employee can start using advanced AI solutions—without upfront costs or formal approval.
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Lack of clear policies: Many organizations have yet to define clear guidelines for the responsible use of AI, leaving a critical gap.
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Productivity pressure: Teams under aggressive performance targets often seek quick solutions, even if not officially authorized.
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Technical unawareness: Not all employees understand the risks involved in using AI outside secure corporate environments.
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Lack of monitoring: Without tools to track or audit AI usage, Shadow AI spreads unchecked.
What Are the Risks of Shadow AI?
Below are the main risks associated with Shadow AI:
1. Exposure of Sensitive Data
Tools like ChatGPT and other AI platforms use user-provided data to train their models. This means that any information entered into prompts—such as customer details, contracts, or corporate strategies—may be stored and eventually used to improve the model, leading to the exposure of confidential data.
2. Compliance Breaches and Legal Violations
The unauthorized use of AI may violate data protection regulations such as LGPD (Brazil’s General Data Protection Law), GDPR, or other sector-specific laws. This can result in legal sanctions, operational restrictions, and multi-million-dollar fines.
3. Execution of Incorrect Actions
Unsupervised bots and automations can cause severe operational errors—from sending emails to the wrong recipients to mishandling critical data. These failures jeopardize business continuity and reputation.
4. Loss of Traceability
Without control over which tools are being used, it’s impossible to track what was done, by whom, using which data, and under what circumstances. This lack of traceability hinders investigations, problem resolution, and continuous monitoring.
How to Avoid the Risks of Shadow AI
Eliminating Shadow AI in organizations requires an approach that combines governance, awareness, and active monitoring. Below are five key practices to help contain this risk:
1. Establish a Corporate AI Usage Policy
The first step in preventing Shadow AI is to create a clear and formalized policy on how AI should be used within the company. This document should define:
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Approved tools: Which AI solutions are permitted?
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Permissible data types: What kind of information can be entered into these tools (and which are strictly prohibited, such as personal, financial, or customer data)?
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Security best practices: Guidelines on authentication, data anonymization, approved usage channels, and operational limits.
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Consequences of misuse: Sanctions, warnings, or access restrictions in case of policy violations.
In addition, this policy should be reviewed periodically to keep up with technological advancements and emerging AI use cases within the corporate environment.
2. Implement Traceable Solutions
The foundation of an effective governance strategy is transparency. Therefore, it’s essential to invest in AI tools that:
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Record detailed logs of all interactions with AI models and agents;
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Provide access control based on user profiles, restricting usage by department, project, or sensitivity level;
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Integrate with observability systems and cybersecurity tools.
This enables the identification of abnormal behavior, unauthorized access, and even the automation of responses to potential violations. Traceability is also critical for auditing and regulatory compliance (e.g., LGPD, GDPR).
3. Promote Continuous AI Education
Many cases of Shadow AI don’t stem from bad intentions, but from a lack of proper guidance. That’s why continuous education must be a core component of the company’s strategy. Your plan should include:
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Regular workshops and training sessions on ethical and secure AI usage;
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Support materials explaining both risks and benefits;
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A guide to authorized corporate AI tools, including clear usage instructions;
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Open communication channels with the security or governance team.
4. Deploy Continuous Monitoring Tools
Protection against Shadow AI is only truly effective when combined with continuous monitoring, traceability, and centralized control over user activities, automations, and corporate applications.
Key measures include:
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Using observability platforms capable of detecting abnormal behavior—such as frequent uploads to public AI platforms, unauthorized extension usage, or irregular API consumption;
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Configuring automated alerts for anomalies that may indicate the use of non-approved AI agents;
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Integrating with incident response and cybersecurity tools, ensuring corrective actions can be applied in real time.
In This Context, BotCity Stands Out as a Strategic Ally
BotCity provides:
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Complete visibility into all running automations and AI agents within your organization;
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Centralized traceability of logs and executions, including version control for code and pipelines;
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Secure, governed orchestration of automated workflows with role-based access controls;
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Integration with existing security and monitoring systems.
Learn more: How to avoid Shadow IT?
How BotCity Helps Mitigate Shadow AI Risks
BotCity is an enterprise automation platform designed to ensure governance and control over the use of AI and RPA within corporate environments.
With BotCity, your organization can bring AI initiatives into a secure, auditable, and compliant environment, preventing Shadow AI from growing outside IT’s oversight.
This framework ensures security, transparency, and compliance, effectively eliminating risks associated with Shadow AI.
Ready to learn more? Talk to one of our specialists and discover how BotCity can support your AI governance strategy.
