With the rise of AI chatbots, Shadow AI has become very popular among talent working in fast-paced environments and looking to complete tasks on time. But it can also pose a potential risk for the company.
Here we break down everything you need to know about Shadow AI:
What Is Shadow AI?
According to IBM, Shadow AI refers to the use of artificial intelligence tools inside an organisation without the knowledge, approval, or oversight of its IT or security teams.
For instance, an employee might use an AI chatbot to draft emails faster, upload internal data into an online AI tool to generate insights, or paste company code into a generative AI platform to troubleshoot problems.
What Does Shadow AI Do?
Most shadow AI tools are used to simplify everyday work. They generate content, summarise long documents, analyse spreadsheets, automate repetitive tasks, and even write code. Because many of these tools are cloud-based and easy to access, increasingly talents see them as ways to make their work faster and cover more ground.
The concern is the data being fed into these systems. When sensitive company information, customer records, financial details, or proprietary research are uploaded into external AI platforms, that information may leave secure internal environments. Once it moves outside approved systems, organisations lose visibility and control over how it is stored, processed, or retained.
Unlike traditional unauthorised software, AI tools don’t just store information. They process it, generate new outputs from it, and sometimes improve their systems through user interactions. That added layer of complexity makes the risks harder to track.
What Harm Can It Cause?
The most immediate danger is data exposure. Employees may unintentionally share confidential material with third-party AI providers, creating the possibility of leaks or misuse. In regulated industries, this can trigger legal consequences, compliance investigations, and significant financial penalties.
There is also reputational risk. AI-generated content can contain factual errors, biased conclusions, or misleading statements. If such material is used in official communications or decision-making without verification, the damage can extend beyond internal mistakes to public trust.
Another concern is flawed business judgement. AI models are only as reliable as the data and assumptions behind them. If employees rely on unapproved systems for strategic analysis or forecasting, leadership decisions may rest on incomplete or inaccurate outputs.
Why Is It Growing?
Shadow AI is spreading because artificial intelligence tools are widely available, inexpensive, and designed to save time. In fast-paced workplaces, employees often prioritise speed and convenience. Waiting for internal approvals can feel slower than simply opening a browser and trying a tool immediately.
This gap between rapid innovation and slower governance creates the perfect conditions for shadow AI to thrive.
How Can People Protect Themselves?
For organisations, protection starts with clarity. Clear policies about AI usage, approved tools, and data handling expectations reduce confusion. Providing secure, vetted AI alternatives internally can also limit the need for employees to look elsewhere. Regular training helps teams understand that uploading sensitive information into public platforms carries consequences, even if the intention is harmless.
For individuals, awareness is key. Before entering company data into any AI system, it’s important to understand where that data goes and whether the tool is authorised. If policies are unclear, asking first is safer than assuming. Convenience should never outweigh responsibility.
Shadow AI is not about banning artificial intelligence. It’s about recognising that powerful tools used without oversight can introduce risk. Understanding how it works and where it can go wrong is the first step toward using AI in a way that supports innovation without undermining security or trust.
Ogbonda Chivumnovu
