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As AI adoption accelerates, shadow AI is becoming the bigger risk
This article is part of our Opinions section, where we invite industry professionals to share their views on the most pressing technology questions of our time.
Here, Dan Clarke, President of Truyo, explores why shadow AI is emerging as one of the biggest risks associated with enterprise AI adoption. The article also examines why organisations must prioritise visibility and governance to ensure AI innovation is both responsible and secure.
Enterprise organisations are largely focused on adopting AI as quickly as they can, eager for the productivity gains and competitive advantages. But other than the sloppiness that can come with rushed adoption, there’s another, urgent danger lurking: shadow AI.
Shadow AI refers to tools that employees use which haven’t been approved at the corporate level, and it’s rapidly becoming one of the biggest governance challenges facing enterprise organisations today.
Where organisations get into trouble
The AI tools that enterprise orgs approve usually go through a committee, are vetted against the company’s policy, and have been through normal security and compliance checks. They’re able to be used across the team in a way that’s responsible and safe. Meanwhile, the AI that employees use that haven’t gone through such processes are where companies get into trouble.
It’s very typical for a business to have five to 10 approved use cases, but when a third-party performs a scan, to discover 50, 100, or even 200 additional use cases, all because of shadow AI. This happens even when organisations have enacted a policy around AI, and even when it explicitly states that employees aren’t to use AI tools that haven’t been specifically approved by management.
The issue is that team members don’t adhere to policy alone. Even if a company goes beyond that and puts a blocker on a platform like ChatGPT on its laptops, employees can just pull out their phones, take a photo of what they’re working on, and use the platform anyway.
The hidden risks of shadow AI
The main problem with shadow AI is a lack of security. Many people aren’t aware that a lot of AI software exfiltrates data, meaning information entered into the platform may be stored, retained, or used in ways the user never intended.
Employees often assume they’re using AI responsibly because they’re trying to solve a business problem. They may upload a document for summarisation, ask an AI assistant to review a report, or use a tool to help screen resumes. In many cases, they don’t realise how much sensitive information they’re sharing in the process.
That risk extends beyond public AI tools. Many software vendors are rapidly introducing AI capabilities into existing products, often making them easy to enable with little more than a settings change. A team member may activate an AI feature designed to make their job easier without fully understanding what data the model can access, how it learns, or whether the capability has been reviewed internally. The result is that organisations often have far more AI in operation than leadership realises.
Most employees are trying to become more productive. They are looking for ways to save time, automate repetitive tasks, and improve their output. AI makes those goals easier to achieve, which is exactly why shadow AI has become so widespread.
Why shadow AI is growing so quickly
The speed of AI adoption is directly tied to how accessible the technology has become. Artificial intelligence has existed for years, but accessing advanced AI capabilities used to mean hiring developers, data scientists, or machine learning specialists. Large language models changed that equation. Today, anyone who can write a prompt can use AI.
Human resources teams can create screening workflows, finance departments can build reporting assistants, and customer service groups can launch chatbots. Employees no longer need technical skills to build powerful AI-driven workflows. The same factor that makes AI valuable also makes it difficult to govern.
Many of the people creating agents and automations have never been responsible for evaluating security requirements, compliance obligations, data privacy concerns, or intellectual property risks. They’re focused on solving a business problem, not navigating governance processes. As a result, AI can spread throughout an organisation far faster than traditional technology deployments ever could.
Governance starts with visibility
Policies are important, but they only work if leaders understand how AI is already being used. Otherwise, governance becomes little more than a set of rules disconnected from reality. An organisation cannot govern AI it doesn’t know exists.
That is why visibility has become one of the most important components of AI governance. Before leaders can determine what should be approved, restricted, monitored, or expanded, they need an accurate picture of how AI is currently being used across the business.
That includes approved platforms, employee-created workflows, AI capabilities embedded within third-party software, and the growing number of agents being built by business users.
In many cases, the greatest risks don’t come from the use cases organisations perceive as dangerous. High-profile applications involving legal decisions, policy interpretation, or regulatory compliance typically receive significant scrutiny. The bigger challenge often comes from everyday uses that seem harmless.
Resume screening, report generation, chatbot deployments, workflow automations, and content creation tools frequently have access to large amounts of organisational data. Because these applications are viewed as routine, they may receive less oversight despite carrying meaningful risks related to privacy, bias, intellectual property, and data exposure.
Visibility allows organisations to identify those blind spots before they become larger problems.
Building a more mature approach
Traditional governance models were designed for a slower pace of technology change. Organisations could review software annually, update policies periodically, and rely on lengthy approval processes without creating major operational challenges. AI does not operate on that timeline.
New models, capabilities, and applications appear constantly. Features that did not exist a month ago may suddenly become available across an organisation’s software stack. Waiting for annual reviews or infrequent audits leaves too much room for risk to accumulate. A more effective approach begins with understanding where AI is being used today.
Most organisations already have AI policies and approved use cases. The challenge is that AI adoption rarely follows a neat approval process. Employees find new ways to use it every day, often through tools the organisation already owns. As a result, AI usage typically expands much faster than governance efforts can keep up. That makes understanding AI usage critical. Leaders can’t manage risks they can’t see, and they can’t address problems they don’t know are there.
Training can also play an important role. Employees who understand issues like data privacy, intellectual property risks, bias, and information exposure are better equipped to make responsible decisions. They are also more likely to recognise when a particular use case should be reviewed before deployment.
The new competitive divide
Shadow AI has become a natural byproduct of rapid adoption. It exists because employees are finding value in the technology and using it to solve real problems. The danger emerges when those activities occur without visibility, oversight, or an understanding of the associated risks.
Organisations that succeed with AI will be the ones that develop the visibility and governance needed to manage it responsibly.
The conversation has moved beyond adoption. Today’s challenge is understanding the AI that is already inside the business and ensuring it is being used in ways that support both innovation and accountability.
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