Artificial intelligence (AI) is rapidly becoming part of everyday work. Employees use AI tools to write, analyze, summarize, code, and make decisions faster than ever before. In many organizations, this adoption is happening organically, without being fully reflected in internal policies, approved tools, or governance structures.
This article explores Shadow AI: what it is, why it appears, and why its most serious risks often remain invisible until an incident occurs.
What is Shadow AI?
Shadow AI refers to the use of artificial intelligence tools by employees without the knowledge, approval, or oversight of the organization. This can include generative AI tools used for drafting documents, analyzing data, generating code, or validating decisions, even when those tools are not officially sanctioned.
This includes generative AI tools used for drafting documents, analyzing data, generating code, or validating decisions, even when those tools are not officially sanctioned.
While Shadow AI is often compared to Shadow IT, the impact is significantly broader. Shadow IT primarily affected infrastructure and tooling. Shadow AI directly influences data flows, judgment, and company culture.
Recent research shows that 38% of employees admit to sharing sensitive work information with AI tools without employer permission. This moves Shadow AI from a hypothetical risk to a reality.
Why employees use unsanctioned AI
To understand Shadow AI, it is essential to understand intent. In most cases, employees are not acting maliciously.
Research consistently shows that people turn to AI tools because they want to:
- Work faster
- Solve problems efficiently
- Meet increasing performance expectations
When approved tools or clear guidance are missing, employees often fill the gap themselves. In that sense, Shadow AI is frequently a rational response to modern work pressures rather than an act of non-compliance.
The visible risks organizations already recognize
Most organizations are aware of the direct risks associated with unauthorized AI use:
- Data leakage and security exposure. A recent poll of CISOs found that 1 in 5 UK companies experienced data leakage linked to employee use of generative AI.
- Regulatory non-compliance, such as GDPR, due to uncontrolled data processing, storage, and cross-border transfers.
- Reputational damage when sensitive information is exposed.
- Financial impact, from incident response costs, fines, or loss of trust.
These risks are serious, but they are also relatively visible. Logs, incidents, and alerts eventually bring them to light, making them easier to anticipate and mitigate.
Shadow AI, however, introduces a deeper layer of risk.
The less visible risks: authority erosion and cultural alienation
One of the most significant but least discussed consequences of Shadow AI is its effect on organizational authority and culture.
An UpGuard study shows that 27% of workers trust AI more than their managers or colleagues for reliable information. This signals a shift in where employees seek validation and guidance.
When AI becomes the default reference point:
- decisions are pre-validated outside formal processes
- managers lose influence without realizing
- accountability becomes unclear
Authority erosion happens quietly. There is no resistance, no obvious policy violation, just a gradual shift in how decisions are made. By the time leadership notices something is wrong, it often surfaces as a compliance breach or a security incident rather than a cultural issue.
Why banning AI is a recipe for failure
Faced with Shadow AI, many organizations react by banning AI tools or rolling out generic awareness training. Both approaches tend to fail.
Banning AI entirely only forces people to become creative. Employees turn to personal devices or unsanctioned accounts, creating blind spots in security and governance. In fact, 53% of decision-makers already report that employees use personal devices for work-related AI tasks.
On the other hand, generic AI training can also backfire. Research shows that employees who receive AI safety training are often among the most frequent users of unapproved tools. Even more telling, UpGuard found that 68% of security leaders report using unapproved AI tools at work, with many incorporating them into daily workflows.
Effective ways to deal with Shadow AI
Successful responses focus on adaptability and creating safe systems in which employees can function:
- Providing access to sanctioned AI tools that employees can realistically use
- Creating clear, practical usage policies that are tied to real workflows
- Updating AI policies regularly, ideally every few months, as tools and risks evolve
- Educating employees in context, based on their actual roles and responsibilities, instead of forcing them to attend a generic awareness training
The goal should never be to slow teams down, but to align productivity with security and accountability.
Remember this…
AI is still new, but it is already embedded in how work gets done. Shadow AI is not a sign of failure. It is a signal that governance, trust, and accountability need to be redesigned for our current times.
Organizations that adapt quickly and deliberately are far more likely to remain compliant and resilient.
If you want to create a safe space for your employees to use AI without introducing hidden risks, we can help you.
