Your Biggest Security Blind Spot
AI tools have quickly become part of how work gets done. Employees are using them to write emails, analyze data, generate code, create documents all in an effort to move with greater efficiency. In many cases it is working. Some studies have shown performance increases of up to 66%.
But there is a tradeoff that is getting overlooked. Most organizations do not actually know which AI tools are being used across their environment or what data is being shared with them. A recent study found that 97% of organizations are already using or piloting AI coding assistants, and all of them have AI-generated code in their codebases. At the same time, 81% lack visibility into how those tools are being used, and 65% report increased security risk tied to AI.
Another data point makes this even more concerning: 55% of employees say they are using AI tools that have not been approved by their organization.
This is what is known as Shadow AI usage and it is already happening at scale.
What Shadow AI Actually Means
Shadow AI is about how data is being used in ways that security teams cannot see or control.
In practice, it looks like employees pasting sensitive customer or company data into public AI tools or developers using large language models to fix code that may contain credentials. None of this is typically done with bad intent. People are trying to be more efficient and meet expectations within their work environment. But without visibility, that behavior introduces real risk.
The Real Problem Is Visibility
Security teams cannot protect what they do not know is happening. Right now, data is more distributed than ever. According to IBM, 35% of breaches involve data stored in unmanaged or “shadow” sources. Shadow AI is accelerating that problem by creating new paths for data to leave the organization without any security oversight. Once that visibility is gone, control goes with it.
Where Shadow AI Starts to Create Risk
The most immediate issue is data exposure. When sensitive information is entered into AI tools with unclear usage policies, organizations lose control over where that data goes and how it may be used. In most cases, there is no audit trail which makes it difficult to trace or contain potential issues.
There are also clear compliance implications. For organizations operating under regulations like GDPR or HIPAA, not knowing how data is being processed or where it is stored can quickly become a reportable problem. Shadow AI introduces gaps in governance that many teams are not equipped to manage. Every AI tool introduces another potential entry point. These tools frequently sit outside of traditional security controls and create exposures that are not actively monitored.
What makes this more challenging is that Shadow AI does not behave like traditional threats. It does not generate obvious alerts or follow expected patterns in logs. In many cases there is no clear signal that something is wrong until after the fact.
Why Traditional Security Is Not Catching This
Most security controls were not designed for how AI tools are being used today. Without deeper inspection into the AI tool and activities, it is difficult to see what data is being shared. They also do not behave like traditional applications, which makes monitoring and logging more complex.
There is also an identity component that often gets overlooked. Employees may create multiple accounts across different AI tools. Over time, this creates fragmented identities and additional exposure that is difficult to manage.
AI adoption is accelerating across every industry, and employees are not waiting for formal approval before using tools that help them work more efficiently. As a result, new tools are being introduced daily, and data is being shared in ways that organizations are not fully aware of. Unlike previous technology innovations, AI tools can interact directly with business data, which raises the stakes.
A More Practical Approach: Visibility First
Blocking AI usage entirely is not realistic. The focus needs to shift toward visibility and control.
Organizations need to understand which AI tools are being used and what data is being shared to know where potential risk exists. This is where Shadow AI Monitoring becomes important. It is not about restriction. It is about visibility into something already happening.
AgileBlue Shadow AI Monitoring
AgileBlue’s Shadow AI Monitoring helps organizations understand how AI tools are actually being used across their environment. It gives security teams the visibility they need to reduce risk and protect sensitive data.
To learn more, visit our Shadow AI Monitoring webpage.
