The rise of AI tools has inadvertently turned employee experience into a security issue when it comes to protecting customer data.
The applications employees use to solve everyday problems are data processing environments as much as they are productivity applications. Every interaction with AI, whether a prompt, uploaded file, pasted transcript, spreadsheet extract, customer record, internal policy, or meeting note, can become a point of exposure that the enterprise may not see, control, retain or govern.
Employees will use AI wherever it is easiest. If the tools approved by an enterprise are slow, unavailable, poorly integrated, or locked behind approval processes, they will turn to personal AI accounts.
As Patricia Egger, Co-Founder of Women in Cyber Switzerland and Head of Security at encrypted business cloud storage provider Proton, told CX Today:
“The first issue is that a chat window feels like a private conversation (it's just you and the machine) so employees can forget there's a company, infrastructure, logs and settings behind it.”
A sense of familiarity when interacting with AI tools can cause employees to become comfortable sharing information that should stay within the company.
“The second problem is everything happening outside official channels, people using AI in their personal lives, or at work regardless of what the policy says. We usually call this shadow IT, but we could call it shadow AI,” Egger said.
And with new functionalities emerging at a rapid pace, hidden AI is a moving target.
“New AI tools are popping up literally every day, so knowing what's out there, what people are using, what they're playing around with, it's not easy to keep track. In a business without a dedicated security function, nobody is even trying to keep track, and that's where the exposure builds up,” Egger added.
The result is a growing gap between where organizations think AI is being used and where work is actually happening.
Personal AI Use Has Become an Enterprise Blind Spot
Despite all the discussion around AI security, nearly half of enterprise AI consumption still happens through personal accounts.
According to the State of AI Usage Report 2026 by secure enterprise browser (SEB) technology provider Layer X Security, 47 percent of enterprise AI conversations are conducted through personal identities rather than corporate accounts. These interactions sit outside the normal scope of enterprise AI security, making them difficult to monitor as they are often disconnected from corporate data retention and compliance policies.
Employees are not trying to create risk. In many cases, they are trying to get work done. They may be drafting emails, summarizing documents, analyzing customer feedback, preparing meeting notes, troubleshooting technical issues or rewriting support responses. The risk emerges when those tasks are carried out in environments the business cannot see.
Even corporate identity does not fully solve the problem. The data also shows that more than 14 percent of conversations carried out using a corporate identity are happening via personal AI licenses. While an employee may appear to be operating within a business context, the underlying license may still allow data to be used for model training or sit outside enterprise-grade protections.
The organization may believe employees are using AI safely because they are signing in with work credentials. But in reality, the data may still be leaving the confines of its environment.
Sensitive Data Exposure Is Already Happening
AI chatbot users recently found that conversations in Claude, Grok and Meta AI can show up in Google search results via share links, exposing sensitive data, including names, addresses, work notes and cryptocurrency wallet keys to the public web.
More than six percent of enterprise AI conversations contain sensitive data, according to the Layer X report. ChatGPT, in particular, has a sensitive data exposure rate of 8.38 percent, making it the largest enterprise AI data exposure channel.
Sensitive data exposure can take many forms. An employee might paste a customer complaint containing contact details into an AI tool to generate a response. A sales manager might upload pipeline notes to summarize account risks. A support leader might analyze call transcripts that include personal information. A product team might use AI to review bug reports containing internal system details. Or a finance employee might ask AI to explain a spreadsheet containing commercial information.
In each case, the employee’s intent to save time or improve quality may be reasonable, but the data protection risk depends on the environment in which that task takes place.
In an interview with CX Today, Adam Spearing, EMEA Head of AI Innovation at ServiceNow, cited an unnamed customer that
“found 20,000 AI agents, what we call shadow AI, that have been built by people for all good reasons, with all good intention, doing stuff that the IT organization had no clue about.”
A lack of visibility has implications for governance and compliance, because if a business cannot see which AI tools are being used, which identities are being used to access them, what data is being shared, or how that data is handled, it cannot make credible claims about control.
The Workforce Experience Problem Behind AI Risk
Contact center agents, sales teams, customer success managers, marketers and service leaders under pressure to respond quickly to customers in personalized interactions and extract insight from unstructured data are likely to experiment with personal AI tools that they find to be fast and flexible.
Enterprise tools, by contrast, can be fragmented. Access may be limited to certain teams and approved AI assistants may not connect to the systems employees actually use. Some platforms may be available but poorly communicated.
As Simon Morris, Senior Director of Solution Engineering for Freshworks, put it in a CX Today roundtable discussion: