The Shadow AI Systems Running Your Company

"We used to have shadow IT. Now we have the idea of shadow AI. People are just using all kinds of models without any governance." - Tushar Katarki, Head of Product, Red Hat AI Platforms Across large organisations, AI adoption is already happening outside official systems. More than 80% of employees now use unapproved AI tools at work, often without the knowledge or oversight of IT teams, according to UpGuard's 2025 State of Shadow AI Report. That activity is largely invisible. When employees use external generative AI services, organisations lose visibility into how data is processed, where it is stored, and which models are being used. In practice, sensitive data is already flowing into these systems. Reports show that 77% of employees share company data with tools like ChatGPT, including internal documents, customer information, and proprietary code. The pattern extends across functions. In healthcare systems, usage logs show AI tools being accessed across departments , including by clinicians operating in sensitive environments. “We used to have shadow IT. Now we have the idea of shadow AI. People are just using all kinds of models without any governance,” says Tushar Katarki, Head of Product, Red Hat AI Platforms, in a conversation with AIM Media House . Shadow AI is widespread, unsanctioned, and largely untracked. The default response has been to restrict it. But the data shows that the approach fails. Nearly half of employees continue using personal AI tools even after bans are introduced. The Case Against Bans Shadow AI restrictions don’t work. Nearly half of employees would continue using personal AI tools even after explicit prohibitions. About 68% of CISOs use unauthorised AI themselves. The tools are much too accessible, useful, and frictionless compared to the glacial pace of corporate procurement. Joseph Izzo, Chief Medical Information Officer at San Joaquin General Hospital, is already seeing the consequences inside clinical environments. Speaking at the RSAC 2026 Conference, he described how healthcare professionals use AI tools for dosing support, medical searches, clinical summaries, and billing workflows. Much of this activity happens outside approved systems. Clinicians often rely on personal devices and unvetted tools. They use public large language models, creating visibility gaps for security teams. This increases the risk that sensitive patient data enters unmanaged environments. The behaviour is not driven by policy evasion. It is driven by workload pressure. Healthcare professionals adopt these tools to manage their caseload and improve efficiency in settings where time directly affects patient care. Speaking to AIM Media House, Izzo noted that clinicians are not trying to bypass controls. “They want to be more efficient,” he said. That creates a problem that organisations cannot ignore. Security teams cannot monitor or manage systems they cannot see. Yet, employees are already integrating AI into core workflows. Building Visibility, Not Just Walls The conversation needs to shift from restriction to infrastructure. Katarki explains the gap between experimentation and production. “What does production mean? It means I need to provide accountability,” he explains. “That accountability could be everything from SLAs that I guarantee, to governance, from hallucinations to what I would call intentional or unintentional fallout from these AI systems. Then there is auditability. I need to be able to audit root cause problems. And I need to have control. What action can I take in response to that? That’s usually what differentiates what’s in experimentation and what’s in production.” The implication is straightforward. You cannot have production AI without visibility into all AI—authorised and otherwise. You cannot have accountability without knowing what’s actually deployed. And you cannot have control without first understanding what you’re trying to control. This moves shadow AI from a compliance problem to an infrastructure problem. It requires building systems that allow organisations to learn at the speed their employees are already moving. The Scope of Unauthorised Use The problem extends beyond shadow AI tools. Unauthorised adoption exists at multiple layers. Yage Zhang is a researcher at CISPA studying unauthorised API usage. She audited deployments across academic institutions and enterprise environments. Nearly 190 research papers relied on third-party endpoints that weren’t official versions. Nearly half of these failed basic model fingerprint verification, meaning the researchers had no reliable way to confirm what they were actually using. This is a different problem from employees using ChatGPT, but it reveals the same underlying pattern. When people need tools, and official tools are unavailable or too slow, they find alternatives. They don't wait for permission. Speaking to AIM Media House about her findings, Zhang described the implications, “Shadow APIs aren’t just a risk

The Shadow AI Systems Running Your Company

The Case Against Bans

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