Shadow AI is often defined narrowly as the AI tools employees use without IT or security approval; like ChatGPT, Claude, and the thousands of AI features baked into everyday SaaS apps. But the tools are only one layer. The bigger governance gap is in how AI is being used: whether the task itself is an approved use case, whether the content being shared is privileged or confidential, and whether the interaction falls within the boundaries of your acceptable use policy.
An employee using a fully sanctioned AI tool can still create serious risk by pasting in client-privileged information, regulated health data, or unreleased financials. Or by relying on AI for decisions your policy says require a human in the loop. Conversely, a quick grammar check in an unsanctioned tool may be entirely low-risk. Shadow AI matters because the risk lives at the intersection of tool, task, and data, and most organizations have very little real time visibility into the combination of all three.
That visibility gap also has direct regulatory consequences. Frameworks like the Colorado AI Act, the EU AI Act, and NIST AI RMF classify certain uses of AI (e.g. hiring, lending, healthcare, education, and other consequential decisions) as high-risk AI systems that trigger documentation, risk assessment, and human oversight obligations. Without a clear picture of how employees are engaging with AI, organizations can’t tell whether shadow usage has pulled them into high-risk territory and can’t demonstrate the reasonable care these frameworks require.
Maro discovers shadow AI continuously across all three dimensions, ranks usage by actual risk and in accordance with your policy, and helps you enforce acceptable use in real time, with the audit trail regulators expect.