Shadowcue

Organizations may see traffic to consumer wellness and AI-companion services without knowing whether approved benefits address employee needs. Shadowcue creates a narrow governance view from authorized network metadata, but it never identifies who seeks therapy, infers diagnosis, monitors content or converts personal behavior into benefits procurement. The supplied research confirms two new shadow-AI classifiers, low EAP utilization evidence and no product joining wellness-app exposure to a benefits artifact. One capability was verified. A domain match is not proof that a service was used for mental health, paid for personally or preferred over an EAP. Network logs can be shared, routed through relays or generated by background processes. Findings are aggregated above a minimum cohort and time window, with no employee, device or department drill-down where reidentification is plausible. App privacy notes cite public policies and independent research with dates; they are not universal risk scores or legal conclusions. Detection, classification, privacy review, security action, benefits discovery, procurement approval and employee outcome remain separate. Benefits teams receive only approved aggregate patterns. Success is better governance and informed voluntary-resource discovery—not employee surveillance, diagnosis, utilization claims or procurement conversion.

A joint workplace security, privacy and benefits governance team assessing consumer wellness-app exposure without employee-level monitoring.