RiKhaNET addresses a core operational challenge in Bhutan’s tiger landscapes where field data and incident information are often fragmented across tools and stakeholders, limiting timely prevention. RiKhaNET consolidates these inputs into a single, governed platform by integrating Bhutan’s SMART (Spatial Monitoring and Reporting Tool) data with AI-driven analytics and real-time sensor inputs. Using machine-learning models, the system generates dynamic risk heatmaps, geofencing alerts, and automated notifications to help rangers and communities anticipate conflict hotspots before incidents occur, shifting response from reactive to proactive prevention. The Stakeholder meetings conducted emphasized that the pilot must be designed to strengthen trust and adoption, not only technology performance. Three design requirements were highlighted: Data governance and transparency: The current hosting arrangements raise concerns about filtering, access control, and sensitive wildlife-location data; governance must be explicit and communicated clearly. Community safety communication: The alert systems must be carefully designed so alerts support prevention and coordinated action rather than increasing fear; community focal persons can strengthen trusted communication. Operational realism for detection: It has been noted that tiger detections can be rare; deployments may run for months yet capture limited tiger events, so the pilot should prioritize focused hotspot monitoring with sufficient duration to produce meaningful training and pattern data.