Introduction. Existing regional monitoring systems of the narcological situation are predominantly focused on recording clinical outcomes and demonstrate limited sensitivity to phases in which the system is already losing governability while clinical indicators still remain within acceptable ranges. As a result, pre-crisis states are detected with delay, and managerial response becomes lagged. Purpose. To present the AURA diagnostic dashboard as an operational tool of the Adaptive Unified Regional Antidrug Architecture model and to demonstrate its potential for early identification of regime shifts in a regional antidrug system. Materials and methods. Data from official statistical reporting forms No. 11 and No. 37 of the Ministry of Health of Russia for the Kabardino-Balkarian Republic for 2014–2024 were used. The Monitoring Divergence Index (IDM = Tₜ/Pₜ) was developed, the normalized medical contour (Md*) was calculated, and the recurrent adaptive index Aₜ was estimated. The baseline regime classification was based on the combination of IDM and Md*, while the trajectory of Aₜ was used as an interpretative layer for retrospective system analysis. Results. IDM was found to be statistically significantly correlated with population mortality over a 1-year lag (rₛ = 0.64; p = 0.046). Retrospective analysis revealed that in 2021, the system entered a pre-crisis trajectory, interpreted as an S1→S2 transition: at IDM = 0.78, the medical contour still maintained the marginal permissible value of Md* = 0.55, whereas in 2022–2024, clinical manifestations of ill-being occurred (Md* up to 0.273; IDM up to 8.07). Discussion. A combined analysis of IDM and Md* allowed us to retrospectively identify a critical state in 2022 that a single-parameter IDM approach would not have interpreted as a crisis, indicating the additional diagnostic value of a two-parameter regime assessment. Conclusion. The AURA dashboard transforms the theoretical model of adaptive management into a reproducible computational format suitable for managerial diagnostics. Its scientific value lies not only in data visualization but also in the formalization of rules enabling the detection of pre-crisis states 1–2 years before clinical manifestation. At the same time, the tool should be considered as a stage of internal empirical validation, requiring further interregional calibration of thresholds and expansion of the set of input managerial parameters.