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While the literature on channel state information (CSI)-based authentication and key distillation is vast, the two topics have customarily been studied separately. This paper proposes unsupervised learning techniques to disentangle deterministic from stochastic fading to decompose observed CSI vectors into “predictable” and “unpredictable” components. The former, primarily due to large-scale fading, can be used for node authentication. The latter, primarily due to small-scale fading, can be used for secret key generation (SKG). The parameterization of the decomposition is performed using the following metrics: 1) CSI fingerprint “separability” criterion, expressed through the maximisation of the total variation distance (TVD) between the empirical CSI fingerprints; 2) statistical independence metric for CSI collected at different users in neighboring locations, using the d-dimensional Hilbert Schmidt independence …