This paper presents a practical approach to deploying a priori speaker dependent thresholds (SDT) for adaptive speaker verification applications. Our motivations for exploring SDTs are twofold: one is to eliminate the externally pre-set overall system thresholds and replace them with automatically-set internal thresholds calculated at runtime; the second is to counter the verification score shifts resulting from online adaptation. The second motivation is based on the observation that after adaptation, verification scores for both true speakers and impostors increase, which in turn increases the false accept (FA) rates. The rise of FA rates, in an adaptive system, can be costly because of the possibility of model corruption. In this work, an approach similar to ZNORM  is used to calculate a threshold for each speaker, which is automatically updated every time the claimant model is adapted. The paper explores various computational efficiency strategies to make the deployment of this approach practical for a fielded system. Results of experiments on one Japanese and one English digits database are presented.