Abstract: Systems and methods are provided for risk management in an expert system. A distribution of feature values within a training dataset for the expert system and each of a plurality of baseline datasets are determined, and Kullback-Leibler divergences between the baseline dataset and the training dataset to provide set of Kullback-Leibler divergence values. Measures of central tendency and statistical dispersion of the set of Kullback-Leibler divergence values are determined, and a threshold Kullback-Leibler divergence value is determined from these values and a desired confidence interval for new datasets. A Kullback-Leibler divergence value between the feature values of novel dataset and the training dataset is determined and it is determined that the second dataset represents an unacceptable divergence if this Kullback-Leibler divergence value exceeds the threshold value.