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Let us consider a cooperative group of decision-makers striving to strategise against failing to attain a goal. The risk of failure depends on external factors and the decision-makers' assessment and acceptance of risk. Furthermore, the setting is complex, e.g. owing to multiple objectives and criteria, making it difficult for the individual decision-maker, as well as the group, to assess the risk in a coherent way. To aid humans in this situation formal risk frameworks have been developed. One such framework is UTADIS (UTilities Additives DIScriminantes) and the contribution of this paper is to complement this framework with objective measures of volatility. This is done in such a way that the original features of UTADIS are preserved. This paper demonstrates how objective time-series data can be exploited in groups' risk mitigating for a given decision problem. To illustrate how this solution is implemented we consider the problem of United Nations policy-makers in monitoring food security as a UN sustainable development goal. The aim of the policy makers is to classify countries into crisis groups, giving due importance to the price volatility of food staples. UTADIS is hereby applied for the purpose of developing a food price volatility classification model to satisfy the preferences of the decision-makers. The enhanced UTADIS method provides both the global risk scores of the countries and the utility threshold. This is achieved in an efficient manner, meaning that a minimised misclassification between the decision-makers' preferred classification and their authentic classification is obtained in just one iteration. The resulting marginal utility function is consequently accurate enough and validated to satisfy the preferences of the United Nations decision-makers in the making of classification on future datasets.