Research on self-adaptive systems (SASs) has proliferated in the last fifteen years. Approaches resting on models at run-time have been proposed (e.g., to model system variants), as well as methods that aim at giving requirements a key role in driving the adaptation process (e.g., to choose the most appropriate system variant). More recent research focuses on automating model-based decisions, such as requirements revision, by exploiting data generated at execution time.Uncertainty is considered a first-class citizen in SAS engineering. A well recognised technique for dealing with uncertainty is risk management. Several risk management methods exist, as well as visual modelling languages that aim at supporting risk analysis.Our objective is to investigate how complementing requirements modelling with risk modelling could support automating risk-driven requirements analysis. While risk could be identified and modelled at design-time using domain knowledge and data generated by previous system executions, their estimation will be done at run-time, and guide the selection of system behaviour that minimises the risk of the system not being compliant with requirements.In this paper, we introduce our research objective that concerns the definition of an engineering framework, called Risk4SAS, that enables risk-driven requirements analysis in SASs life-cycle and describe first steps towards its realisation, including a meta-model, which captures the dependency between risk and the characteristics of a SAS's variants. We conclude by presenting our research road-map.