Proximity remote sensing techniques, both land- and drone-based, allow for a significant improvement of the quality and quantity of raw data employed in the analysis of rockfall phenomena. In particular, the large amount of data these techniques can provide allows for the use of probabilistic approaches to rock mass characterization, with particular reference to block volume and shape definition. These, in return, are key parameters required for a proper rockfall hazard assessment and the optimization of countermeasures design. This study aims at providing a sort of guide, starting from the data gathering phase to the processing, up to the implementation of the outputs in a probabilistic-based scenario, which is able to associate a probability of not being exceeded with total kinetic energy values. By doing so, we were able to introduce a new approach for the choice of design parameters and the evaluation of the effectiveness of mitigation techniques. For this purpose, a suitable case study located in Varaita Valley (Cuneo, Italy) has been selected. The area has been surveyed, and a model of the slope and a digital model of the rock faces have been defined. The results show that a 6.5 m3 block has a probability of not being exceeded of 75%; subsequent simulations show that the level of kinetic energy involved in such a rockfall is extremely high. Some mitigation techniques are discussed.