I will present a dynamic risk analysis, including artificial intelligence, designed to evaluate the probability and the impact of fake news, as well as “fake-fake news” defined as realities denied as fake news. Key factors of the analysis are the source of the fake news and the targeted population. The objective is to identify countermeasures and to assess their risk reduction benefits. The model involves a probabilistic analysis of the occurrence of some fake news attacks, and their effects on different targeted groups, based on two statistical surveys focused on political statements. The results emphasize the needs for the government and other organizations to develop further their risk management defenses by quick fact checking, identification of the key agents that spread the fake news and broadcasting the correct message in a convincing way.