Context: Assessment of the dust environment of a comet is needed for data analysis and planning of spacecraft missions, such as ESA’s Comet Interceptor (CI) mission. The distinctive feature of CI is that the target object will be defined shortly before (or even after) launch therefore the properties of the nucleus and dust environment are poorly constrained and therefore make the assessment of the dust environment challenging. Aims: The main goal of the work is to provide realistic estimations of a dust environment based on very general parameters of possible target objects. Methods: Contemporary numerical models of dusty-gas coma are used to obtain spatial dis- tribution of dust for a given set of parameters. By varying parameters within a range of possible values we obtain an ensemble of possible dust distributions. Then, this ensemble is statistically evaluated in order to define the most probable cases and hence reduce the dispersion. This en- semble can be used to estimate not only the likely dust abundance along e.g. a fly-by trajectory of a spacecraft but also quantify the associated uncertainty. Results: We present a methodology of the dust environment assessment for the case when the tar- get comet is not known beforehand (or when its parameters are known with large uncertainty). We provide an assessment of dust environment for the CI mission. We find that the lack of knowledge of any particular comet results in very large uncertainties (∼ 3 orders of magnitude) for the dust densities within the coma. The most sensitive parameters affecting the dust densities are the dust size distribution, the dust production rate and coma brightness, often quantified by Afρ. Further, the conversion of a coma’s brightness (Afρ) to a dust production rate is poorly constrained. The dust production rate can only be estimated down to an uncertainty of ∼ 0.5 orders of magnitude if the dust size distribution is known in addition to the Afρ. All results are publicly available under https://www.doi.org/10.5281/zenodo.6906815. Conclusions: To accurately predict the dust environment of a poorly known comet, a statistical approach needs to be taken to properly reflect the uncertainties. This can be done by calculating an ensemble of comae covering all possible combinations within parameter space as shown in this work.