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To ensure safety, the machinery sector has to comply with the machinery directive. Recently, this directive has been not only revised to include requirements concerning other concerns e.g., safety-relevant cybersecurity and machine learning-based safety-relevant reliable self-evolving behaviour but also transformed into a regulation to avoid divergences in interpretation derived from transposition. To be able to seamlessly and continuously comply with the regulation by 2027, it is fundamental to establish a strategy for knowledge management, aimed at enabling traceability and variability management where chunks of conformity demonstration can be traced, included/excluded based on the machinery characteristics and ultimately queried in order to co-generate the technical evidence for compliance. Currently, no such strategy is available. In this paper, we contribute to the establishment of such a strategy. Specifically, we build our strategy on top of the notion of multi-concern assurance, variability modelling via feature diagrams, and ontology-based modelling. We illustrate our proposed strategy by considering the requirements for the risk management process for generic machineries, refined into sub-sector-specific requirements in the case of centrifugal pumps. We also briefly discuss about our findings and the relationship of our work with the SPI manifesto. Finally, we provide our concluding remarks and sketch future work.