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Reliable strategies to capture patients at risk of progression from precursor stages of multiple myeloma (MM) to overt disease are still missing. We assembled a comprehensive collection of MM genomic data comprising 1,030 patients (218 with precursor conditions) that we used to identify recurrent coding and non-coding candidate drivers as well as significant hotspots of structural variation. We used those drivers to define and validate a simple 'MM-like' score, which we could use to place patients' tumors on a gradual axis of progression toward active disease. Our MM precursor genomic map provides insights into the time of initiation and cell-of-origin of the disease, order of acquisition of genomic alterations and mutational processes found across the stages of transformation. Taken together, we highlight here the potential of genome sequencing to better inform risk assessment and monitoring of MM precursor conditions.