Advances in genome editing tools make it possible to rapidly develop new cell and gene therapies. Artificial genes and modifications to existing ones can readily be made in a site-specific manner. However, the use of gene editing represents a new paradigm for treating disease. Tools are needed to ensure this new class of medicines are safe and efficacious for patients. Genome editing carries risk. These relate to malignant transformation of the target cells by a variety of mechanisms, including the inherent function of the gene editing platforms themselves. Each has the potential to cause genomic instability in the form of breaks at unintended sites in the genome. Off-target editing that activates a proto-oncogene or disrupts a tumor suppressor has the potential to be carcinogenic. Off-target mutagenesis may also contribute to a range of cellular dysfunction. A variety of genomic tools have been developed to screen for off-target editing. However, each of these methods has acknowledged limitations and none has emerged as a definitive standard. More robust, reliable, reproducible and quantitative methods are needed for testing on and off-target editing during pre-clinical, clinical and post-treatment follow-up. At present an unbiased, standardised and scalable method that can be used across the industry is lacking. Here, we describe the development and characterization of INDUCE-seq to address this. We describe INDUCE-seq: a cell-based, unbiased, PCR-free platform for the precision measurement of genomic breaks induced by gene editing. A HESI-led public study conducted CRISPR-Cas9-based gene editing of five well-studied genetic targets. Two industry partners, used two cells types and both on and off-target editing was measured using INDUCE-seq. The mutational (editing) outcomes was measured by Duplex Sequencing (DS) with a sensitivity of 0.01%. We report the reproducibilty of the INDUCE-seq in detecting on and off-target editing within and between experimental replicates by both industry partners. We calculate the sensitivity, specificity, precison and accuracy of the assay. Finally, we report mutation frequencies at these on and off-target sites revealing the performance of INDUCE-seq by showing the correlation between editing-induced breaks and mutation frequency. We conclude that INDUCE-seq is a reliable and reproducible assay to assess the on and off-target effects of gene editing.