The monitoring of data quality is crucial both online, during the data taking, to promptly spot issues and act on them, and offline, to provide analysts with datasets that are cleaned against the occasional failures that may have crept in. Typically, data quality monitoring (DQM) is performed by shifters who look at a set of integrated quantities, compare them with reference histograms, and, based on their experience and training, assign quality flags. Recently CMS has developed the possibility of producing DQM plots per-lumisection, where a lumisection is a time unit corresponding to about 23 𝑠 of data taking. To analyze per-lumisection data, a manual approach would be prohibitive due to the high number of lumisections, therefore an automated one would be preferable. In this work, the first use in CMS of AutoEncoders to perform anomaly detection on per-lumisection data, specifically for quantities associated with jets and missing transverse energy, is presented. The technique developed allows the detection of anomalies at the level of individual lumisections, which might be overlooked when examining integrated quantities, and serves as a proof of concept regarding the efficacy of this and similar approaches.