A computer system, non-transitory computer storage medium, and a computer-implemented method of assessing technical risk using visual pattern recognition in an Information Technology (IT) Service Management System. A data visualization engine and a time series generation engine receive the operational data, respectively. A first representation of the data is generated by the data visualization engine, and a second representation of the data is generated by the time series generation engine. Anomaly patterns are identified by a pattern recognition engine configured to perform feature extraction and data transformation. An ensembler is configured to accept the outputs from two AI anomaly engines and make a final decision of whether anomaly patterns are captured. Risk scores based on the identified anomaly patterns are output by a pattern recognition engine to an automated management system. The anomalies includes information regarding vulnerabilities of devices or components of the IT Service Management System.