Railway maintenance is faced with increasing demands, including the need to improve service.Data measuring the track state and suitable models or applications are needed to make good maintenancedecisions. This critical review paper investigates many research papers on the use of information assurance (IA)within condition-based maintenance (CBM) on a railway track. An IA framework sheds light on the data andinformation used to make maintenance decisions. The paper considers work on data processing and decisionmakingin CBM. The results show condition monitoring suffers from an inability to determine exact positioningon the track; some data are inaccurate or unavailable. Existing studies have not adequately dealt with data contentor the various technologies used. They focus on integrity, availability, authentication, authorisation and accuracy,but do not consider other IA principles important to understand data.CBMmodels and algorithms have difficultyunderstanding degradation models, and data problems mean it is difficult to make good decisions. There is alack of long term maintenance plans. Models also need to be integrated for more realistic but not necessarilyoptimum solutions and to ensure practical predictions of maintenance. Some models focus on degradation, othersconsider prediction, and still others calculate the maintenance cost; it is difficult to combine these. Overall, dataare inaccurate, there is no testing phase using realistic data, and existing models are insufficient. This has anegative impact on maintenance decisions.