Data security is a critical component of data-driven decision-making in smart cities, where integration involves diverse sources such as IoT, sensors, and cloud platforms. However, many existing frameworks lack a comprehensive model that integrates security, regulation, and governance. This study addresses that gap by proposing a conceptual model DSDMC (Data Security & Decision Making for Cities) structured into four core domains: data integration, technology security, regulation & privacy, and governance. The model was developed using a Systematic Literature Review (SLR) approach. From an initial corpus of 28,926 documents, 278 were selected through screening, and 58 studies were analysed in depth. These informed the design of DSDMC, which incorporates technologies such as edge computing, federated learning, encryption, and multi-party authentication. The model offers a strategic foundation for building secure, adaptive, and trustworthy smart city systems.