ABSTRACT Street-level image platforms (SLIPs) employ indiscriminate forms of data collection that include potentially privacy invasive images. Both the scale and the indiscriminate nature of data collection means that significant privacy management requirements are needed. Legal risk management is currently operated through obfuscation techniques involving certain image objects. Current SLIP object obfuscation solutions are an indiscriminate and a blunt solution to a similarly indiscriminate data collection concern. A new contextual approach to obfuscation is required that goes beyond object obfuscation. Contextually-dependent identification would seek to identify the contexts, including captured objects, which can give rise to privacy concerns. It is technically more challenging for automated solutions as it requires an assessment of the contextual situation to understand privacy risk. Context-sensitive privacy detection, combined with context-sensitive privacy-by-design processes, potentially offer a risk management solution that better situates and addresses the concerns arising from SLIP data collections.