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Water contamination is a growing concern in society. New environmental laws are being enacted to define intolerable human activities, and their enforcement is increasingly supported by forensic science. However, water contamination is a broader security issue that is not only caused by illegal human behavior. Risk‐based approaches are needed to prevent (re)occurrence of incidents and minimize their negative consequences. This can be achieved through the formalization of a monitoring process producing intelligence (i.e., actionable knowledge), crucial to detect recurring incidents, and guiding decision‐makers in their choice of preventive and responsive actions. In this perspective, forensic science has a key role to play in integrating vestiges from water‐contaminating activities (i.e., traces) in such a problem‐solving process. Information conveyed by traces allows detecting similarities among contamination events (i.e., patterns), inferring common causes, and better understanding of mechanisms and consequences of water contamination. The different stages of the process will be described and illustrated through a real case example. Current barriers to the implementation of such a process are then discussed, showing how systemic issues and complexity may prevent the establishment of links across contamination events, thus negatively impacting the generation of intelligence. To overcome these obstacles, we underline the importance to initiate local and size‐limited approaches by implementing relatively simple and flexible systems. New knowledge can be used to improve local situations and help stakeholders to understand the benefits of such a process; then, by a bottom‐up iterative learning process, the approach can be given a greater ambition at a larger scale. This article is categorized under:Forensic Science in Action/Crime Scene Investigation > Special Situations and InvestigationsCrime Scene Investigation > From Traces to Intelligence and EvidenceForensic Chemistry and Trace Evidence > Forensic Food and Environment Analysis [ABSTRACT FROM AUTHOR]