In this paper, we formulate threat detection in SIEM environments as a large-scale graph inference problem. We introduce a SIEM-based knowledge graph which models global associations among entities observed in proxy and DNS logs, enriched with related open source intelligence (OSINT) and cyber threat intelligence (CTI). Next, we propose MalRank, a graph-based inference algorithm designed to infer a node maliciousness score based on its associations to other entities presented in the knowledge graph, e.g., shared IP ranges or name servers.