A context graph is a structured model that links related data points, such as employees, skills, jobs, tasks, and business systems, so software can reason with richer context than a flat database provides. In enterprise AI, it helps ground answers and recommendations in trusted relationships instead of relying only on a language model’s general knowledge.
In cyber security, context graphs matter because they often collect sensitive identity, HR, and operational data in one place. That makes access control, data lineage, and least privilege critical: if the graph is overexposed, attackers may learn who does what, which systems are connected, and where valuable workflows live. Defenders also need to watch for data poisoning or stale records, because bad relationships can mislead AI-driven decisions. Used well, a context graph improves detection, automation, and auditability; used poorly, it becomes a high-value target and a source of dangerous business intelligence leakage.