Traditional SIEM architectures are under increasing strain as modern environments generate massive volumes of security telemetry from cloud, SaaS, containerized workloads, and identity systems. As data volumes grow, organizations face rising SIEM costs and operational complexity, often forcing SOC teams to choose between controlling expenses and maintaining detection visibility. This challenge is driving many organizations to adopt modular security data pipelines and data lakes that offer greater scalability, flexibility, and cost control.
However, improving data infrastructure alone does not guarantee better security outcomes. This research brief argues that organizations must integrate detection engineering directly into the security data lifecycle—treating it as a control plane for telemetry quality, detection coverage, and rule health. By embedding detection-as-code, mapping detections to adversary techniques, and continuously validating rule performance, security teams can transform modern data architectures into detection-first platforms that optimize both cost and threat coverage.