SIEM-centric data models cannot scale to meet the storage, ingestion, and query demands of modern security operations — and the security data lake has emerged as the architecture that can. This reference guide is written for SOC architects, security engineers, detection engineers, and platform engineers who need to build a high-performance security data lake that supports AI-driven threat detection, behavioral analytics, and long-term log retention. It covers the full reference architecture for the security data lake stack, integration patterns for connecting MinIO AIStor to the existing SIEM ecosystem, security controls and compliance configuration, log pipeline design, Apache Iceberg integration for advanced security analytics, and operational monitoring. A practitioner-level document designed to be implemented, not just read.
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SIEM-centric data models fail at scale because they were designed for correlation, not storage — creating ingestion bottlenecks and query performance limits that block AI-driven detection and retrospective analytics.
This reference architecture defines the full security data lake stack on MinIO AIStor, including Iceberg integration for advanced analytics, log pipeline design, and SIEM ecosystem connectivity.
SOC teams that implement this architecture gain a scalable, cost-efficient security data layer capable of storing years of telemetry for ML model training, retrospective detection, and compliance retention.
SOC architects, security engineers, detection engineers, and platform engineers responsible for designing and implementing high-performance security data infrastructure.
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