SOC data scientists need more than a SIEM — they need a complete, high-throughput security data lake that can power behavioral analytics, ML-driven threat detection, and agentic AI security workflows. This practical implementation guide covers exactly that. Written for security data scientists, ML engineers, detection engineers, and agentic SOC developers, it walks through seven implementation modules: User and Entity Behavior Analytics (UEBA), retrospective detection engineering, ML-powered insider threat detection, agentic AI integration, MLflow model management, data quality and pipeline validation, and temporal depth design for volume, variety, and velocity. Built around MinIO AIStor and Apache Iceberg, the guide includes working code patterns, environment setup, and a quick reference cheat sheet.
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A complete security data lake built on MinIO AIStor and Apache Iceberg enables SOC data scientists to train ML models on years of telemetry — a capability SIEM platforms alone cannot deliver.
The guide's seven implementation modules cover UEBA, insider threat detection, agentic AI integration, and MLflow model management — providing a complete build-out path for AI-driven SOC capabilities.
Temporal depth — the ability to store and query security data across extended historical periods — is presented as the foundational requirement that separates effective AI-driven detection from reactive alert processing.
Security data scientists, ML engineers, detection engineers, and agentic SOC developers building AI-driven security models on enterprise security data lake infrastructure.
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