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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A complete security data lake built on 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.
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