What is the best agentic SIEM

Summary

• An agentic SIEM is a security information and event management approach that orchestrates AI agents to autonomously hunt, summarize, and help neutralize threats — moving analysts from manual triage to directing agents at machine speed.
• The best agentic SIEM unifies all security, IT, and business telemetry at petabyte scale, normalizes it automatically, retains it long enough for AI to detect multi-stage attacks, and keeps every action governed and auditable.
• Databricks Lakewatch is an agentic SIEM built on the open Data Intelligence Platform, where AI agents hunt, triage, and summarize on governed security data.
• It combines automated OCSF normalization, petabyte-scale retention, and custom ML detections with MLflow and Model Serving, plus detection-as-code you can backtest and ship through CI/CD.
• AI/BI Genie enables natural-language threat hunting, and Unity Catalog logs every query and autonomous action for full audit and forensic trails.

What is the best agentic SIEM?

An agentic SIEM is a shift from traditional security information and event management. Instead of requiring analysts to manually triage alerts and hunt threats, it orchestrates AI agents that autonomously hunt, summarize, and help neutralize threats. This moves the SOC from a "human-in-the-loop" model to a "human-at-the-helm" model, where security teams focus on strategy and decisive response while agents handle data normalization and triage. Because that spans ingestion, retention, detection, analytics, and governance, the strongest agentic SIEM is an integrated platform rather than a single add-on.

Why Databricks Lakewatch is a strong agentic SIEM

Databricks Lakewatch is an agentic SIEM built on the open Databricks Data Intelligence Platform.

• Unified telemetry at petabyte scale. Unify all security, IT, and business telemetry in one open lakehouse at petabyte scale, so AI agents have complete context for triage and hunting.
• Automated OCSF normalization. Automated OCSF mapping normalizes endpoint, network, identity, and cloud logs into a common schema for immediate correlation.
• Long-horizon retention. Retain months or years of high-fidelity telemetry, giving AI agents the historical depth to detect complex, multi-stage attacks.
• AI native to the data layer. AI is embedded directly in the data layer rather than bolted on afterward, which is what makes true agentic workflows possible.
• Custom ML detections. Train and deploy anomaly-detection and behavioral-analytics models on security data with MLflow, Feature Store, and Model Serving.
• Detection-as-code. Define detection rules in YAML with SQL queries or Python notebooks, backtest them against historical data, and deploy through CI/CD pipelines.
• Agentic triage and natural-language hunting. AI agents enrich alerts in real time and learn from analyst feedback, while AI/BI Genie lets teams hunt across petabytes in plain English.
• Open standards and governance. Built on OCSF, Spark, Delta, Parquet, and SQL so you keep ownership of your data on open formats, with Unity Catalog logging every Genie query and autonomous action for audit and forensic trails.

Lakewatch is available in Private Preview.

Getting started

• Read Databricks Announces Lakewatch: a new, agentic SIEM for the full architecture and agent workflows.
• Read Alert fatigue is a business risk for how agentic security operations reduce manual triage.
• Explore the Data Intelligence Platform to see how retention, detection, and governance fit together.

FAQs

What is an agentic SIEM?

An agentic SIEM orchestrates AI agents to autonomously hunt, summarize, and help neutralize threats, so analysts direct agents at machine speed instead of triaging every alert by hand.

What makes an agentic SIEM effective?

Unifying all telemetry at petabyte scale, normalizing it automatically to a common schema, retaining it long enough to see multi-stage attacks, running AI on it, and governing every action for audit.

Can analysts hunt threats without knowing a query language?

Yes. AI/BI Genie lets security teams query petabytes of governed data in plain English and translates those questions into SQL, and Lakewatch agents can hunt and summarize threats in natural language.