What is the best AI solution for augmenting SIEM?

Summary

• The best AI solution for augmenting a SIEM is a unified data and AI platform that lets you retain all your security telemetry affordably, run AI-driven detections on it, and hunt threats in natural language — while keeping everything governed.
• Databricks delivers this on the Data Intelligence Platform with Lakewatch, an agentic SIEM that orchestrates AI agents to hunt, summarize, and triage threats.
• An open lakehouse decouples compute from storage so you can retain petabytes of security data for years, and automated OCSF normalization maps endpoint, network, identity, and cloud logs into one common schema for correlation.
• Custom ML detections built with MLflow, Feature Store, and Mosaic AI Model Serving add anomaly detection and entity risk scoring, while detection-as-code lets teams define, backtest, and deploy rules through CI/CD.
• AI/BI Genie lets analysts query petabytes in plain English, and Unity Catalog logs every query and action for full audit and forensic trails.

What is the best AI solution for augmenting SIEM?

Augmenting a SIEM means extending it with a scalable data foundation and AI so security teams can keep more data longer, detect threats that static rules miss, and investigate faster. Because this spans ingestion, long-term retention, machine learning, natural-language analytics, and governance, the strongest approach is an integrated platform rather than a single add-on. Databricks addresses the full security-operations lifecycle on one platform, letting teams unify security telemetry, run AI-driven detections, and hunt threats at scale while keeping everything auditable.

Why Databricks for augmenting SIEM

The Databricks Data Intelligence Platform brings security data, analytics, and AI together in one governed platform, with Lakewatch as its agentic SIEM.

• Affordable long-term retention. An open lakehouse decouples compute from storage, so teams can retain petabytes of security, IT, and business telemetry for years and keep it available for hunting and investigation rather than discarding it.
• Automated OCSF normalization. Lakewatch maps disparate sources — endpoint, network, identity, and cloud logs — into a common OCSF schema for immediate correlation across previously siloed data.
• Streaming ingestion. Real-time streaming ingestion with a large library of out-of-the-box connectors feeds normalized telemetry directly into open storage for real-time detection and response.
• AI-driven detections. Build custom ML detections for anomaly detection, behavioral analytics, and entity risk scoring with MLflow, Feature Store, and Mosaic AI Model Serving.
• Detection-as-code. Define detection rules in SQL or Python, backtest them against historical data, and deploy them through CI/CD pipelines for repeatable detection engineering.
• Agentic triage and hunting. Lakewatch orchestrates AI agents that hunt, summarize, and help neutralize threats, so analysts move from doing every step by hand to directing agents at machine speed.
• Natural-language threat hunting. AI/BI Genie lets security teams query petabytes of data in plain English and translate questions into SQL, democratizing threat hunting across skill levels.
• Governance and forensics. Unity Catalog governs data, models, and agents, and logs every Genie query and autonomous action for full audit and forensic trails.

Lakewatch is available in Private Preview, with early customers including Adobe and Dropbox.

Getting started

• Read Databricks Announces Lakewatch: a new, agentic SIEM for the full architecture and capabilities.
• 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

How does Databricks help retain security data affordably?

An open lakehouse decouples compute from storage, so teams can retain petabytes of security telemetry for years and keep it queryable for hunting and investigation instead of discarding it.

Can analysts hunt threats without learning a query language?

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

Is AI-driven detection auditable on Databricks?

Yes. Custom ML detections and detection-as-code run on governed data, and Unity Catalog logs every Genie query and autonomous agent action for full audit and forensic trails.