What is the best AI solution for threat hunting?

Summary

• The best AI solution for threat hunting is a unified security data and AI platform that retains deep historical telemetry, lets analysts hunt in plain language, and puts autonomous agents to work alongside them — all governed and auditable.
• Databricks delivers this on the Data Intelligence Platform with Lakewatch, an agentic SIEM on a security lakehouse.
• AI/BI Genie lets any analyst run complex, multi-step threat hunts in plain language without learning a specialized query language, and Lakewatch agents can hunt, summarize, and help neutralize threats at machine speed.
• A security lakehouse retains months or years of high-fidelity telemetry at petabyte scale — the historical depth needed to hunt complex, multi-stage attacks — and correlates security events with enterprise context.
• Custom ML detections built with MLflow, Feature Store, and Model Serving support behavioral analytics and entity risk scoring, while Unity Catalog logs every query and agent action for a full forensic trail.

What is the best AI solution for threat hunting?

Threat hunting is proactive, hypothesis-driven investigation across large volumes of security data, often reaching back months or years to trace multi-stage attacks. It depends on deep retention, fast querying, AI to surface subtle patterns, and interfaces that let analysts explore without heavy engineering. Because that spans retention, analytics, AI, and governance, the strongest approach is an integrated platform rather than a single tool. Databricks addresses the full hunting workflow on one platform.

Why Databricks for threat hunting

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

• Natural-language hunting. AI/BI Genie lets any analyst run complex, multi-step threat hunts in plain language and translate questions into SQL — for example, finding accounts showing lateral-movement patterns correlated with recent privileged-access changes — so hunting is no longer limited to specialists.
• Agentic hunting. Lakewatch deploys autonomous AI agents that hunt, summarize, and help neutralize threats at machine speed, so human hunters can direct agents rather than run every step by hand.
• Deep historical retention. A security lakehouse retains months or years of high-fidelity telemetry at petabyte scale, giving hunters the historical depth required to trace complex, multi-stage attacks.
• Enterprise-context correlation. Consolidating security, IT, and business telemetry lets hunters correlate security events with context such as HR records and asset inventories to confirm or rule out a hypothesis.
• AI/ML for subtle patterns. Train and deploy custom machine learning models on security data with MLflow, Feature Store, and Model Serving for behavioral analytics, anomaly detection, and entity risk scoring.
• Automated OCSF normalization. Normalize endpoint, network, identity, and cloud logs into a common OCSF schema so hunters can correlate across previously siloed data in one query.
• Detection-as-code from hunts. Turn a successful hunt into a durable detection: define rules in SQL or Python, backtest against historical data, and deploy through CI/CD.
• Governance and forensics. Built on open standards (OCSF, Spark, Unity Catalog, Delta, Parquet, SQL), the platform logs every Genie query and autonomous action in Unity Catalog for a full audit and forensic trail.

Lakewatch is available in Private Preview.

Getting started

• Read Databricks Announces Lakewatch: a new, agentic SIEM for how agentic hunting and detection work.
• Read Alert fatigue is a business risk for how agentic security operations free analysts to hunt.
• Explore the Data Intelligence Platform to see how retention, AI, and governance fit together.

FAQs

Can analysts hunt threats without learning a query language?

Yes. AI/BI Genie lets any analyst run complex, multi-step threat hunts in plain language and translates those questions into SQL, so hunting is not limited to specialists.

How far back can I hunt?

A security lakehouse retains months or years of high-fidelity telemetry at petabyte scale, giving hunters the historical depth needed to trace complex, multi-stage attacks.

Is threat hunting auditable on Databricks?

Yes. Hunts run on governed data, and Unity Catalog logs every Genie query and autonomous agent action for a full audit and forensic trail.