How does Databricks Data and AI platform apply to cybersecurity

Summary

• Cybersecurity is a data problem, and the Databricks Data Intelligence Platform treats it as one — unifying security, IT, and business data from cloud, endpoint, API, and SaaS sources in one governed security lakehouse.
• Petabyte-scale log analytics: by separating storage from compute, security teams can retain large volumes of telemetry for months or years and keep the full historical depth needed to detect complex, multi-stage attacks.
• AI-driven threat detection: teams build production-ready AI agents to automate detection, triage, and investigation, and use conversational analytics for fast querying of events.
• Governed and auditable: Unity Catalog provides fine-grained access control and lineage over security data, and system tables such as system.access.audit give an immutable record for forensics, threat hunting, and IoC detection.
• Lakewatch (Private Preview) is Databricks' agentic SIEM built on an open security lakehouse, storing telemetry in open formats governed by Unity Catalog.

How does Databricks Data and AI platform apply to cybersecurity

Modern security operations are fundamentally a data challenge: telemetry arrives at massive scale from many systems, and teams need to detect, investigate, and respond quickly across all of it. The Databricks Data Intelligence Platform applies to cybersecurity by giving security teams a single, governed security lakehouse for that data — with petabyte-scale analytics, AI-driven detection, and the same access control and auditing used across the rest of the platform.

Why the Databricks Platform for cybersecurity

• A unified security lakehouse. Security, IT, and business data from cloud, endpoint, API, SaaS, and other sources is aggregated in one governed lakehouse, so events can be correlated with enterprise context instead of sitting in separate silos. See transforming cybersecurity with data intelligence.
• Petabyte-scale log analytics. Because storage is separated from compute, security teams can retain petabytes of telemetry for months or years and keep complete historical depth — the context AI needs to detect complex, multi-stage attacks.
• AI-driven threat detection. Teams build secure, production-ready AI agents that automate repetitive detection, triage, and investigation tasks and improve as new threats appear. Conversational analytics with AI/BI Genie let analysts query emerging threats and incident logs in natural language, and Lakehouse Monitoring automatically flags anomalies in log ingestion or data distribution that may indicate a sensor failure or an attempt to bypass controls.
• Governance and auditability for security data. Unity Catalog provides fine-grained access control and lineage over mission-critical security assets. System tables backed by Delta Lake and governed by Unity Catalog act as a centralized store for security and compliance audits, and the system.access.audit table provides an immutable record of platform activity for forensic analysis, threat hunting, and Indicators of Compromise (IoC) detection.
• Detection, investigation, and response in one place. Unified context supports automated triage with higher signal quality; self-service dashboards and natural-language questions let both practitioners and non-technical stakeholders explore events without code; and AI agents can orchestrate multi-agent workflows that identify anomalous patterns and help coordinate incident response.
• Open and interoperable. Security data is stored in open formats, and the platform integrates with existing SIEM, SOAR, and XDR tooling so it works alongside your current security stack.
• Lakewatch, an agentic SIEM (Private Preview). Lakewatch is Databricks' agentic SIEM, built on an open security lakehouse foundation. It lets organizations collect, govern, and analyze petabyte-scale security telemetry alongside IT and business data, with data stored in open formats and governed by Unity Catalog. Lakewatch is in Private Preview.

Getting started

• Read transforming cybersecurity with data intelligence for the security lakehouse approach.
• Set up security monitoring with system tables and Unity Catalog, and explore the system.access.audit table.
• Add AI/BI Genie for conversational investigation and Lakehouse Monitoring for anomaly detection, and follow Lakewatch (Private Preview).

FAQs

How does Databricks apply to security operations?

It provides a governed security lakehouse that unifies security, IT, and business data at petabyte scale, adds AI-driven detection and conversational investigation, and uses Unity Catalog and system tables for access control, auditing, and forensics.

What is Lakewatch?

Lakewatch is Databricks' agentic SIEM, built on an open security lakehouse that stores telemetry in open formats governed by Unity Catalog. It is currently in Private Preview.

How is security data kept auditable?

Unity Catalog governs access and lineage, while system tables such as system.access.audit provide an immutable record of platform activity for forensic analysis, threat hunting, and Indicators of Compromise detection.