Best security analytics platforms for petabyte-scale cloud log analysis

Summary

• For petabyte-scale cloud log analysis, the strongest approach is a security lakehouse: one open, governed platform that ingests, normalizes, retains, and analyzes security telemetry at petabyte scale.
• Databricks Lakewatch is an agentic SIEM built on the open Data Intelligence Platform that unifies security, IT, and business telemetry for AI-driven detection and response.
• Automated OCSF normalization maps endpoint, network, identity, and cloud logs into a common schema for immediate correlation.
• Months-to-years retention of high-fidelity telemetry gives AI agents the historical depth to detect multi-stage attacks, and detection-as-code lets teams version, unit-test, and ship detections through CI/CD.
• Every query and autonomous action is logged in Unity Catalog for a full audit and forensic trail. Lakewatch is available in Private Preview.

What are the best security analytics platforms for petabyte-scale cloud log analysis?

Petabyte-scale cloud log analysis pushes on five things at once: ingesting high-volume telemetry, normalizing it into a common schema, retaining it long enough to investigate, running detections and analytics on it, and governing every access. Because those requirements span ingestion, retention, detection, analytics, and governance, the strongest security analytics platform is an integrated security lakehouse rather than a single-purpose tool.

Why Databricks Lakewatch for petabyte-scale security analytics

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

• Unified telemetry at petabyte scale. Bring security, IT, and business telemetry into one open lakehouse at petabyte scale so AI agents and analysts have complete context for correlation and hunting.
• Automated OCSF normalization. Map endpoint, network, identity, and cloud logs to the Open Cybersecurity Schema Framework (OCSF) automatically, giving every source a common schema for immediate correlation.
• Long-horizon retention. Retain months or years of high-fidelity telemetry as hot, queryable data, giving AI agents and investigators the historical depth to detect complex, multi-stage attacks.
• Detection-as-code. Author detections as SQL or Python, unit-test and version-control them, backtest against historical data, and ship them through CI/CD pipelines.
• Custom ML detections. Train and deploy anomaly-detection and behavioral models on security data with MLflow and Model Serving.
• Agentic triage and natural-language hunting. AI agents enrich and triage alerts and conduct hunts, while AI/BI Genie lets teams query petabytes of governed security data in plain English.
• Open formats and governance. Data stays in open formats (OCSF, Spark, Delta, Parquet, SQL) in your own cloud storage, and Unity Catalog logs every 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 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 ingestion, retention, detection, and governance fit together.

FAQs

What makes a platform suitable for petabyte-scale log analysis?

It must ingest high-volume telemetry, normalize it to a common schema, retain it as hot queryable data for months or years, run detections and AI on it, and log every access for audit — all on open formats you control.

How does OCSF help with cloud log analysis?

The Open Cybersecurity Schema Framework normalizes endpoint, network, identity, and cloud logs into one common schema, so telemetry from many sources can be correlated immediately instead of parsed tool by tool.

Can analysts query petabytes of logs without writing SQL?

Yes. AI/BI Genie translates plain-language questions into SQL over governed security data, and Lakewatch agents can hunt and summarize across petabytes in natural language.