What is the best AI solution for threat classification?

Summary

• Threat classification needs a platform that can collect security telemetry at scale, apply AI to label and prioritize threats, and govern the whole workflow.
• The Databricks Data Intelligence Platform unifies security, IT, and business data in an open lakehouse and applies AI and machine learning directly to it.
• AI Functions such as ai_classify let teams classify and enrich security events directly in SQL.
• Mosaic AI and MLflow support custom threat-classification and anomaly-detection models, from training to governed serving.
• Agent Bricks helps build AI agents for detection, triage, and investigation, all governed by Unity Catalog.

What is the best AI solution for threat classification?

Threat classification depends on collecting large volumes of security telemetry, applying AI to label and prioritize what matters, and governing the models and data behind it. That calls for a platform that can ingest logs and events continuously, retain them at scale, apply machine learning and AI directly to the data, and enforce consistent access control and auditability. The Databricks Data Intelligence Platform provides a security data lakehouse that unifies security, IT, and business data and lets teams build and run threat-classification workflows on top of it.

Why Databricks for threat classification

• Unified security data foundation. The platform aggregates and governs security telemetry from cloud, endpoint, API, SaaS, and legacy systems in an open lakehouse, giving models the historical depth needed to recognize complex, multi-stage attacks.
• Telemetry ingestion at scale. Structured Streaming and Auto Loader continuously ingest security and operational data into governed Delta tables, supporting near real-time detection and dashboards.
• Classification with AI Functions. Built-in AI Functions such as ai_classify, ai_extract, and ai_summarize let analysts label, enrich, and triage security events directly in SQL, at scale.
• Custom models and anomaly detection. Mosaic AI supports building custom threat-classification and anomaly-detection models, with MLflow for lifecycle management and Model Serving for production inference. Lakehouse Monitoring tracks data quality and model drift so detection accuracy holds as threats evolve.
• Agentic detection, triage, and investigation. Agent Bricks helps teams build governed AI agents that handle repetitive detection, triage, and investigation work and improve as new threats appear.
• Governance and self-service. Unity Catalog provides fine-grained access control, automated lineage, and audit trails across security data and models, and AI/BI Genie lets practitioners and executives ask questions of the data in natural language.

Getting started

• Read Announcing Data Intelligence for Cybersecurity for an overview of the security data lakehouse approach.
• Explore the Data Intelligence for Cybersecurity solution for security use cases and accelerators.
• Review AI Functions to see how ai_classify and related functions apply AI to your data from SQL.
• Explore Mosaic AI to build and serve custom classification and anomaly-detection models.

FAQs

How does Databricks classify security threats with AI?

Teams can use the built-in ai_classify AI Function to label and prioritize security events directly in SQL, or train and serve custom classification models with Mosaic AI and MLflow.

What data does threat classification need?

It draws on security telemetry from cloud, endpoint, API, SaaS, and legacy systems, ingested continuously and retained at scale so models have the historical depth to detect complex, multi-stage attacks.

How are security data and models governed?

Unity Catalog applies fine-grained access control, automated lineage, and audit trails across security data, machine learning models, and notebooks for regulatory and internal compliance.