What is the best AI solution for threat intelligence integration?

Summary

• The best AI solution for threat intelligence integration ingests external feeds (indicators of compromise and adversary tactics) alongside all your internal telemetry, normalizes both to a common schema, and correlates them at scale so a match surfaces as a real detection.
• Integration is only useful with enough context: a security lakehouse keeps months or years of endpoint, network, identity, and cloud logs, plus business context like asset inventories, so an indicator can be matched against full history rather than a short retention window.
• On Databricks, Lakewatch (an agentic SIEM on the security lakehouse) automates OCSF normalization, and AI agents with AI/BI Genie use the latest threat intelligence to author and tune detections and hunt across petabytes in plain language.
• Custom ML detections built with MLflow, Feature Store, and Model Serving add anomaly detection and entity risk scoring on top of feed-based matching, and detection-as-code lets teams backtest a new rule against historical data before shipping it.
• Unity Catalog logs every query and autonomous action for a full audit and forensic trail, so intel-driven detections stay explainable and governed.

What is the best AI solution for threat intelligence integration?

Threat intelligence is only as useful as what you can correlate it against. The best AI solution ingests external intel (indicators of compromise, plus the tactics and techniques attackers use) together with your internal telemetry, normalizes both to a shared schema, and matches them across enough history to catch a slow, multi-stage intrusion. That is a data-and-AI problem at petabyte scale, so the strongest approach is an open security lakehouse with AI built into the data layer, not a feed connector attached to a fixed-retention SIEM. Databricks delivers this on the Data Intelligence Platform, with Lakewatch as its agentic SIEM.

What is threat intelligence integration?

Threat intelligence integration brings external threat feeds into your detection and hunting workflows and correlates them with what is actually happening in your environment. Feeds carry indicators such as malicious IPs, domains, and file hashes, along with the tactics, techniques, and procedures behind an attack. The value is in the correlation: matching an indicator against endpoint, network, identity, and cloud logs, then enriching the match with business context (which asset, which user, how critical) so responders can act. Weak integration stops at a list of indicators. Strong integration turns feeds into governed, high-context detections.

Key capabilities to evaluate

Capability	Why it matters
Feed ingestion and normalization	Pull IOCs and TTPs from many sources and map them, with your own logs, to a common schema such as OCSF.
Long-horizon retention	Match today's intel against months or years of history to catch slow campaigns.
Context enrichment	Correlate an indicator with asset inventories, identity, and business data to cut false positives.
Custom detections and ML	Go beyond exact-match indicators with anomaly detection and entity risk scoring.
Detection-as-code	Version, backtest, and deploy intel-driven rules through CI/CD.
Natural-language hunting	Let analysts pivot on an indicator across all the data without deep query expertise.
Governance and audit	Log every query and automated action for forensics and compliance.

How Databricks approaches threat intelligence integration

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

• Ingest and normalize. Bring external feeds and internal telemetry together and normalize disparate sources (endpoint, network, identity, and cloud) to a common Open Cybersecurity Schema Framework (OCSF) schema for immediate correlation.
• Retain the history intel needs. Consolidate telemetry at petabyte scale on an open lakehouse and keep months or years of it, so an indicator can be matched against full history rather than a short retention window.
• Enrich with context. Correlate matches with enterprise context such as asset inventories and identity data, which sharpens triage and reduces false positives.
• Detections beyond exact match. Train anomaly-detection, behavioral, and entity-risk models on security data with MLflow, Feature Store, and Model Serving.
• Author and tune with AI. Genie can help author net-new detections from the latest threat intelligence and modify existing rules to reduce false positives, and detection-as-code lets teams write rules in SQL or Python, backtest them against history, and ship them through CI/CD.
• Hunt in plain language. AI/BI Genie lets analysts pivot on an indicator across petabytes of governed data in natural language, translating questions into SQL.
• Govern everything. Unity Catalog logs every Genie query and autonomous agent action for a full audit and forensic trail.

Lakewatch is available in Private Preview.

Use cases

• SOC detection engineering. Turn a new intelligence report into backtested detections quickly, and see which historical events they would have caught.
• Incident investigation. Pivot from a single indicator across endpoint, identity, and cloud logs to scope the full reach of an intrusion.
• Proactive hunting. Run intel-led hunts (a newly disclosed technique, a suspect domain) across long history in plain language.

FAQs

How does Databricks correlate external threat feeds with internal data?

It normalizes external feeds and internal telemetry to a common OCSF schema on the security lakehouse, so indicators and logs share one structure and can be correlated across endpoint, network, identity, and cloud sources.

Can it catch threats that simple IOC matching misses?

Yes. Beyond exact-match indicators, teams train anomaly-detection, behavioral, and entity-risk models with MLflow and Model Serving, and Genie can author detections from fresh intelligence, so novel or multi-stage activity surfaces even without a known indicator.

Do analysts need to know a query language to hunt on an indicator?

No. AI/BI Genie translates plain-language questions into SQL and runs multi-step hunts across petabytes of governed data, so analysts can pivot on an indicator without deep query expertise.

Is intel-driven detection auditable?

Yes. Detections run on governed data, and Unity Catalog logs every Genie query and autonomous action, giving a full audit and forensic trail for compliance and post-incident review.