Data Pipeline

All your security data, in one place.

Native connectors, automatic normalization, and petabyte-scale ingestion.

Trusted by top security teams

Complete Visibility

Ingest every source without cost tradeoffs

Ingest every log source without cost tradeoffs, eliminating unknown blind spots and protecting your entire environment.

Unified Context

All your security data in a single queryable layer

Every log source is immediately accessible, normalized, and ready to query — so your team spends time on analysis, not data collection.

Data-Grounded AI

Deterministic AI findings

The quality of every AI finding is a direct reflection of the data underneath it, and Panther investigates against a complete, normalized dataset every time.

Control what reaches your data lake.

Filter low-value events before they consume quota, and use transformations to reshape data at ingest so detections and threat hunting queries run against clean, structured logs.

Built for security data at scale.

• Pre-built Integrations
  Native integrations for the tools your team already uses like AWS, Okta, CrowdStrike, Slack, and more.

HealthEquity reduced investigation times by 90% with Panther. That's data-grounded AI in production.

Proof from teams who’ve been there.

• 5x More log data ingested

• 9x More data ingestion

• 90% Infrastructure visibility achieved

Frequently asked questions

Why does the quality of the data pipeline determine the quality of AI findings?

AI investigation results are only as complete as the data the agent can see. An agent working against a partial dataset, because certain sources were too expensive or complex to onboard, produces findings with gaps. Panther normalizes every source into a consistent schema at ingest, so when the AI SOC agent investigates an alert, it draws on a complete dataset rather than whatever your team had budget to include. That completeness is what makes findings trustworthy enough to act on.

How does Panther's ingestion pricing differ from traditional SIEM pricing?

Traditional SIEMs charge based on daily ingest volume, which forces security teams to decide which log sources are worth monitoring and which aren't. Panther's pricing doesn't penalize ingestion volume growth, so coverage decisions aren't also cost decisions. Snyk reached 90% infrastructure visibility after switching, and Cockroach Labs ingested 5x more log data than their previous setup could support.

How does the Log Forwarding Agent work, and when should I use it?

The Log Forwarding Agent is a lightweight component you deploy in your environment to collect and forward logs from sources that don't support direct API ingestion. It's most useful for on-premises infrastructure, air-gapped environments, or sources that require local access to collect.

Can I query Panther security data directly in Snowflake or Databricks?

Yes. Panther runs natively on your existing Snowflake or Databricks instance, so your security data stays in infrastructure you already own. Analysts can query it directly using SQL or PantherFlow.

What is a security data lake, and why does it matter for detection and investigation?

A security data lake is a centralized store of normalized security telemetry: logs from cloud infrastructure, identity providers, endpoints, SaaS apps, and custom sources. Unlike a traditional SIEM, which indexes data in a proprietary store, a security data lake keeps data in open, SQL-queryable formats.