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.

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. Both outcomes are structurally difficult to achieve when pricing creates a direct tradeoff between breadth and budget.

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. The agent handles batching, compression, and delivery so logs arrive normalized and ready for detection without custom pipeline work on your end.

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, and the data lives in your cloud account rather than Panther-managed storage. There are no data residency tradeoffs, no proprietary formats to work around, and no lock-in on the data layer if your needs change.

Panther ships with native integrations for AWS (CloudTrail, GuardDuty, VPC Flow Logs, S3, and more), Okta, CrowdStrike, Google Workspace, GitHub, Slack, Zeek, and dozens of others. For sources without a pre-built connector, the AI Schema Builder generates a schema from a sample payload. For environments where direct pull-based ingestion isn't feasible, the Log Forwarding Agent handles collection and delivery without requiring custom pipeline work.

A security data lake is a centralized store of normalized security telemetry: logs from cloud infrastructure, identity providers, endpoints, SaaS apps, and custom sources, all held in a cloud data warehouse you own and query directly. Unlike a traditional SIEM, which indexes data in a proprietary store, a security data lake keeps data in open, SQL-queryable formats. For detection and investigation, this means analysts and AI agents work against the same complete dataset, with no ingestion-budget decisions forcing you to leave sources unmonitored.

Panther normalizes log data at the point of ingestion, mapping each source to a structured schema before it reaches your data lake. Every event is tagged, typed, and queryable from the moment it lands, with no post-processing step and no second pipeline to maintain. For custom or uncommon sources, the AI Schema Builder generates a schema automatically from a sample payload, so onboarding a new log type doesn't require manual configuration work from your team.