Documentation overview highlighting key features and benefits of Panther's cloud-native threat detection platform
Panther is a cloud-native threat detection platform that transforms terabytes of raw logs per day into a structured security data lake to power real-time detection, swift incident response, and thorough investigations.
With detection-as-code in Python and out-of-the-box integrations for dozens of critical log sources, Panther solves the challenges of security operations at scale.
A diagram showing how Panther works: It ingests and normalizes security logs then alerts your team of suspicious activity.
It works by normalizing security logs from various places and alerting your team when suspicious activity happens.
Focus on security, not ops with a cloud-native architecture that eliminates the need to manage servers, storage and updates.
Detect threats immediately by analyzing logs as soon as they are ingested, giving you the fastest possible time to detection.
Answer security questions quickly with the ability to immediately query months of data in minutes and efficiently search for IoCs across all logs.
Reduce false positives with Python Detection-as-Code, and CI/CD workflows for creating, testing, and deploying detections.
Expedite incident response by adding dynamic context to alerts to power more efficient routing, triage, and automation.
Reduce SIEM costs dramatically while gaining lightning-fast query speeds, with an efficient, highly scalable data lake architecture.
Effortless Data Ingestion: Built-in support for common data transports such as S3, SQS, SNS, and out-of-the-box integrations for critical log sources like Okta, Duo, Slack, Google WorkSpaces, and more.
Log normalization: Logs are parsed and IoC fields like domains and IPs are normalized to support analysis, searches and correlations across all log types.