About
Panther is a next-generation Security Information and Event Management (SIEM) platform built for the cloud era. It combines a scalable security data lake, a Python-based detection engine, and embedded AI agents to help security operations center (SOC) teams move from raw alert to closed investigation in minutes rather than hours. At the core of Panther's platform are autonomous AI agents that perform triage, deep investigation, threat hunting, and detection improvement — all with the same depth as a senior analyst. Agents pivot across the full data lake, alert history, and enrichment sources to produce complete investigation reports automatically. Critical threats are escalated; benign alerts are auto-closed. Detection improvements are even proposed as GitHub pull requests, keeping humans in control while AI handles volume. What truly differentiates Panther is its closed-loop learning system: every triage decision, false positive, and investigation outcome feeds back into the detection engine, making the platform measurably smarter over time. Customers report 85% reductions in alert volume, 70% faster detection tuning, and security teams of 5 operating at the capacity of 20. Panther is purpose-built for enterprise security teams managing cloud infrastructure at scale, and is ideal for organizations looking to modernize their SOC without sacrificing analyst control or data ownership.
Key Features
- AI Triage & Investigation Agents: Autonomous agents pivot across the full data lake, alert history, and enrichments to deliver complete investigations in minutes, not hours — eliminating manual analyst toil.
- Closed-Loop Learning System: Every triage outcome, false positive, and investigation feeds back into the detection engine, so the platform gets measurably smarter with every alert worked.
- Automated Alert Resolution: Benign alerts are auto-closed, critical threats are escalated, and detection improvements are proposed as GitHub PRs — keeping humans in control while AI handles the volume.
- Continuous Threat Hunting: Scheduled hunts surface threats for which no rules have been written yet, continuously expanding coverage without requiring analyst time to author new detections.
- Unified Security Data Lake: A scalable, cloud-native data pipeline and lake provides cross-system visibility, enabling correlation across all log sources at enterprise scale.
Use Cases
- Enterprise SOC teams automating alert triage and investigation to reduce analyst workload and mean time to respond (MTTR).
- Cloud-first organizations needing unified visibility across multi-cloud and SaaS log sources in a scalable security data lake.
- Security engineering teams building and continuously improving Python-based detections with AI-assisted tuning and GitHub integration.
- Lean security teams (5–20 engineers) that need to operate at the scale of a much larger SOC without additional headcount.
- Organizations modernizing legacy SIEM infrastructure with a cloud-native, AI-powered platform that reduces costs and improves detection coverage.
Pros
- Massive alert volume reduction: Customers report up to 85% reduction in alert volume, allowing lean security teams to focus only on what matters most.
- Fast SOC deployment: Teams have stood up a fully deployed, in-house enterprise SOC in a matter of weeks thanks to Panther's out-of-the-box AI capabilities.
- Self-improving detection engine: The platform learns from analyst behavior over time, reducing repeated false positives and improving detection accuracy continuously.
- Developer-friendly detection engineering: Python-based detection rules and GitHub PR integration make detection engineering approachable for security engineers with a coding background.
Cons
- Enterprise pricing: Panther is an enterprise-grade platform with no publicly listed pricing or free tier, making it less accessible for small teams or startups with limited budgets.
- Requires cloud data infrastructure: The platform is built around cloud-native architectures; teams with on-premise or legacy infrastructure may face integration challenges.
- Learning curve for detection engineering: Advanced customization using Python-based detection rules requires security engineers comfortable with code, which may not suit all teams.
Frequently Asked Questions
What is Panther and how does it differ from a traditional SIEM?
Panther is a cloud-native AI SOC platform that combines a security data lake, detection engine, and autonomous AI agents. Unlike traditional SIEMs that require manual analyst review of every alert, Panther's agents autonomously triage, investigate, and resolve alerts while continuously learning from outcomes to improve over time.
How does Panther's AI agent system work?
Panther embeds AI agents across triage, investigation, detection creation, and threat hunting workflows. Agents access the full data lake, alert history, and enrichment sources to produce complete investigations automatically. They can auto-close benign alerts, escalate critical threats, and even propose detection improvements as GitHub pull requests.
What kind of alert reduction can teams expect?
Customers have reported up to 85% reductions in alert volume after deploying Panther's AI SOC capabilities, allowing smaller security teams to operate at a significantly higher capacity.
Which companies trust Panther for security monitoring?
Panther is trusted by security teams at well-known companies including Zapier, Dropbox, Asana, Docker, Snyk, HealthEquity, Loglass, and Infoblox, among others.
Does Panther require a large security team to operate?
No. Panther is specifically designed to help small security teams operate at enterprise scale. Customers have reported that a team of 5 engineers can act with the capacity of 20 engineers using Panther's AI SOC platform.
