Detection Engineering

Detections your team owns. Logic your AI can improve.

Every rule is readable, version-controlled, and modifiable by AI.

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Trusted by top security teams

Built for detection engineers. Amplified with AI.

AI Detection Builder

Closed-Loop Tuning

GitHub PR Workflow

MITRE ATT&CK Coverage Mapping

Pre-built Detection Library

Real-time Testing

AI Detection Builder

Describe a threat in plain language and get a production-ready Python detection — ready to deploy, tune, and test from day one.

Detection Velocity

Coverage for emerging threats, shipped in minutes not sprints.

Turn a natural language threat description into a complete Python detection — filters, severity logic, and test cases included — so your detection library keeps pace with the threat landscape.

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No Black Boxes

Your detection logic has nothing to hide.

Because detections are written in Python, your team can audit, modify, and improve every rule directly — no vendor tickets, no proprietary constraints, no logic you can't inspect.

Learn more about our detection-as-code

Closed-Loop Detection

Every false positive makes your detection program stronger.

Every triage outcome traces back to the rule that fired it. Panther identifies the source detection and proposes a fix, so the same false positive doesn't come back.

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Defensible Coverage

Proven enterprise-level maturity.

Provide security leaders with continuous visibility into detection coverage and program performance, so every conversation with auditors, customers, and the board starts from a position of confidence.

Learn more about our framework mapping

Frequently asked questions

How does MITRE ATT&CK coverage mapping help security leaders communicate program maturity?

The MITRE ATT&CK framework catalogs known adversary tactics and techniques.

How does Panther's pre-built detection library work, and can teams customize it?

Panther ships with 300+ detections covering common attack patterns across cloud infrastructure, identity providers, endpoints, and SaaS applications.

What does it mean to own your detection logic, and why does it matter?

When detections live in proprietary vendor systems, your team can see what fires but not always why, and modifying logic often means filing a support ticket or navigating a GUI with limited expressiveness.

How does Panther handle the false positive problem without requiring manual tuning cycles?

When an alert resolves as a false positive, Panther traces the outcome to the specific rule that generated it and evaluates whether the detection logic can be refined.

How does Panther help detection engineers ship new coverage faster?

Describe a threat scenario or known TTP in natural language and Panther generates a complete Python detection with filters, dynamic severity logic, and unit tests.

What causes detection programs to fall behind, and why is it hard to catch up?

Detection backlogs compound.

What is detection engineering, and how is it different from writing detection rules?

Detection engineering is the discipline of building, testing, maintaining, and improving the logic that determines what your security platform alerts on.