Cloud Detection Engine for Modern Security Teams | Panther

Detection Engine

Detection that improves itself over time

Native access to your detection logic means every triage outcome feeds back into the rules that fire.

Compounding Intelligence

Alert quality improves automatically as your team works

When an alert resolves as a false positive, AI traces it to the source rule and proposes a fix via GitHub pull request. Your team reviews and approves it. The same false positive doesn't come back.

Learn more about closed loop AI

Proactive Coverage

Build detections from threat descriptions, not just known patterns

Detection coverage is limited by how many rules someone had time to write. Describe a threat behavior in natural language and Panther generates a complete Python detection with filters, dynamic severity, and test cases.

Learn more about our AI detection builder

Complete Context

Agents read and modify your detection logic because it lives in code

Because detections are structured Python with version control, agents can read exactly why an alert fired and write a specific fix rather than a general recommendation.

Learn more about detection-as-code

Autonomous Action

Detection improvements ship through the same review process as your code

Detection engineers stay in control of what fires and why. The role shifts from writing every rule manually to reviewing AI-proposed improvements: same quality bar, fraction of the effort.

Learn more about AI-enabled CI/CD

How it works

AI rule generation
Detection-as-code
Alert quality loop
GitHub PR workflow
Exploratory detections

AI rule generation

Describe a threat scenario or known TTP and Panther produces a complete Python detection with filters, dynamic severity logic, and test cases.

Proof from teams who’ve been there.

Frequently asked questions

Why does using Python for detections give AI agents better access to detection logic than proprietary query languages?

Large language models work well with Python. They can read a detection rule, understand its logic, identify why it fires, and write a specific modification. Proprietary query languages like Splunk's SPL or CrowdStrike's CQL are not structured in ways LLMs parse reliably, which is why AI features built on top of legacy SIEMs tend to offer general recommendations rather than specific code changes.

Can detection engineers maintain control over what Panther's AI proposes and deploys?

Yes, and that control is structural rather than just a setting. Nothing in Panther's detection engine deploys without human approval. AI-generated rules and AI-proposed tuning changes are both presented as reviewable code through GitHub.

What are exploratory detections, and how do they differ from standard detection rules?

Standard detection rules fire when known patterns match. Exploratory detections work the other way: they analyze broad datasets to surface suspicious activity that no pre-written rule would have caught.

How does the GitHub PR workflow for detection improvements work?

AI-proposed detection changes appear in your existing GitHub repository as pull requests, formatted the same way a detection engineer would submit code.

What is the alert quality loop, and how does it reduce false positives over time?

When an analyst triages an alert as a false positive, Panther traces the outcome back to the specific Python rule that fired it and evaluates whether the detection logic can be refined.

How does Panther's AI rule generation work?

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

What is detection-as-code, and why does it matter for a modern security team?

Detection-as-code means writing security detection rules as version-controlled code rather than configuring them through a GUI or proprietary query interface.

Bolt-on AI closes alerts. Panther closes the loop.

Detect, investigate, and respond to threats at cloud scale — powered by code and AI.