How would you define the success of Panther's custom detection rules for threat hunting?

Introduction

Defining the success of Panther's custom detection rules for threat hunting is crucial for evaluating the effectiveness of our cybersecurity product. To approach this product success metrics problem effectively, I will follow a simple product success metric framework. I'll cover core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Panther's custom detection rules are a key feature of our cloud-native security analytics platform. These rules allow security teams to create tailored detections for specific threats in their environment, enhancing their threat hunting capabilities.

Key stakeholders include:

• Security analysts: Seeking to efficiently detect and respond to threats
• CISOs: Aiming to improve overall security posture and reduce risk
• IT teams: Looking for seamless integration with existing systems

User flow:

1. Analysts create custom rules using Panther's detection-as-code approach
2. Rules are deployed and continuously run against ingested log data
3. Alerts are generated when rules detect potential threats
4. Analysts investigate and respond to alerts

This feature aligns with Panther's strategy of empowering security teams with flexible, scalable threat detection. Compared to competitors like Splunk or Microsoft Sentinel, Panther's Python-based rules offer greater customization and ease of use.

Product Lifecycle Stage: Growth - The feature is established but still evolving with regular updates and expanding use cases.

Software-specific context:

• Platform: Cloud-native, leveraging serverless technologies
• Integration: APIs for connecting with various log sources and SIEM systems
• Deployment: Continuous integration/continuous deployment (CI/CD) for rule updates