Panther Security Lake

Panther's Security Data Lake addresses the core challenge of centralized security data management and analysis, particularly in cloud-scale environments. The technical architecture of Panther's Security Data Lake is designed to ingest, normalize, and enrich vast amounts of security data from various sources, including AWS CloudTrail, VPC Flow, and other cloud resources.

At the heart of Panther's approach is the use of ETL (Extract, Transform, Load) pipelines to coalesce data into a structured and normalized format. This process involves parsing, normalizing, and transforming unstructured data into a unified data model, which is then enriched with contextual information such as identity, vulnerability, and business context. This enrichment is crucial for eliminating noise and prioritizing higher-risk threats.

The integration with Amazon Security Lake and support for the Open Cybersecurity Schema Framework (OCSF) ensures seamless ingestion of standardized data, enabling security teams to leverage Panther's detection-as-code capabilities. This integration allows for real-time detections and efficient alert response workflows, facilitating rapid incident response and comprehensive analysis of historical logs.

Operationally, Panther's Security Data Lake offers several key benefits. It provides petabyte-scale search capabilities with significant performance improvements, especially when handling large volumes of data. The separation of compute and storage resources allows for instant scalability, which is essential for incident response and threat hunting. Additionally, the platform includes fully managed data features such as encryption, compression, and governed policies, ensuring robust security and compliance.

However, there are operational considerations to note. While the platform is highly scalable, the cost of storage and compute resources can vary based on usage. The use of hot storage ensures rapid access to data, but this can impact costs, especially in multi-petabyte environments. Moreover, the effectiveness of the platform relies on the quality of the data normalization and enrichment processes, which can be resource-intensive.

In terms of technical details, Panther supports over 100 security log types across 50 different categories and allows for custom log ingestion using schema inference. The platform delivers real-time alerts and supports multi-event correlation to reduce noise and alert fatigue. The unified search functionality, built on top of Snowflake, enables analysts to conduct complex searches without requiring in-depth SQL knowledge, using an intuitive query-building UX.