Building a security data platform

The comprehensive Security Data Platform architecture, as detailed by Kadd Systems, offers a robust framework for organizations aiming to build or enhance their security stacks for various use cases such as Security Operations Center (SOC) applications. This platform leverages modern data lakes and warehouses, utilizing tools like Snowflake, Databricks, and Starburst, which is built on open-source Trino, to facilitate a federated approach ideal for hybrid environments. Essential components include data ingestion, where existing pipelines can incorporate security telemetry via tools like Vector.dev and Python-based systems orchestrated with Airflow, and data standardization and normalization, ensuring a uniform data model across multiple sources. The architecture also emphasizes streaming capabilities with platforms like Kafka, serving as a buffer and enabling layered detections, while storage engines can be any major cloud provider or on-prem solutions like MinIO, with Apache Iceberg/Delta serving as the table format. Starburst's compatibility with various specialized platforms such as Neo4j, Elastic Search, and Clickhouse, along with its integration with Jupyter notebooks, enhances capabilities for machine learning and threat hunting. The platform supports a wide array of organizational teams, including SOC, Insider Threat Detection, and Machine Learning teams, offering use cases from on-demand reporting and data correlation to complex threat hunts and integration with collaboration tools like Slack. Its decentralized methodology is particularly advantageous for federal agencies and organizations with hybrid cloud architectures, providing a cost-effective and flexible solution to centralized or decentralized data management.