Collects assets and relationships from cloud, SaaS, and security systems into a Neo4j graph for security analysis.
Starbase is an open-source graph-based security analysis tool that collects assets and relationships from cloud infrastructure, SaaS applications, and security systems into a Neo4j database. It provides a unified view of an organization's digital footprint, enabling security teams to analyze connections and identify risks across diverse environments. The platform automates data ingestion and classification to simplify security query development.
Security engineers, DevOps teams, and IT professionals who need to map and analyze assets across cloud and SaaS environments for security posture management. It is particularly valuable for organizations with complex, multi-service infrastructures requiring consolidated visibility.
Developers choose Starbase for its extensive out-of-the-box integrations, uniform data model that simplifies querying, and open-source extensibility. It offers a self-hostable alternative to commercial security graph platforms, providing deep, relationship-focused security analysis without vendor lock-in.
Graph-based security analysis for everyone
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Supports 115+ open-source graph integrations out-of-the-box, including major cloud providers like AWS, Azure, and Google Cloud, as detailed in the README's expanded list.
Automatically classifies collected data into a consistent model, enabling generic queries across different systems without custom mapping, as highlighted in the 'Why Starbase?' section.
Built on a modular SDK, it allows easy development of custom graph integrations, with active community contributions and clear documentation for extending the platform.
Leverages Neo4j for storing entities and relationships, facilitating complex security analysis like attack path mapping, as shown in the demo GIF and emphasized in the philosophy.
Requires manual configuration of a `config.yaml` file with API credentials for each integration and separate Neo4j setup, which can be time-consuming and error-prone for newcomers.
Data collection is triggered via CLI commands like `yarn starbase run`, lacking built-in real-time synchronization or automated scheduling, limiting dynamic security monitoring.
Tightly couples the platform to Neo4j as the primary storage backend, reducing flexibility for teams using other databases or preferring a vendor-agnostic approach.
The README notes slower execution times on macOS when using Docker, and handling large datasets may require Neo4j optimization, impacting scalability for resource-constrained teams.