Velociraptor
A powerful tool for endpoint visibility and incident response, leveraging the Velociraptor Query Language (VQL) for customizable data collection.
| Category | Incident Response & Forensics |
|---|---|
| Community Stars | 3046 |
| Last Commit | last week |
| Last page update | 19 days ago |
| Pricing Details | Free and open-source |
| Target Audience | Security teams, incident responders, forensic investigators. |
Velociraptor manages gaining comprehensive visibility into endpoint state information, a crucial aspect of modern security and incident response. At its core, Velociraptor leverages the Velociraptor Query Language (VQL) to define and collect a wide range of artifacts from endpoints. This approach allows for highly customizable and targeted data collection, making it a powerful tool for security teams.
The technical architecture of Velociraptor is built around a client-server model, where the server can be deployed locally or via Docker, and clients can be configured to collect specific artifacts defined by VQL queries. The GUI provides an intuitive interface for building and managing collectors, as well as uploading and analyzing collected data. The community-driven Artifact Exchange further enriches the tool by providing a repository of pre-defined artifacts for common use cases, which can be easily integrated into deployments.
Operationally, Velociraptor requires careful consideration of deployment scale and resource management. For instance, building and running the GUI and server components necessitate specific dependencies, such as Node.js and Golang tools. Additionally, the use of Docker for server deployment can simplify management but may introduce additional complexity in multi-environment setups. The tool's performance can be optimized through careful configuration of artifact collection and data retention policies, though large-scale deployments may require careful resource allocation to avoid performance degradation.
Technically, Velociraptor's VQL queries allow for granular control over data collection, with the ability to collect a wide range of artifacts, from file system data to network activity. The integration with Sigma rules through the velociraptor-sigma-rules repository adds another layer of detection capability, enabling the conversion of Sigma rules into VQL queries. This flexibility, however, comes with the need for thorough understanding and management of the query language and artifact definitions to ensure effective and efficient data collection and analysis.