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Securing Your Future: ArangoDB for Advanced Threat Management
“We use ArangoDB to find malware in a network by ingesting all the traffic and then using ML to connect and correlate detections and identify real threats. ArangoDB is very powerful at identifying real intent within the threat landscape."
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(no credit card required), and experience the shortest time to value for a hosted graph DB.
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Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.
Comprehensive
Relationship Analysis
Leverage native multi-model support to represent diverse threat data, such as IP addresses, domains, and user accounts, in a single database. By modeling relationships between entities, security teams can uncover complex threat patterns and attack vectors before they wreak havoc.
Real-Time Graph Traversal
Security analysts experience more efficient and deeper graph traversal to navigate interconnected threat data rapidly and thoroughly. This facilitates quick identification of relationships between malicious entities, aiding in the detection and response to exponentially more threats than previously possible.
Predictive Insights
Security teams use ArangoDB’s advanced graph algorithms and machine learning capabilities to build predictive models for threat detection. By analyzing historical threat data and relationships, they can identify patterns that signal potential attacks and proactively mitigate risks.
Contextual Investigation
Add contextual information to threat data using ArangoDB’s schema-flexible nature. This contextual enrichment helps in understanding the broader context of threats, such as their origin, impact, and affected assets, streamlining incident response.
Scalability & Performance
Truly robust threat detection demands unparalleled scalability and performance. Unique among its peers, ArangoDB empowers security teams to analyze the largest, most complex and interconnected datasets to identify and mitigate threats, even in real-time. ArangoDB's unique ability to scale horizontally (and not just vertically) lowers costs while rapidly adding capacity as data volumes grow and real-time requirements become more urgent.
ArangoDB vs. Legacy Graph DBs
For Cyber / Threat Management
 | ArangoDB For Cyber / Threat Management | Neo4j & Others for Cyber / Threat Management |
|---|---|---|
| Represent complex threat relationships |  |  |
| Threat data relationship traversal | | |
| Real time threat landscape updates | | |
| Dynamic schema to handle evolving threat data | | |
| Comprehensive data model (graph, document, search, key/value) for unified view of threat data | | |
Get started
Free today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Read the
Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.