Beat the Exploit Clock: From CVE Chaos to Proactive Remediation

Standard VM tools act as list-generators. They find vulnerabilities but lack the context of how those vulnerabilities relate to your business operations. The Integrated Solution integrates Threatworx's comprehensive scanning (covering code, cloud, and endpoints) with Rocketgraph's high-performance graph analytics. We don't just tell you what is broken; we show you how it endangers your critical assets (blast radius) and provide the AI-generated scripts to fix it immediately.

No. While Rocketgraph is built on powerful graph technology, our Mission Control interface allows you to use natural language (GenAI) to ask questions about your security posture. You ask, "Where am I most exposed to the latest zero-day?" and the system translates that into the necessary graph queries, presenting you with a visual answer and a remediation path.

Yes. Unlike legacy tools that stop at "reporting," the Threatworx engine within the Integrated solution provides active remediation capabilities. It can generate AI-validated code fixes for application vulnerabilities and remediation scripts for infrastructure issues, allowing you to patch holes in minutes, not months.

We attack false positives from two angles. First, Threatworx's Attenu8 engine uses AI to curate threat data from the dark web, filtering out "theoretical" risks that aren't being exploited in the wild. Second, Rocketgraph applies environmental context. If a vulnerable asset has no path to a critical system or no internet exposure, its risk score is automatically downgraded.

Scale is our DNA. Rocketgraph's xGT core is designed for Department of Defense-level workloads and can traverse hundreds of billions of nodes and edges in memory. Whether you have 10,000 assets or 10 million, the Integrated Solution analyzes the relationships between them in near real-time without the performance degradation seen in traditional graph databases.

The Integrated Rocketgraph and Threatworx Solution is designed to complement your EDR (Endpoint Detection and Response) and SIEM (Security Information and Event Management), not necessarily replace them. While EDR protects endpoints and SIEM collects logs, the Integrated Solution provides the Proactive Attack Surface Management layer. We ingest data from your EDR and SIEM into our graph to model attack paths, helping you prevent breaches before your EDR has to block them.

We offer the best of both worlds. Threatworx capabilities include a "Universal Scanner" that is agentless, developer-friendly, and capable of zero-trust discovery across cloud and container environments. For deep forensic analysis or specific compliance needs, lightweight agents are also available.

The Integrated Rocketgraph and Threatworx Solution is designed for rapid value. With Rocketgraph's high-speed connectors and Threatworx's API-first architecture, we can immediately ingest data from your existing clouds (AWS, Azure, GCP), code repositories, and asset managers. Most customers can visualize their attack surface and see critical attack paths within the first 24 hours of deployment.

Rocketgraph operates as a high-performance analytics engine rather than a primary system of record. While all processing occurs in RAM for speed, data persistence is handled through two primary mechanisms:

1. Snapshots & Checkpoints: You can configure Rocketgraph to periodically serialize the in-memory graph state to non-volatile storage (disk/SSD). In the event of a restart, the engine rehydrates the graph from the latest snapshot.
2. Source-of-Record Sync: Most deployments treat the in-memory graph as "ephemeral but synchronized." Data is persisted in your data lake (S3, Parquet, CSV) or database (Neo4j, Postgres,MongoDB etc.). Rocketgraph ingests this data on startup or via streaming updates. Results and enriched graph data can be written back to these persistent stores for long-term retention.

You do not lose your source data, as that remains in your persistent storage (S3, SQL, etc.). You lose only the volatile in-memory state since the last checkpoint. For mission-critical uptime, we recommend a high-availability architecture where a secondary Rocketgraph instance mirrors the primary, ensuring that if one node fails, the other can immediately serve queries without needing a cold reload.

We have closely integrated AI with the user-experience so analysts can get started with graph more quickly and produce more insights faster than they could previously.

The answer is more complex than a simple yes or no. But we have run a benchmark comparing the two which shows that Rocketgraph performs better than Neo4j on larger graphs and more complex queries. The benchmark can be found here.

First it’s important to set the context on how we evaluate the size of a graph. In our experience the best way to estimate the amount of work that needs to be done to search a graph is with the total number of edges. Nodes typically represent entities, like people, phones, computers, etc. whereas edges represent connections. So while we might have a graph with thousands of nodes, there could be tens of millions of connections. Rocketgraph has been used for graphs ranging in size from a few thousand edges to hundreds of billions.