Assistive AI for

Security Analysts

A graph-first approach to modeling, analyzing, and understanding security data. No playbooks required.

Our Cyber Knowledge Graph links data and identifies relationships across multiple data sets, instantly — pinpointing where issues reside in your system, what they connect to, and the actual risk with problems at that attack vector.

Request a demo with the founders of Cybermonic.

What it Does

Empower analyst workflows with graph analysis

We tell the true story behind your security alerts, automatically, enabling analysts to seamlessly search and see the relationships between all data sources from a single UI.

Search

Unified search works across all data sources to find entities and relationships of interest.


Analyze

Analyze graph relationships by exploring and pivoting through findings to understand connections and risk.


Triage

Graph AI algorithms automatically contextualize and rank security alerts identifying the most critical events.


Detect

Detect unusual activity and risky entities for proactive remediation.


Save time, money, and stress

Cybermonic's novel approach to cybersecurity increases efficiency and efficacy of analysts, resulting in more time allocated to the events that matter most.

+80%

Time Savings

When analysts used Cybermonic to triage alerts, they averaged 80% less time to perform a similar analysis compared to their standard methods.


+90%

Accuracy

Cybermonic has demonstrated highly accurate detection of sophisticated APT attack scenarios using behavioral graph-based anomaly detection.


10x

Data Coverage

Users can ingest more than 10 unique heterogeneous data sources into the Cyber Knowledge Graph providing instant correlation and analysis.


How it Works

Simple & seamless integration with your existing tools

Cloud native, UI and API driven installation and configuration means you won't burden your team with tool management and maintenance. In as little as a few minutes, you can plug Cybermonic into your existing systems and enjoy:


Automatic Log Parsing

Graph data models are extremely flexible, allowing us to ingest all of your logs into a single source of truth for your environment. This process is as simple as determining logs to be ingested and configuring an appropriate data connector.


Graph AI Analysis

Once your data is converted into a Cyber Knowledge Graph, we apply a host of Graph Analytics and Graph AI algorithms designed to learn patterns of relationships between entities in the graph. This knowledge is then leveraged to assign risk scores to activity, as well as aid alert triage, incident response, and threat hunting.


Visual Analysis Platform

Explore and analyze the Cyber Knowledge Graph with an intuitive visual analysis platform. Perform complex analysis tasks with simple search terms and graph transformations -- all in a no-code environment.


Founding Team

Business focus with academic rigor

The founding team is comprised of researchers with a passion for Graph Theory, Data Science, and Cybersecurity to help businesses scale securely.

Howie Huang, Ph.D.

Co-CEO

In addition to his role as CEO of Cybermonic, Dr. Huang is also a Professor in the Department of Electrical and Computer Engineering at the George Washington University, where his GraphLab works on graph analytics, machine learning, and cybersecurity applications. His research has been supported by roughly 20 grants of close to $7M from NSF, DARPA, DoD, and companies including Raytheon, IBM, NVIDIA and Comcast.



Benjamin Bowman, Ph.D.

Co-CEO

Benjamin is a cybersecurity expert ranging from source code analysis to APT detection based on novel graph-based systems and algorithms. Before pursuing his PhD and subsequently founding Cybermonic, Benjamin was a software engineer for AT&T where he was developing a defensive cyber security appliance used to protect and monitor the AT&T enterprise network.

Research

Our team has a proven track record from DARPA funded research programs focusing on application of graph systems and algorithms to various cybersecurity problems

Our work published in RAID leveraging Graph AI for lateral movement detection.

Our position paper published in ACM SIGOPS justifying the increased use of graph analysis for cyber.

Our work published in EuroS&P utilizing graph analytics for detecting vulnerable source code.

Careers

Join our team!

We are looking for talented & motivated individuals with a passion for entrepreneurship, cybersecurity, and deep technology. Our current job postings are listed below.

Cybersecurity Software Marketing Executive - Details