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“Pivoting” is an often overlooked but critical feature of the day-to-day work of a Cyber Threat Intelligence (CTI) analyst. Possibly the earliest reference to the concept is within a cornerstone work for CTI, “The Diamond Model of Intrusion Analysis”, with the idea elaborated in the graphic below: Fig 1: The

Elemendar’s READ. platform uses proprietary Machine Learning (ML) technology to extract entities in STIX and MITRE ATT&CK formats from unstructured text (check out this blog for more details). In plain terms, we teach machines to replicate a very specific human task: mapping text strings to more abstract categories such as

Here at Elemendar we spend a lot of time talking to the global Cyber Threat Intelligence (CTI) analyst community. While we are well versed in all aspects of CTI, the main topic of conversation is typically Artificial Intelligence (AI) and the role of Machine Learning (ML) within structuring unstructured data.

Elemendar has recently rolled out a graph editing feature within the READ. platform which allows the user to edit entities within a source document through a graph-based visual representation of the document. We’re now excited to present this deep dive into Graph Editing! Previously, the only way to edit documents

We hope you are enjoying summer and staying safe wherever you may be. With in-person events reopening, the Elemendar team have been busy travelling and even met many of you these past couple of months. Here’s to that continuing!  Product Updates  Elemendar’s CTO Syra Marshall Speaks at Defence Disrupted Conference

MITRE is a leading light in the current cyber threat intelligence (CTI) scene being the pioneer of the MITRE ATT&CK framework and the associated data sources. For those unfamiliar with MITRE ATT&CK take a look at the website as the first port of call; however, as a very brief summary,

Pictured (from left to right) Stewart Bertram, Giorgos Georgopoulos and Ben Strickson The RSA Conference hosted in San Francisco burst back onto the cyber security scene after a two year COVID enforced hiatus – and some of the Elemendar team were there to see it!  So, what were the main

As a new joiner, one of my first tasks involved familiarising myself with Elemendar’s READ. tool - a tool designed to leverage Machine Learning techniques to add structure to unstructured CTI data such as white papers, reports and articles. As READ. works in two main ways: either in a “headless”

Elemendar’s READ. uses machine learning techniques to add structure to unstructured data in a STIX format. Given how much of the world’s Cyber Threat Intelligence (CTI) data is stored in an unstructured format, the strategic design rationale behind the READ. tool is obvious; however, we have found that some of

The MITRE ATT&CK framework has, quite rightly become one of the cornerstones of modern cyber threat intelligence. For those not aware of the framework, the core of the system is the ATT&CK Navigator, shown below: Figure 1: MITRE ATT&CK framework (X and Y axis) The X Axis in Figure 1