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The final instalment of our blog series on AI’s potential for the threat intelligence cycle is here. After all the directing, collecting, and analysing, it’s time to disseminate, or publish the insights gathered for your client.  Dissemination is the most overlooked intelligence-cycle phase. Maybe that’s because it’s the end and,

We’ve been busy thinking and writing about AI as applied to the four phases of the threat intelligence cycle (see our blogs on direction and collection), and it’s now time for analysis.  Can AI be a good match for what is arguably the most glamourous phase? When the Cyber Threat

Our first blog about the use of AI in the intelligence cycle covered the cycle’s first phase, direction. AI in collection, the next phase, is our focus of discussion today.  Of all the phases, collection is easily the most routine, mechanical, or even atomic. Unlike with the direction phase, collection

We recently blogged about which AI form the CTI analyst should use, sparking a conversation at Elemendar about applying AI to the threat intelligence lifecycle. If we consider each cycle phase to have a beginning, a middle, and an end, at which stage/s would AI be beneficial, or not?  This

AI has become nearly a household term. Its influence has seeped into multiple areas of our lives, promising direct benefits: significantly reducing errors in daily work, increasing accuracy and precision, and giving us freedom from repetitive tasks. Let’s take a look at how AI can play a supporting role in

Ever read a classic crime novel? The villain’s identity only becomes clear after a couple of plot twists and turns. Attribution in Cyber Threat Intelligence (CTI) sometimes feels like that kind of detective work. For the cyber-threat analyst, gathering clues and assigning responsibility for cyber-attacks to specific individuals, threat groups

“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 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

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,

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”