We are hiring!

National Cyber Security Centre

Elemendar was founded in 2017 by Giorgos Georgopoulos and Syra Marshall at the UK’s first GCHQ / NCSC Cyber Accelerator, powered by Wayra UK, to develop Cyber Threat Intelligence (CTI) enrichment capabilities.

Elemendar is the leader in developing AI to translate CTI into machine readable and instantly actionable data. This can be fed into systems such as SIEMs and TIPs directly to reduce risk and return valuable time to analysts at the forefront of organisations’ defence across enterprise, government and law enforcement. Elemendar’s technology is used by both government and private customers.

Our AI automates the reading and translation of CTI from human authored unstructured text and documents into machine readable and actionable data output as STIX 2.0 and incorporating MITRE ATT&CK™. Our technology utilises leading-edge Machine Learning (ML) and Natural Language Processing (NLP) technologies.


Elemendar’s technology can improve processing time and triage accuracy by capturing the STIX data modelling of our analyst team in a scalable environment to create a timelier and more valuable product for our customers. EclecticIQ’s Fusion Center team is working with Elemendar to improve our processing of reports with Elemendar’s remarkable technology.

Chris O’Brien, Director Intelligence Collaboration, EclecticIQ

The NCSC continues to support the cutting-edge companies from our Cyber Accelerator programme and we’re delighted to see Elemendar – from our first cohort – flourish. The interplay between threat analysis and machine-driven defence remains a challenge for the industry, but Elemendar have recognised this challenge and their product offers substantial opportunities for improved efficiency and effectiveness.

Chris Ensor, Deputy Director Cyber Skills & Growth, UK National Cyber Security Centre

We chose Elemendar because we see the greatest common development potential here as OMV is already very active in the area of ​​cybersecurity and Elemendar has developed an AI-based solution to automate the analysis of data about cybersecurity threats.

Jan Leitermann, CIO, OMV

Our Team

We have created Elemendar as we are passionate about security. Organisations and cyber analysts are drowning in critical CTI and we want to make it usable for all, not only the 1% who can afford analyst teams.

Giorgos Georgopoulos, Elemendar team

Giorgos Georgopoulos

Founder and CEO

Syra Marshall, Elemendar team

Syra Marshall

Founder and CTO

Devon Barrett, Elemendar team

Devon Barrett


Tristan Palmer

Tristan Palmer

CMO and Head of Growth

Chris Evett

Chris Evett

Programme Manager

Tristan Palmer

Rita Anjana

Head of Machine Learning

Ananya Kapoor

Devon Barrett, Elemendar team

Bharath Suresh

Evans Odeh

Lee Jones

Süleyman Sümertaş

Ben Strickson

Investors & Advisors

Hector Monsegur

Hector Monsegur

Hector Monsegur, Elemendar advisor

David Moloney

Dr Kimmo Soramäki

Dr Kimmo Soramäki

Dr Kimmo Soramäki, Elemendar advisor

Jami Jenkins

Mia Bennett, Elemendar advisor

Mia Bennett

Mia Bennett, Elemendar advisor

Sven Ripper

Nathan Koren, Elemendar advisor

Nathan Koren

Lawrence Lundy-Bryan

What We Do

Elemendar translates unstructured reports into STIX outputs for Cyber Analysts.
Our AI makes your analysts more efficient, instantly flagging threats.
We read reports in seconds, not hours.

Save your analysts’ time.
Empower them to focus on what’s most important.

What we do at Elemendat


In one hour of an analyst’s job, 50 minutes is reading, 10 minutes is valuable analysis. Elemendar shortens the reading to 50 seconds, increasing efficiency up to 5x. Our customers using the A.I. say it can perform as well as a junior analyst.

Incoming C.T.I. is automatically converted into machine readable, actionable data. Using STIX2 (and imminently, MITRE ATT&CK™), this can be exported / imported directly into a SIEM or TIP.

Elemendar uses long short-term memory (LSTM) recurrent neural networks (RNNs), building on NeuroNER, Tensorflow and spaCy. We currently achieve precision >98% and recall >91% for NER. As our A.I. learns more, this will go even higher.

Where We’re Going


2018 - Read - CTI is read and translated into STIX

C.T.I. is read and translated into STIX2.


2019 - Remember - Networks are built by correlating across source docs

Networks are built by correlating across source C.T.I. docs.


Relationships are understood between multiple data points. Network analysis to find patterns

Relationships are understood between multiple data points. Network analysis to find patterns


2021 Orchestrate - The patterns are used to share enriched context with other tools both ways.

Patterns are used to share enriched context with other tools going both ways. Iterative machine-led action on C.T.I.