Darktrace Cyber
AI Research Centre
Research unlocks the unknowns; it also helps shed light on what we are collectively up against.
— Jack Stockdale OBE, CTO
Our history
The Darktrace AI Research Centre is foundational to our continued innovation. Teams of mathematicians and other experts examine how AI can be applied to real-world problems, to find new paths forward to augment human capabilities.
With more than 130 patents and patents pending, the AI Research Centre comprises more than 200 R&D employees, including experts with ~100 master's degrees and 20 doctorates in disciplines from astrophysics to linguistics to data science.
With more than 130 patents and patents pending, the AI Research Centre comprises more than 200 R&D employees, including experts with ~100 master's degrees and 20 doctorates in disciplines from astrophysics to linguistics to data science.
Rather than confined to work on product roadmaps, researchers are free to experiment and explore, guided by creative insight arising from pure research. Yet we have never shied away from taking on real challenges faced by the industry, and the AI Research Centre operates with a common goal of solving these challenges with AI and mathematics. Following Darktrace’s acquisition of Cybersprint, a second R&D center is operational in The Hague, The Netherlands.
The AI Research Centre has produced numerous award-winning breakthroughs, which have gone on to form the AI capabilities that today comprise our products contained in the Darktrace Cyber AI Loop. Please see below a selection of research abstracts stemming from these research initiatives.
28%
R&D Headcount Growth in FY22
20+
Doctorates
~100
Masters
Featured Research
Innovating Cyber Recovery - Key to Cyber Resilience
Read our second Darktrace Discourse paper, authored by John C. Allen, MBA, CRISC, and VP Cyber Risk & Compliance, Darktrace. This paper reviews the current state of incident recovery and sets forth seven areas of innovation to improve Recovery from cyber incidents, based on a core capability: Real-Time Adaptive Incident Response.
Attack Path Modeling Research
This paper outlines the Attack Path modeling (APM) module of Darktrace PREVENT, exploring how real-time, automated, dual-aspect, multi-data-source, end-to-end APM can be used to give blue teams a comprehensive view of realistic, risk-prioritized attack paths so that resources can be best allocated to defend key assets.
Prevent: Security through Adversity
Read our Darktrace Discourse paper, authored by one of our researchers based at the Centre. The author has seven years’ experience in the automation of complex cyber-centric processes with specialization in the offensive domain, and holds a PhD in Astrophysics from the University of Cambridge.
Research
Identification of cryptomining credentials and their use in differentiating between insider threats and wide-spread malware
How global analysis of cryptomining credentials can identify malicious criminal organizations leveraging corporate computing power for financial gain.
Researchers
Dr. Andrew Woodford
Read more
Identifying compromises with graphs of high-precision security metadata
Using directed graphs of lower-level security events to reliably discover complex security incidents.
Researchers
Dr. Tim Bazalgette
Dr. Dickon Humphrey
Read more
Researchers
Bachelor of Science
Stanford University
Specializes in electrical engineering including analog circuit design and organic semiconductors.
PhD in Astrophysics
University of Cambridge
Specializes in general automation of complex, typically human-driven processes with current focus on adversarially-oriented preventative security.
PhD in Mathematics and Physics
University of Cambridge
Specializes in applications of machine learning and statistical analysis, clustering techniques and the use of neural networks for the classification of categorical data.
PhD in Biochemistry
University of Cambridge
Specializes in neuroscience and behaviour modeling, machine learning to identify novel threats, and automated healing of computer systems following a digital compromise.
Master’s in Mathematics
Durham University
Specializes in mathematical biology and mathematical modeling, numerical analysis and adversarial attack simulations.
PhD in Statistics
Durham University
Specializes in non-parametric modeling, statistical and optimization problems, dimension reduction and classification problems.
Master’s in Physics
University of Cambridge
Specializes in the application of nuclear magnetic resonance to manufacturing processes, and the application of machine learning to galaxy clustering.
Master’s in Engineering
University of Oxford
Specializes in applications of machine learning techniques in bioengineering, applying neural networks to extract structures in medical imagery, and investigating novel phishing techniques.
PhD in Theoretical Linguistics
University of Cambridge
Specializes in algorithmic modeling of cognitive representations and acquisition of natural language syntax.
PhD in Cosmology
University of Cantabria
Specializes in developing novel advanced statistical techniques in data analysis and software engineering for supercomputers.
PhD in Mathematics
University of Bath
Specializes in applications of AI to endpoint and cloud environments and network traffic analysis.
PhD in Mathematics
University of York
Specializes in mathematical physics, anomaly detection in complex data to improve efficiency, and optimization and stability in data processing.