Polystream - ‘Cloud in a box’
We are working with Polystream to develop its core processes to produce a new, ‘Cloud in a box’ capability for the Defence and Security Industry.
Polystream overcomes the issue of having to use graphics processing units (GPUs) in the cloud, enabling a revolutionary development in the delivery of simulations and synthetic environments.
By moving the cost and overhead of application deployment and management entirely into the cloud. Polystream enables training rooms to use client devices such as laptops and small PCs. Training rooms themselves can now be virtualized, allowing users to access synthetic environments from wherever they are located.
The ‘Cloud in a box’ gives users control of where and how they access cloud architectures.
The Cloud in a Box and all of Polystream’s supporting technology is built entirely using lower cost Commercial Off The Shelf (COTS) components, both in the cloud and at the point of need. This makes them cheaper to build and maintain. This is a powerful development for how and where training providers can access synthetic environments and greatly increases the computational power available to deliver these environments through the ‘Intelligent Cloud’.
Elemendar - Cyber Threat Intelligence
Elemendar was founded in 2017 by two graduates of the UK’s first GCHQ / NCSC Cyber Accelerator, powered by Wayra UK, to develop cyber threat intelligence (CTI) enrichment capabilities.
Elemendar’s AI analyst turns intelligence reports - written for humans by humans - into machine-readable cyber threat information, through leading-edge Machine Learning (ML) Natural Language Processing (NLP) technologies.
Development wise, we now have a live demo for you to experiment with on our site, so that you can see for yourself how the natural language engine at the heart of Elemendar's AI analyst works. Simply head to demo.elemendar.com and enter the URL of a cyber threat intel report. We'll turn it into a STIX bundle and visualise it for you.
Data-Led Futures (DLF)
We have pioneered and trialled a range of approaches to make Futures and foresight analysis more data led. Through our ‘Data-Led Futures’ Programme we have compiled a range of techniques and sources derived from data science processes.
We have a background in supporting the analysis that underpins the Global Strategic Trends programme. Our ‘scan of scans’ helped understand key strategic trends and outliers in a large volume of foresight data.
We are currently leading a trial that uses ‘Futurescaper’ to model strategic trends and tests them with experts and policy makers through a crowdsource. The results of this activity then form the basis of a ‘Choose your own future’ exercise, through which users choose particular outcomes and policy choices to better understand how their actions could influence future trends.
With our partners, we are also exploring how to extract quantitative data at scale to implement a data driven ‘horizon scanning’ processes that changes how foresight analysis is conducted - changing the focus from scenario based discussion exercises to quantitative processes that make analysis more subject to probabilistic analysis and testing.