Noble Vision case study – financial services
Posted on March 9, 2020 by Noble
A global financial broker, offering financial services for institutional clients, including hedge funds, asset managers and the largest investment banks.
The financial services industry is under continuous attack. As reported by the Financial Conduct Authority, there was a tenfold increase in the reported cybersecurity incidents reported during 2018 in the sector just in the UK, and it is expected to be maintained during 2019. Being holders mainly of financial and personal data, makes them the prime target for cyber-criminals with the potential to cause a substantial damage to their business.
The ubiquity of the attacks and the increase in their sophistication made this organization assume no system could be trusted and that a continuous automated trust assessment was required.
After a full upgrade of their security infrastructure, the company decided to evaluate Noble Vision to identify gaps in their security platform and address any recognized risks. Noble Vision unbiased methodology reduces the security gap by learning and summarizing the activities across a company’s entire network and using this to detect and highlight unexpected behavior. Noble Vision is powered by advanced Deep Learning, that never tires, never stops searching the network, and never stops learning.
“The information provided was exactly what we needed. Thanks to Noble Vision I can positively state that our company is now more secure.”
Once the training phase was completed, Noble Vision started identifying anomalies in their activities.
Due to the unbiased nature of Noble Vision’s unsupervised deep learning algorithms, the detected anomalies helped identify several risky situations that had gone completely undetected by their existing security solutions.
Having a digital infrastructure that has grown organically as the business required, it is very common to have a co-existence of new systems and legacy ones.
Noble Vision was able to pinpoint with great accuracy activities that were generated by legacy systems no longer in use in the business.
The results generated also helped identifying IT practices that could lead to security issues and to identify dormant threats.
The organization was able to optimize both their business systems and IT operations to notably close security gaps that were not detected by any of the existing security tools.