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RPC's Cyber Team Peer-Reviews Leading AI Research Paper

Published on 09 December 2020

A paper on emerging cyber threats was peer-reviewed by RPC's cyber law team and published in an international legal journal.

The team, led by Richard Breavington and Daniel Guilfoyle, contributed to cutting-edge cyber-security research in order to provide advanced warning of the emerging cyber-security risks that businesses and organisations might face in the predictable future as technology improves. Alex Matheson was invited to conduct the peer-review of the paper on the legal and cyber-security implications of advances in 'adversarial machine learning'. 

Commenting on the peer-review, Alex added:

"'Adversarial machine learning' is a fast-developing sub-field of the field of artificial intelligence (AI) with the twist that it can exploit the technology for malicious outcomes. It is foreseeable that such advances in AI technology may be adapted to power the viruses and ransomware of the future."  

Following peer-review, the paper has been published in the Legal Issues Journal (2020, Vol 8, Issue 2, 75-98) to benefit other AI and cyber-security researchers and lawyers in the field. The peer-review process allows the work of one expert to be anonymously and objectively stress-tested by another expert in line with the state-of-the-art of knowledge of a field at the time of review.  The original author's name was anonymised by the journal for the review and prior to publishing but has since been revealed, upon publication, to be Dr Peter Stephenson – a leading cyber-security law researcher. His contribution to the field in this paper is titled 'Adversarial Machine Learning: The Coming Legal Storm'. 

The paper not only provides a robust research basis for methods of understanding and addressing bleeding-edge cyber-security risks – such as the risk of self-directed ransomware (like self-driving cars), but reflects upon the researcher's findings that there is currently a lack of real cyber expertise amongst the legal profession generally.