Agenda

Presentations already confirmed include:


► Mallet against machine: regulating the AI landscape

Subhajit Basu, Associate Professor Cyber Law, University of Leeds 

  • In order to make automation effective, feeding a vast amount of data is necessary. How do you ensure that this data is used in a secure way and for the right purposes?
  • What are the legal implications if a machine learning platform gets hacked?
  • The connected landscape needs greater regulation. But how do you implement this, especially when there is a stigma that ‘lawyers don’t understand technology and technology professionals don’t understand the law!

► Innovation: Information security’s worst nightmare?

Adam Hembury, Director of Innovation, DLA Piper

  • Where do innovation and Information Security collide?                 
  • The strategic data questions we face:   

 - Client confidentiality

 - Data ownership

 - Data Analytics

 - Artificial Intelligence - Machine Learning

 - Enterprise search

 - Automation

  • Case study: innovation and its application at DLA Piper

► Machine vs. machine. How AI is changing how we tackle cyber-threats

Noura Al Moubayed, Assistant Professor in the department of Computer Science, Durham University

  • How artificial intelligence is impacting the cyber-threat landscape
  • How can AI and automation help the process of cyber threat detection and prevention
  • Collaboration between academia and law enforcement

► Get your head out of the Clouds: clear truths on how AI can help Cloud security

Stephen McGough, Senior Lecturer, Data Science, Newcastle University

  • How can AI help secure the Cloud?  Case study on how machine learning algorithms can be used in detecting fraudulent behaviour.
  • How AI can help in the efforts against ransomware. How machine learning algorithms can be used to profile both the victims and the perpetrators of ransomware attacks
  • The need for greater collaboration between industry, private sector and law enforcement if we are to secure connected networks and tackle cyber-criminals who are also using machine learning.

►Automate to authenticate. AI as a key player in authentication

Stefano V. Albrecht, Assistant Professor, Artificial Intelligence, School of Informatics at The University of Edinburgh

  • The new Centre of Excellence in Cyber Security Research at Edinburgh University: research and collaboration opportunities
  • Rethinking remote authentication: a new approach based on interaction between intelligent autonomous agents
  • The DARPA Spectrum Collaboration Challenge: designing wireless networks as intelligent collaborative systems and associated security risks

 


► HR Analytics and the 'Insider Threat' Detection 

John Bishop, Professor of Cognitive Computing, Goldsmiths University 

  • Insider threat: why is it still such a problem and can AI help? 
  • Case study: insights into how to use HR Analytics to analyse and assess user behaviour and aid in the cyber-threat detection effort
  • Can AI be used to detect a potential insider threat before it happens?

► Placing your bets on Machine Learning: Now core capability for all functions 

Finbarr Joy, Group CTO, Superbet

  • Formerly a niche / edge specialisation, ML has become core to how all software is built 
  • Embedding ML as a core function ensures security considerations can be better integrated than ever before  
  • Looking at strategy and implementation scenarios for ML-driven initiatives in support of security  

 


► AI: Reasons to Just Say No 

James McKinlay, Chief Information Security Officer, Barbican Insurance Group 

  • The AI bandwagon. And the risks of jumping on it 
  • When every vendor is claiming AI expertise, how do you differentiate?
  • What could/should you be doing instead? 

► CNI AI: the Impact of Machine Learning on Data Protection and Management in Critical National Infrastructure 

Peter Jackson, Chief Data Officer, Southern Water

  • What is the real business case for implementing machine learning? Do the benefits in business efficiency outweigh the risks and costs?
  • The critical real world consequences when machine learning goes wrong
  • AI and data protection. In order for a machine learning platform to be truly effective, a huge amount of data needs to input. How do you manage, regulate and secure this data?