Dr Paul Lewis Chief Technology Officer Real World Cyber Security Use Cases for Machine Learning and Artificial Intelligence within Financial Services

CREATING CYBER SECURITY PRODUCTS TECHNOLOGY TRANSFER ARTIFICIAL INTELLIGENCE DDoS BLOCKCHAIN ENCRYPTION EARLY WARNING OF INTRUSION IoT & SMART CITIES DIGITAL FORENSICS ANALYTICS STEGANOGRAPHY CYBER RISK/THREAT ASSESSMENT COMPUTATION ON ENCRYPTED DATA SOCIAL ENGINEERING PEN TESTING CYBER HORIZON SCANNING TECHNOLOGY TRANSFER CLOSE ASSOCIATION WITH ADVANCED CYBER SECURITY RESEARCH UNIVERSITIES

The Problem Around 2-3 years ago we decided to start working on our first ML enabled cyber product (specifically Ant-DDoS) However lots of companies and organisations fall somewhere in the following categories: Know about Machine Learning and AI techniques, and are using or attempting to use them (notable examples are DWP, KPMG Deepmind, Babylon Health etc.). Heard about ML/AI techniques, but have no idea where to start. Have no idea, but watched Terminator 2 and are worried!

Why Are People Interested? Most organisations now understand the advantage in exploiting their cyber data, however are grappling with the ’four V’s’.

What Are We Doing About this? Decided to run a series of workshops to work on the ‘big’ problems in cyber security that ML techniques could be applied to. Four global organisations (Investment bank, Consumer Bank, Big 4 consultancy, Large Insurance) Three academic institutions (MIT, Imperial, Edinburgh) ….and us

Why Is Industry Interested? A recent trend that all cyber products have been dipped in, and are AI/ML enabled. Build products that are useful, relevant and actually rooted in proven ML research and techniques.

General Themes From Workshops Generic AI - This category includes ideas that should be seen as general problems in AI. They may need to be addressed as part of a solution emerging from other categories Intelligent defence - ideas that perform or assist defence activities which require a degree of automated discrimination or contextual awareness as a part of their performance. Situational awareness / decision support - aim to assist the analyst, and those with responsibility and accountability, to understand and navigate complexity in the context of risk, and operational and strategic decision-making. Fundamental design - desirable functions that might be ‘baked-in’ to the system or software to provide enhanced performance, Cyber AI context - Operational context of today’s enterprises in terms of supply chains, regulatory environments and responsibilities to staff, customers and citizens. Whole company services - links security context with the wider services to the enterprise that might be informed by the analytic processes.

Two Specifics – Standards & IP IP protection on vendors tools The idea here was to avoid being locked into a supplier of services by the provision of intellectual property to the supplier that could never be retrieved or forgotten. This could stretch to Models, Data Insight or other aspects. Porting data between vendors. Relating to the above is the requirement to be able to electronically port data between vendors. This might be a tool or a service – or both. 

Key Insights From the Workshops Companies are starting to seriously look at data and wonder what the hell am I going to do with it all. Most companies fundamentally misunderstand what Machine Learning and Artificial Intelligent techniques actually are and what they are capable of. Most companies can’t even patch out of date software, let alone use ‘advanced’ technology (See recent Equifax incident). Awareness of ML and AI is growing, and needs realistic assumptions and promises to be made. Lots of potential applications, and a real business need for them.

Challenge : Experimentation!

Thanks and Questions @XwordCTO paul.lewis@crosswordcybersecurity.com