With all the buzz about "AI", perhaps it's time to take a step back and look at more than just the marketing spin and ChatGPT chatter. Join us for a deeper dive into how Machine Learning impacts cybersecurity, both from the offensive and defensive perspectives. How hard is it really to train these models and how are the custom ones being used to help and harm us. This one is going to get a bit more technical with the goal of making AI seem a bit less like the magic mystery box many still make it out to be. We'll round it all out with a review of AI related threats, defenses, cautions, and predictions.
 

ScottBaker

Scott Baker

Manager, Sensitive Research, University of British Columbia

Scott Baker is the Manager, Sensitive Research at UBC Advanced Research Computing. He leads a group of privacy and security professionals focused on helping researchers find balanced solutions for their research projects. He is a member of the Digital Research Alliance of Canada national security council, and has over 25 years of experience managing people, projects, and IT systems.

Summit Speaker

Kevin Radford

Cybersecurity Analyst II - Threat Intelligence, University of British Columbia

Kevin Radford is a graduate of UBC's Master of Data Science Program and works as the resident Data Scientist and Machine Learning Subject Matter Expert within UBC's Cybersecurity teams.

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Slide Deck

Technology Track

Session Format
Speaker Presentation (45 mins)