Thought Leaders in Cloud Computing: Evident.io CEO Tim Prendergast (Part 4)

Tim Prendergast: Companies are drowning in data nowadays. You see this heavy push into machine learning and AI to try and rationalize this data. The goal is correct. However, I think the approach is slightly off. New entrepreneurs are looking at this problem of data overload. We need to approach it from a methodology where we say, “Humans are the curators of the data but are not the decision makers.” We should find ways where we can augment small to medium sized teams with technology to help them answer deep questions about this data without having to have large engineering forces or large data science teams.

From a security-specific view, your typical team 10 years ago saw less than 10% of the security alert and risk issues that they receive today. Business are rapidly changing and there’s so much more technology and infrastructure being deployed by organizations nowadays that you have this overwhelming mass of telemetry to process. In security, the one thing we know is humans are not fundamentally well-suited to processing large data sets individually.

We need to step back from the differentiation of how good the people you hire to process the data are and start building more systems and platforms out there that can be consumed. In a way, we are looking at a one-time or utility-based model to help businesses rationalize all this data that they have and derive the handful of actionable insights they need to take away from that data as opposed to only getting what they look for in the data.

Sramana Mitra: We also do a Thought Leaders in Cyber Security series. We’ve seen plenty of companies that are using AI kinds of techniques to deal with large volumes of data and extrapolating from that data actionable insights in the security domain. This is a very active field of entrepreneurship at the moment.

Tim Prendergast: We still haven’t broken free of the human-machine relationship in that domain. The thing that would be really interesting is to find a way to decouple the experts who can come in and define the algorithms and define the training models and things like that from the systems that it can actually then take data as an API input, and output can be a series of actionable items based on the algorithm that it determined would run best.

Sramana Mitra: Terrific. Thank you for your time.

This segment is part 4 in the series : Thought Leaders in Cloud Computing: Evident.io CEO Tim Prendergast
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