You’ve likely seen cybersecurity companies tossing around buzzwords related to machine learning, artificial intelligence, and data science in recent years with increasing frequency. And for good reason. ML models, if designed with care, can provide solutions to problems that are too complex or time consuming for humans to manage alone. They can speed up processes and augment product offerings in impressive ways. Unfortunately many organizations miss the mark, blinded by the buzzword but lacking the experience, intention, or understanding to generate high value with machine learning. The simple fact is that ML sells. It grabs the attention of customers, investors, and the public, even if an organization’s use of ML isn’t elevating their product or, in worst case scenarios, is creating dangerous blind spots. As a seasoned data scientist who’s passionate about building ML models for the security space, this drives me crazy. In this talk, I’ll be breaking down the information asymmetry that exists between ML experts and the rest of the security world. If you’re considering how to implement ML in your product, curious what the data scientists within your organization are spending their day doing, are looking for ways to weed through the buzzwords when vetting a new security product, or somewhere in between, join me to learn how to generate high value with machine learning.