Oh my gosh Oh my gosh, I’m dying. Oh my gosh, I’m dying. That’s so funny! And in just three short lines our emotions boomeranged from intrigue, to panic, to intrigue again…and that illustrates the all-important concept of context! In this episode of Uncovering Hidden Risks, Liz Willets and Christophe Fiessinger sit down with Senior Data Scientist, Christian Rudnick to discuss how Machine Learning and sentiment analysis are helping to unearth the newest variants of insider risks across peer networks, pictures and even global languages. 0:00 Welcome and recap of 1:25 Meet our guest: Christian Rudnick, Senior Data Scientist, Microsoft Data Science and Research Team 2:00 Setting the story: Unpacking Machine Learning, sentiment analysis and the evolution of each 4:50 The canary in the coal mine: how machine learning detects unknown insider risks 9:35 Establishing intent: creating a machine learning model that understands the sentiment and intent of words 13:30 Steadying a moving target: how to improve your models and outcomes via feedback loops 19:00 A picture is worth a thousand words: how to prevent users from bypassing risk detection via Giphy’s and memes 23:30 Training for the future: the next big thing in machine learning, sentiment analysis and multi-language models Liz Willets: Hi everyone. Welcome back to our podcast series, Uncovering Hidden Risks. Um, our podcasts, where we cover insights from the latest in news and research through conversations with thought leaders in the insider risk space. My name is Liz Willets and I'm joined here today by my cohost Christophe Feissinger, um, to discuss some really interesting topics in the insider risks space. Um, so Christophe, um, you know, I know we spoke last week with Raman Kalyan and Talhah Mir, um, our crew from the insider risk space, just around, you know, insider risks that pose a threat to organizations, um, you know, all the various platforms, um, that bring in signals and indicators, um, and really what corporations need to think about when triaging or remediating some of those risks in their workflow. So I don't know about you, but I thought that was a pretty fascinating conversation. Christophe Feissinger: No, that was definitely top of mine and, and definitely an exciting topic to talk about that's rapidly evolving. So definitely something we're pretty passionate to talk about. Liz Willets: Awesome. And yeah, I, I know today I'm, I'm super excited, uh, about today's guests and just kind of uncovering, uh, more about insider risk from a machine learning and data science perspective. Um, so joining us is [Christian redneck 00:01:24], uh, senior data scientist on our security, uh, compliance and identity research team. So Christian welcome. Uh, why don't you- Christian Redneck: Thank you. Liz Willets: ... uh, just tell us a little bit about yourself and how you came into your role at Microsoft? Christian Redneck: Uh, yeah. Hey, I'm Christian. Uh, I work in a compliance research team and while I just kinda slipped into it, uh, we used to be the compliance research and email security team, and then even security moved to another team. So we were all forced to the complaints role, uh, but at the end of the day, you know, it's just machine learning. So it's not much of a difference. Liz Willets: Awesome. And yeah, um, you know, I know machine learning and and sentiment analysis are big topics to unpack. Um, why don't you just tell us a little bit since you've worked so long in kinda the machine learning space around, you know, how, how that has changed over the years, um, as well as some of the newer trends that you're seeing related to machine learning and sentiment analysis? Christian Redneck: Yeah. In, in our space, the most significant progress that we've seen in the past year, was as moving towards more complex models. The more complex models and also more complex way of analyzing the task. So if you look at the models that were very common, about 10 years ago, they basically would just look at words, it's like, uh, a set of words. Uh, so the order of words don't matter at all and that's changed. The modern algorithms, they will look at sen- sentences as a secret before and they will actually think the order of the words into account when they run analysis. The size of models has also increased dramatically over the years. So for example, I mentioned earlier that I've worked the email security at the [monastery 00:03:04] that we had shipped. They were often in the magnitude of kilobytes versus like really modern techniques to analyze the pensive language. They use deep neural nets and the models they can be the sizes of various gigabytes. Christophe Feissinger: What's driving that evolution of the models. Uh, you know, I'm assuming a, a big challenges to, uh, or a big goal is to make those model better and better to really re- reduce the noise and things like false positives or, or misses. Is that what's driving some of those ...
