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  • Resumen

  • Welcome to Uncovering Hidden Risks, a broader set of podcasts focused on identifying the various risks organizations face as they navigate the internal and external requirements they must comply with.   We’ll take you through a journey on insider risks to uncover some of the hidden security threats that Microsoft and organizations across the world are facing.  We will bring to surface some best-in-class technology and processes to help you protect your organization and employees from risks from trusted insiders.  All in an open discussion with topnotch industry experts!
    2020 Microsoft Corporation. All rights reserved.
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Episodios
  • Episode 8: Class is in session

    Episode 8: Class is in session
    May 26 2021
    When Professor Kathleen Carley of Carnegie Mellon University agreed to talk with us about network analysis and its impact on insider risks, we scooched our chairs a little closer to our screens and leaned right in. In this episode of Uncovering Hidden Risks, Liz Willets and Christophe Fiessinger get schooled by Professor Carley about the history of Network Analysis and how social and dynamic networks affect the way that people interact with each other, exchange information and even manage social discord. 0:00 Welcome and recap of   1:30 Meet our guest: Kathleen Carley, Professor at Carnegie Mellon University; Director of Computational Analysis & Social and Organizational Systems; and Director of Ideas for Informed Democracy and Social Cybersecurity 3:00 Setting the story: Understanding Network Analysis and its impact on company silos, insider threats, counter terrorism and social media. 5:00 The science of social networks: how formal and informal relationships contribute to the spread of information and insider risks 7:00 The influence of dynamic networks: how locations, people and beliefs impact behavior and shape predictive analytics 13:30 Feelings vs Facts:  Using sentiment analysis to identify positive or negative sentiments via text 19:41 Calming the crowd: How social networks and secondary actors can stave off social unrest 22:00 Building a sentiment model from scratch: understanding the challenges and ethics of identifying offensive language and insider threats 26:00 Getting granular: how to differentiate between more subtle sentiments such as anger, disgust and disappointment 28:15 Staying Relevant: the challenge of building training sets and ML models that stay current with social and language trends.   Liz Willets: Well, hi, everyone. Uh, welcome back to our podcast series Uncovering Hidden Risks, um, our podcast where we uncover insights from the latest trends, um, in the news and in research through conversations with some of the experts in the insider risk space. Um, so, my name's Liz Willets, and I'm here with my cohost, Christophe Fiessinger, to dis- just discuss and deep dive on some interesting topics.             Um, so, Christophe, can you believe we're already on Episode 3? (laughs) Christophe Fiessinger: No, and so much to talk about, and I'm just super excited about this episode today and, and our guest. Liz Willets: Awesome. Yeah, no. I'm super excited. Um, quickly though, let's recap last week. Um, you know, we spoke with Christian Rudnick. He's from our Data Science, um, and Research team at Microsoft and really got his perspective, uh, a little bit more on the machining learning side of things. Um, so, you know, we talked about all the various signals, languages, um, content types, whether that's image, text that we're really using ML to intelligently detect inappropriate communications. You know, we talked about how the keyword and lexicon approach just won't cut it, um, and, and kind of the value of machine learning there. Um, and then, ultimately, you know, just how to get a signal out of all of the noise, um, so super interesting, um, topic.             And I think today, we're gonna kind of change gears a bit. I'm really excited to have Kathleen Carley here. Uh, she's a professor across many disciplines at Carnigen Melligan, Carnegie Mellon University, um, you know, focused with your research around network analysis and computational social theory. Um, so, so, welcome, uh, Kathleen. Uh, we're super excited to have you here and, and would love to just hear a little bit about your background and really how you got into this space. Professor Kathleen Carley: So, um, hello, Liz and Christophe, and I'm, I'm really thrilled to be here and excited to talk to you. So, I'm a professor at Carnegie Mellon, and I'm also the director there of two different, uh, centers. One is Computational Analysis of Social and Organizational Systems, which is, you know, it brings computer science and social science together to look at everything from terrorism to insider threat to how to design your next organization. And then, I'm also the director of a new center that we just set up called IDeaS for Informed Democracy and Social Cybersecurity, which is all about disinformation, uh, hate speech, and extremism online. Liz Willets: Wow. Professor Kathleen Carley: Awesome. Liz Willets: Sounds like you're (laughs) definitely gonna run the gamut over there (laughs) at, uh, CMU. Um, that's great to hear and definitely would love, um, especially for the listeners and even for my own edification to kinda double-click on that network analysis piece, um, and l- learn a little bit more about what that is and kind of how it's developed over the past, um, couple years. Professor Kathleen Carley: So, network analysis is the scientific field that actually started before World War II, and it's all about connecting things. And it's the idea that when you have a set of things, the way ...
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    32 m
  • Episode 7: Say what you mean!

    Episode 7: Say what you mean!
    May 26 2021
    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 ...
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    29 m
  • Episode 6: Cracking down on communication risks

    Episode 6: Cracking down on communication risks
    May 26 2021
    Words matter. Intent Matters.  And yes, most certainly, punctuation matters.  Don’t believe us? Just ask the person who spent the past five-minutes eating a sleeve of cookies reflecting on which emotion “Sarah” was trying to convey when she ended her email with, “Thanks.” In this episode of Uncovering Hidden Risks, Raman Kalyan, Talhah Mir and new hosts Liz Willets and Christophe Fiessinger come together to examine the awesomely complex and cutting-edge world of sentiment analysis and insider risks. From work comm to school chatter to social memes, our clever experts reveal how the manifestation of “risky” behavior can be detected.   0:00 Hello!: Meet your new Uncovering Hidden Risks hosts 2:00 Setting the story: The types and underlying risks of company communication 6:50 The trouble with identifying troublemakers: the link between code of conduct violations, sentiment analysis and risky behavior 10:00 Getting the full context: The importance of identifying questionable behavior across multiple platforms using language detection, pattern matching and AI 16:30 Illustrating your point: how memes and Giphy’s contribute to the conversation 19:30 Kids say the darndest things: the complexity of language choices within the education system 22:00 Words hurt: how toxic language erodes company culture 26:45 From their lips to our ears: customers stories about how communications have impacted culture, policy and perception Raman Kalyan: Hi everyone. My name is Raman Kalyan, I'm on the Microsoft 365 product marketing team, and I focus on insider risk management from Microsoft. I'm here today, joined by my colleagues, Talhah Mir, Liz Willetts, and Christophe Eisinger. And we are excited to talk to you about hidden risks within your organization. Hello? We're back, man. Talhah Mir: Yeah, we're back, man. It was super exciting, we got through a series of a, a couple of different podcasts, three great interviews, uh, span over multiple podcasts and just an amazing, amazing reaction to that, amazing conversations. I think we certainly learned a lot. Raman Kalyan: Mm-hmm (affirmative). I, I learned a lot. I mean, having Don Capelli on the podcast was awesome, talked about different types of insider risks, and what I'm most excited about today, Talhah, is to have Liz and Christophe on the, on the show with us 'cause we're gonna talk about communication risk. Talhah Mir: Yeah, super exciting. It's a key piece for us to better understand sort of sentiment of a customer, but I think it's important to kind of understand that on its own, there's a lot of interesting risks that you can identify, uh, that are completely sort of outside of the purview of typical solutions that customers think about. So really excited about this conversation today. Raman Kalyan: Absolutely. Liz, Christophe, welcome. We'd love to take an opportunity to have you guys, uh, introduce yourselves. Liz Willetts: Awesome, yeah, thanks for having us. We're excited to kind of take the reins from you all and, and kick off our own, uh, version of our podcast, but yeah, I'm, I'm Liz Willetts. I am the product marketing manager on our compliance marketing team and work closely with y'all as well as Christophe on the PM side. Christophe Eisinger: Awesome. Christophe. Hello everyone, I'm, uh, Christophe Eisinger and similar to Carla, I'm on the engineering team focusing on our insider risk, um, solution stack. Raman Kalyan: Cool. So there's a, there's a ton, breadth of communications out there. Liz, can you expand upon the different types of communications that organizations are using within their, uh, company to, to communicate? Liz Willetts: Yeah, definitely. Um, and you know kind of as we typically think about insider risks, you know, there's a perception around the fact that it's used, um, and related to things like stealing information or, um, you know, IP, sharing confidential information across the company, um, but in addition to some of those actions that they're taking, organizations really need to think about, you know, what might put the company, the brand, the reputation at risk. And so when you think about the communication platforms, um, you know, I think we're really looking to collaboration platforms, especially in this remote work environment- Raman Kalyan: Hmm. Liz Willetts: ... where employees, you know, have to have the tools to be enabled to do their best work at home. Um, so that's, you know, Teams, uh, Slack, Zoom, um, but then also, you know, just other forms of communication. Um, we're thinking about audio, video, um, those types of things to identify where there might be risks and, and how you can help an organization remediate what some of those risks might be. Raman Kalyan: Awesome. And Christophe, as we think about communications risk more broadly, what kind of threats do you... have you start seeing, um, organizations being more concerned about? Christophe Eisinger: Yeah, so exactly to what you just mentioned ...
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    33 m
Economía Política y Gobierno
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