Welcome to Ask the Experts, brought to you by CloudServicesUniversity.com. In this video, Intelisys’ SVP Cloud Transformation Andrew Pryfogle discusses how companies are leveraging big data to make accurate and predictive calculations around security threats with Masergy’s Andy Singleton. Learn more about cloud-based security solutions from the Masergy team here: https://cloudservicesuniversity.com/supplier-directory/masergy/
| Andrew: | Well, welcome back again to another Ask the Experts session. We love these, we get great feedback from our students out there in the world that are learning great, great pearls of wisdom from these Ask the Experts sessions. Back to the studio again, Andy Singleton, who is the Director of Security Solutions Engineering for Masergy. A really compelling provider of cloud-based security solutions for our customers. Andy, welcome back, man. |
| Andy: | Thank you, it’s good to be here. |
| Andrew: | I want to talk about something pretty interesting in the world of cloud-based security. It has to do with big data. I know that there are tons of data points that we’re now collecting on, right? It’s not just computers and laptops. It’s the explosion around mobility; it’s the explosion around the Internet of Things. I’m curious how Masergy and others perhaps are leveraging big data technologies to look at all those data points to make more accurate and predictive calculations around security threats. Talk about that. |
| Andy: | Absolutely. Big data has become a concern for a lot of companies out there, whether you’re a small manufacturer or pharmaceutical, or even a banking type customer. Data is money for these companies. How do I ingest that data? How do I get meaningful information out of it that will help my business? At the same time, a lot of this data can be used and should be used for information security, cybersecurity, analytics, and really understanding trending of this data as it comes into their systems. Understanding that the trends around machine spend or machine usage, those sorts of things. Or at the same time understand if there’s something malicious being injected in there because it is a point of threat that can be leveraged by cybercriminals. If I can take and manipulate that data coming into our company, I can really upset the applecart, so to speak, with these companies on what they’re analyzing. Understanding those trends, but also being able to ingest all that data, is immensely important. |
| You take a pharmaceutical-type company where you’re understanding, “How much did I manufacture today? How much human resources am I using? Are those being used appropriately?” Then flip that around to security. Are things operating the way they should? Am I predicting their behavior over time? Am I understanding that data? To do this, it takes really machine learning. We’re talking about artificial intelligence, machine learning, kind of the next phase in the way data is analyzed. You take a lot of the same principles, a lot of the technologies that are around. | |
| As an example: credit card theft. If you ever get your credit card stolen or skimmed. All of us have at one time. You get that alert from, say, American Express who says, “Hey, are you trying to charge $1000 in California and another $1000 at London right now?” Probably not. They end up with kind of the same technologies as we are adhering to as well. It’s a lot about machine learning and pattern recognition. | |
| Andrew: | Wow. That’s some Star Wars stuff, man. That’s pretty cool. We’ve had to get really smart about this, right? The criminal element is definitely getting smart about this. Am I right? |
| Andy: | Absolutely. They’re adapting. If you look at what the criminals are doing now, I mean it’s not just one weakness or one vulnerability that they’re leveraging. There’s actually kits out there, right? As a criminal, I can go buy a whole kit that may leverage an Adobe vulnerability. It may leverage an IE vulnerability. It may leverage some other vulnerability in the system and maybe one of them hits. |
| Andrew: | Yeah. |
| Andy: | If you’re not looking at all those inputs—and this goes for the Internet of Things—as you have hop off points, potentially, from these maybe sensors or other technologies out there that are inputting data into a general system. If I can find a leverage point of that, that’s a point of weakness. You’ve got to be looking at all this traffic as it passes in and out of your network of your business and really pattern match that over a long period of time. |
| Andrew: | Is it as if there’s fingerprints on this all the way through at all these different points that artificial intelligence is looking for? Are there certain signatures that, for lack of a better word, that they’re looking for to determine if those patterns exist and that there’s a threat impending? |
| Andy: | That’s part of it. |
| Andrew: | Okay. |
| Andy: | See, you’ve got to have a part of it where it is looking for certain pattern matches, but you’ve also got to have a part of it where the machine just learns. It learns trending and that’s … And it learns kind of via an AI model what is normal? What is the normal operating behavior of these data flows over a long period of time? Not only do you want to pick up the easily skimmed off stuff, such as signature type threats, but also the unstructured data. The methods of these criminals that get into business now are really not to set off signature alerts, not to set off log alerts, but to very stealthily sneak in and bypass all of that and use some system as a hop-off point. You’ve got to be trending that in a manner where you can really understand normalization over a long period of time. |
| Andrew: | Andy, I think you’re describing exactly why I’m losing in fantasy football this year. |
| Andy: | Exactly. |
| Andrew: | They say all these data scientists are using big data to win fantasy football. That’s just wrong. It’s very, very wrong. Can you help me with that? |
| Andy: | Oh yeah, I agree, agree. You take all the inputs of possible scenarios and you input it into it and mangle up all that data, as our chief scientist would say, he’s a data mangler. You take all that unstructured information in there and you got a machine that can say, “Oh, well because of this, this team’s going to win.” I guess who can afford … The top dogs that can afford those systems … They probably produce pretty good results. |
| Andrew: | No doubt. They’re whooping my butt this year. Well, hey, there you go. That’s a great analogy because whether it be something as harmless as fantasy football, or as egregious as hurting what you call this, which is data is money, right? I mean, that’s a very, very big deal and that can be extremely destructive to a company. Andy, great, great stuff, man. Thanks again for jumping in. |
| Andy: | Absolutely, glad to be here. Thank you. |
| Andrew: | Very cool. Guys, that’s Andy Singleton. He is the Director of Security Solutions Engineering for Masergy, one of our go-to providers in the cloud for cloud-based security solutions. Really impressed with their story. Check out their learning center here at the University and get smart about how they can help you design a holistic unified security solution and strategy for your client. You can win big in the cloud with Masergy. Good selling. |
