WEBVTT 1 00:00:00.280 --> 00:00:09.880 Nineteen thirty two dot org. The information economy as a ride. The world 2 00:00:10.000 --> 00:00:16.160 is teeming with innovation as new business models reinvent every industry industry. Inside Analysis 3 00:00:16.280 --> 00:00:20.559 is your source of information and insight about how to make the most of this 4 00:00:20.760 --> 00:00:26.280 exciting new era. Learn more at inside analysis dot com, Insideanalysis dot com 5 00:00:26.280 --> 00:00:34.759 And now here's your host, Eric Kavanaugh. Well, ladies and gentlemen, 6 00:00:34.799 --> 00:00:38.439 Hello, and welcome back once again to the only coast to coast radio show 7 00:00:38.479 --> 00:00:42.039 all about the information economy. It's called Inside Analysis. Your host here, 8 00:00:42.439 --> 00:00:46.840 Eric Kavanaugh. Very special show today, folks. In fact, we just 9 00:00:46.920 --> 00:00:52.039 recorded a nice session with some slides. Hop online to inside analysis dot com 10 00:00:52.039 --> 00:00:55.799 and go to the webinar section to find where you can watch that show. 11 00:00:55.880 --> 00:01:00.719 Later on we'll put it on YouTube. But it's all about streaming graph at 12 00:01:00.719 --> 00:01:03.359 a company called that dot which has invented this technology. It came out of 13 00:01:03.400 --> 00:01:07.799 DARPA and we have Paige Roberts with us. Will also be hearing from Sanjif 14 00:01:07.799 --> 00:01:11.920 Mohan of Sanjmel formerly of Gartner. Now he's out independent so we can do 15 00:01:11.959 --> 00:01:15.680 cool stuff like this with us, which is awesome and a long time listener, 16 00:01:15.719 --> 00:01:19.120 first time caller h Alexander Husky is out there from Exxon Mobile. He 17 00:01:19.359 --> 00:01:25.120 is a real aficionado of technology and architectures and he always asks great questions and 18 00:01:25.159 --> 00:01:26.959 he's always challenging me on some of the things that we say in the show. 19 00:01:27.000 --> 00:01:30.040 So it's good to have him here in the mix as well. And 20 00:01:30.079 --> 00:01:33.840 with that, let's dive right in with Paige Roberts. So, Paige, 21 00:01:33.879 --> 00:01:38.120 first of all, congratulations on lining up with this company. Is absolutely fascinating. 22 00:01:38.159 --> 00:01:42.599 I think there are a whole host of use cases that simply cannot be 23 00:01:42.719 --> 00:01:48.280 reasonably addressed by existing technology, which you are uniquely positioned to tackle. So 24 00:01:48.319 --> 00:01:52.239 with that, give us a quick overview of that Dot and streaming graph. 25 00:01:52.280 --> 00:01:59.400 Sure, so that dot was, as you said, the technology of quine 26 00:01:59.439 --> 00:02:05.239 open so was developed by DARPA and then that DOT was developed as the as 27 00:02:05.280 --> 00:02:07.719 the company the commercial side of that. So that Dot streaming Graph and that 28 00:02:07.759 --> 00:02:15.879 dot Novelty are products, and they both are super useful for uh, finding 29 00:02:15.919 --> 00:02:23.400 really advanced threats in cybersecurity, and Novelty is especially useful for finding unknown unknowns, 30 00:02:23.479 --> 00:02:25.199 the things that you don't even know to look for. So you can 31 00:02:25.319 --> 00:02:30.919 just feed it your data and it has an AI built in to find whatever 32 00:02:30.039 --> 00:02:39.800 is an omalis in your data set as it flows in. Eric, I 33 00:02:39.840 --> 00:02:43.719 think you're talking about I'm not hereious, I'm I was on mute. Sorry 34 00:02:45.319 --> 00:02:49.400 there rarely do that, but it's okay. One of the very interesting use 35 00:02:49.439 --> 00:02:53.439 cases here, of course, is the fact that typically analytics is fixed, 36 00:02:53.520 --> 00:02:57.319 you have a structured query on set unfixed data. Well, streaming comes along 37 00:02:57.719 --> 00:03:01.400 and you could do persistent queries on the streaming data. Now, they've been 38 00:03:01.400 --> 00:03:05.080 streaming analytics solutions for a while, but maybe you could talk just per a 39 00:03:05.120 --> 00:03:08.840 minute about how that dot achieves this. So you have this slite up for 40 00:03:08.840 --> 00:03:15.840 our studio audience, but talk about how you have a persistent query on streaming 41 00:03:15.919 --> 00:03:21.479 data. But then that graph can alter over time, so new nodes can 42 00:03:21.520 --> 00:03:25.599 be sort of dynamically created as new information comes in. Can you talk about 43 00:03:25.599 --> 00:03:30.520 that a little bit? Sure? So the concept is you have data streaming 44 00:03:30.520 --> 00:03:36.759 in from multiple data sources, different Kafka topics or kinesis or pulse are Plus 45 00:03:36.759 --> 00:03:44.199 you're also probably got some context data coming in from some sort of batch capability 46 00:03:44.280 --> 00:03:47.680 like a file or whatever. And we have two things inside of that dot 47 00:03:47.800 --> 00:03:53.840 or colin that you do and that is first one is called an jest query. 48 00:03:54.199 --> 00:03:59.719 Now, in jest queries turn data into graphs. The idea in our 49 00:04:00.120 --> 00:04:03.800 where is that all nodes already exist, and what you were doing as the 50 00:04:03.879 --> 00:04:10.639 data comes in, you are using an extension of the cipher graph query language, 51 00:04:10.680 --> 00:04:15.719 which most people know that it worked with graph to tell it which pieces 52 00:04:15.759 --> 00:04:20.439 of data need to be, nodes need to be, properties need to be, 53 00:04:20.720 --> 00:04:26.439 edges, relationships, and as that data flows in, the graph becomes 54 00:04:26.480 --> 00:04:31.199 more and more rich, and it's a dynamic graph, so it changes over 55 00:04:31.240 --> 00:04:35.959 time as the information comes in. Now, the other thing that we do 56 00:04:36.120 --> 00:04:42.959 is called standing queries, and these are again just cipher query language, So 57 00:04:43.000 --> 00:04:47.399 it's anybody who's used NEO for j or any other graph capability is probably familiar 58 00:04:47.439 --> 00:04:51.759 with this language. But the concept is that you can query the past, 59 00:04:51.959 --> 00:04:56.959 the present, and even the future all at once. There is no time 60 00:04:57.000 --> 00:05:00.839 window, there's no definition of what time you're querying. So if a piece 61 00:05:00.879 --> 00:05:06.120 of data came in a year ago, and a piece of data comes in 62 00:05:06.160 --> 00:05:12.040 a microsecond ago, and then the final piece of the pattern you're looking for 63 00:05:12.160 --> 00:05:16.560 comes in next week, as soon as it comes in, it finds it. 64 00:05:16.560 --> 00:05:24.519 It finds that pattern within less than a couple of microseconds and immediately gives 65 00:05:24.560 --> 00:05:30.519 the answer and puts that into a new data flow like another Cafka topic. 66 00:05:30.759 --> 00:05:38.360 Or it can trigger actions within an application, which is really common. It 67 00:05:38.399 --> 00:05:42.480 can also go into a real time monitoring system and say, hey, you 68 00:05:42.800 --> 00:05:46.319 know, flag it. This is something important, this is what you were 69 00:05:46.360 --> 00:05:49.480 looking for. It can help with predictions, It can do all that sort 70 00:05:49.519 --> 00:05:55.360 of thing. So and of course the data is persisted in it's pluggable. 71 00:05:55.399 --> 00:06:01.800 You can decide what you would like anything that's Cassandra or Cassandra API compatible like 72 00:06:02.959 --> 00:06:09.279 rockstv SCYLIDB things like that, or ClickHouse or a couple of other things. 73 00:06:09.319 --> 00:06:14.399 So you can persist that what you need to recreate the graph in case you 74 00:06:14.519 --> 00:06:18.399 lose power or something, because it's all in memory or you know. It 75 00:06:18.480 --> 00:06:25.120 also persists the cool new insights that you've gotten, the places where you've actually 76 00:06:25.199 --> 00:06:29.040 found what you were looking for. So that's the cool idea, and you 77 00:06:29.439 --> 00:06:33.319 and you preserve state too. Write In other words, as the draph changes, 78 00:06:33.399 --> 00:06:36.319 you can still roll back in time and see what the world looked like 79 00:06:36.360 --> 00:06:40.040 at a particular point in time. Right, because if you change a schema 80 00:06:40.199 --> 00:06:43.560 typically you're changing the view of everything, but you can roll back and see 81 00:06:43.560 --> 00:06:46.040 what it was like to do some sort of comparison. Is that right? 82 00:06:46.439 --> 00:06:51.120 You can? There's also a really a big value to that because a lot 83 00:06:51.160 --> 00:06:59.399 of graph algorithms are designed to look at static data. So because you can 84 00:06:59.399 --> 00:07:02.279 take a snap shot from whatever time you wish, you can then use that 85 00:07:02.399 --> 00:07:08.680 to train your graph algorithm if you'd like, and then apply it to predict 86 00:07:08.920 --> 00:07:12.160 what you're what you're looking for. Yeah, this is just fascinating. Well, 87 00:07:12.199 --> 00:07:15.160 I'm going to be quiet for a while and bring Sanjiv Mohan into the 88 00:07:15.160 --> 00:07:17.120 picture so he can ask some questions. Look him up online, by the 89 00:07:17.160 --> 00:07:20.800 way, Sangmo Sanjeev. I'm fascinated by this stuff. What do you think? 90 00:07:20.839 --> 00:07:25.839 What are you seeing? Yeah? Eric, me too. In fact, 91 00:07:25.839 --> 00:07:30.399 it is ironic when you invited me to come up this show. The 92 00:07:30.000 --> 00:07:35.959 two topics streaming and graph were part of my podcast and blogs just in the 93 00:07:36.040 --> 00:07:41.720 last two weeks, and it's fascinating. In fact, in my document, 94 00:07:41.839 --> 00:07:46.040 without knowing too much about that Dot, I'd already mentioned this product, so 95 00:07:46.240 --> 00:07:51.879 it was quite quite a I guess this is this is that dot. This 96 00:07:53.040 --> 00:08:00.120 is like Paige and I've already connected in this one hundreds of millions of dots 97 00:08:00.160 --> 00:08:05.720 in a graph, but with this event our relation, the edges certainly came 98 00:08:05.759 --> 00:08:11.279 alive and those dots are now like, you know, the focus is on 99 00:08:11.319 --> 00:08:15.600 that, and I think that's why you call it that dot page. I 100 00:08:15.600 --> 00:08:20.319 know you're happy. There's actually there's actually like our our founder is a big 101 00:08:20.319 --> 00:08:26.360 geek. So essentially when you when the data flows in, it goes into 102 00:08:26.399 --> 00:08:31.800 a variable called that and every time you tell it, you know, say 103 00:08:31.959 --> 00:08:37.840 the the IP address is now the node I d it has in the code, 104 00:08:37.879 --> 00:08:43.960 it'll say that dot node address equals node ID or something along those lines. 105 00:08:43.960 --> 00:08:48.720 So the code is full of that dot something. So that's pretty much 106 00:08:48.720 --> 00:08:52.960 I think where they where the original name came from. That is great. 107 00:08:54.120 --> 00:08:58.320 The other thing that that struck me is that the note already created, they 108 00:08:58.480 --> 00:09:05.159 just coming into the context. Yes, right, all notes exist. That 109 00:09:05.279 --> 00:09:13.399 is one of the fundamental process so that that's quite a deviation from a typical 110 00:09:13.440 --> 00:09:18.320 graph database. The other thing that's also a deviation, and that's why I 111 00:09:18.360 --> 00:09:26.679 find this to be fascinating, is in streaming stream processing event stream processing, 112 00:09:26.080 --> 00:09:33.120 there's always this concept of a window. That window is like either bounded for 113 00:09:33.240 --> 00:09:37.320 a certain time and then it slides or it tumbles. So there's all these 114 00:09:37.360 --> 00:09:43.840 things. And what I'm learning is that in this scenario there are no windows 115 00:09:43.519 --> 00:09:50.000 because windows sort of constrain you, and you may have a later arriving transaction, 116 00:09:50.639 --> 00:09:56.759 maybe hours later, weeks later. So the standing queries, that's a 117 00:09:56.759 --> 00:10:01.639 common concept. I think we call it continuous queries. You're calling it standing 118 00:10:01.679 --> 00:10:07.360 queries, correct, Yes, So the query is always running, streaming data 119 00:10:07.440 --> 00:10:13.120 is coming in. There is no window, which means I can identify patterns 120 00:10:15.279 --> 00:10:18.919 even a week later, yes, even a year later. Yeah, We've 121 00:10:18.960 --> 00:10:26.799 had we've had tests up to three years of data and you can still less 122 00:10:26.799 --> 00:10:30.639 than five microseconds, and I'm being generous with that. It's usually less than 123 00:10:30.639 --> 00:10:35.320 one, and less than three is the most we've ever seen. So page 124 00:10:35.679 --> 00:10:39.919 give us an example. What was this use case where the data just showed 125 00:10:41.000 --> 00:10:46.679 up a year later? And then what insight was generated? Not sure if 126 00:10:46.679 --> 00:10:50.840 you know, but I think the one that the first one that always comes 127 00:10:50.879 --> 00:10:54.480 to my mind is the one that we were invented for. I mean, 128 00:10:54.519 --> 00:10:58.279 we've we've done a lot of things since then, and there's we're great in 129 00:10:58.519 --> 00:11:03.679 financial fraud and things like that. But the big problem that we see the 130 00:11:03.720 --> 00:11:09.519 most often is the advanced persistent threat thing. That's that's why DARPA developed us 131 00:11:09.960 --> 00:11:16.639 because bad actors are as I think Eric mentioned earlier. They're not a single 132 00:11:16.759 --> 00:11:22.120 guy in a hoodie in a basement somewhere. It's a government, it's a 133 00:11:22.159 --> 00:11:26.279 bad you know, it's a whole bunch of people, or a criminal organization. 134 00:11:26.600 --> 00:11:30.960 It's a lot of people. And they're very patient and very smart. 135 00:11:31.039 --> 00:11:35.840 And what they do is find a way to get into a secure system. 136 00:11:37.440 --> 00:11:39.919 And once they're there, they just sit there and wait for those time windows 137 00:11:39.960 --> 00:11:45.200 that you're talking about that everybody else depends on these time windows, and as 138 00:11:45.240 --> 00:11:48.879 soon as they slide past them, them getting in, that first part of 139 00:11:48.919 --> 00:11:54.360 the pattern is completely invisible. It's gone. They might they look like just 140 00:11:54.440 --> 00:11:58.879 like any other legitimate user of the system. So if they then start accessing 141 00:12:00.679 --> 00:12:03.600 data or maybe even finding a way to copy it to a temp file, 142 00:12:03.679 --> 00:12:09.240 and then somebody else just magically comes in and copies that ten file externally and 143 00:12:09.720 --> 00:12:16.440 boom, you have stolen something that's very important, or in some cases they 144 00:12:16.440 --> 00:12:20.799 go in and do damage. I mean, we've had instances lately of people 145 00:12:20.840 --> 00:12:28.159 doing this sort of thing. It's like Microsoft and water utilities and lots of 146 00:12:28.240 --> 00:12:35.000 problems with this particular difficulty. And that's why DARPA developed the software is because 147 00:12:35.200 --> 00:12:39.360 we just can't have that continue to be a problem. It's like we need 148 00:12:39.399 --> 00:12:45.440 something to address it. And once we invented the technology, then we realized, 149 00:12:45.519 --> 00:12:48.559 oh, this is really good for a lot of other things as well. 150 00:12:48.639 --> 00:12:56.039 But that's what it started as it's to summarize, So go ahead, 151 00:12:56.120 --> 00:12:58.320 Alex, Hey, Alex timing, Go ahead, Well, I was just 152 00:12:58.360 --> 00:13:03.600 going to say to get my mind around streaming versus static and a year old 153 00:13:03.679 --> 00:13:05.600 or two year old data. It doesn't seem streaming to me, though, 154 00:13:07.159 --> 00:13:13.960 So how do you So the data streams in and is capped, it's persistent, 155 00:13:13.679 --> 00:13:18.919 so you can stream data in on a cluster and have millions of data 156 00:13:18.919 --> 00:13:26.200 points your graph as it as it gets new, data will constantly grow and 157 00:13:26.279 --> 00:13:31.279 it will become more and more elaborate and more and more capable, and it 158 00:13:31.360 --> 00:13:35.399 might have three years of data if that's if that's what it takes to find 159 00:13:35.399 --> 00:13:39.279 what you need. But it a data continuing to stream in, so each 160 00:13:39.320 --> 00:13:45.200 time it right more information, it becomes more elaborated. We have a reposit. 161 00:13:45.759 --> 00:13:48.240 So you have a repository and you want to look back and analyze it 162 00:13:48.639 --> 00:13:56.559 stream it into your system. That the no because because the majority of our 163 00:13:56.759 --> 00:14:01.879 capabilities are in memory. Is so actually all of this is in memory and 164 00:14:01.919 --> 00:14:05.559 it doesn't take a lot of resources. You might think, oh my god, 165 00:14:05.639 --> 00:14:11.720 this is gonna this is going to require massive beefy round nodes with that 166 00:14:11.799 --> 00:14:15.960 are expensive and all of that. We have actually done a demo on the 167 00:14:16.000 --> 00:14:22.559 smallest Raspberry Pie they make. Our technology usually takes less than it usually takes 168 00:14:22.559 --> 00:14:28.840 between It usually takes about one hundred and fifty mics of RAM, and at 169 00:14:28.840 --> 00:14:33.879 the max when it's really just going strong and doing like I said, three 170 00:14:33.960 --> 00:14:39.600 years of data whatever you need, it uses about three hundred megabytes of RAM 171 00:14:39.919 --> 00:14:46.519 for machine not really so the scaving. I'm starting to understand that the analysis 172 00:14:46.559 --> 00:14:50.039 is taking place of pattern plaining, pattern recognition. All that's taking place continually. 173 00:14:50.039 --> 00:14:54.360 You're not storing all this data. It's coming in being processed and either 174 00:14:54.399 --> 00:15:00.159 alerts are showing up or some kind of analysis is happening, right exactly that 175 00:15:00.159 --> 00:15:05.039 that's correct. We do persist everything, but it's just in case you have 176 00:15:05.120 --> 00:15:07.960 a power outage or or you need to go audit it or something like that. 177 00:15:09.000 --> 00:15:13.799 It's it's not persistent for the for the technology to function. It's persistent 178 00:15:13.879 --> 00:15:16.159 in case something bad happens. Oh yeah, I totally, I totally get 179 00:15:16.159 --> 00:15:20.279 that. So persistent that would be I'm just trying to get the synonyms right 180 00:15:20.320 --> 00:15:24.600 here, data persistent, you know, Cassandra, that's really just a database, 181 00:15:24.639 --> 00:15:28.519 right. Wouldn't you be able to use Snowflake for that too, No, 182 00:15:28.919 --> 00:15:33.559 just because we're not set up to support it. It's very It's not 183 00:15:35.000 --> 00:15:37.480 because there's anything wrong with snowfl like, it's just because it's not not designed 184 00:15:37.559 --> 00:15:43.639 for that. We recently had a large customer request that we add ClickHouse as 185 00:15:43.679 --> 00:15:46.960 a capability. We didn't have it before, and now we do. It's 186 00:15:48.000 --> 00:15:54.360 pretty much just a matter of adding a new persistor to the technology, Cassandra. 187 00:15:54.679 --> 00:16:00.159 One that's most common. I'll have to look into the other. I 188 00:16:00.240 --> 00:16:03.799 just want to jump in and ask one more thing. But maybe it's the 189 00:16:03.840 --> 00:16:07.639 novelty module. But I was. I read your website and instead that the 190 00:16:08.559 --> 00:16:12.080 okay, this this technology does this pattern recognition, finds fines of things, 191 00:16:12.120 --> 00:16:18.039 but it does mention in there somehow a person has to set up the pattern 192 00:16:18.120 --> 00:16:21.440 that you want to find so it'll find it streaming and all that. But 193 00:16:22.559 --> 00:16:25.559 I got the idea that somehow you have to define a pattern that you're looking 194 00:16:25.600 --> 00:16:29.720 for. So there's two technologies that we're talking about. One is the that 195 00:16:29.840 --> 00:16:33.840 dot streaming graph, in which case you need to define what it is you're 196 00:16:33.879 --> 00:16:37.919 looking for. And the other is that dot novelty, which has a built 197 00:16:37.960 --> 00:16:44.720 in pattern learning AI which figures out what what is a normalist in your data 198 00:16:44.720 --> 00:16:48.840 set. It doesn't require any training, it doesn't require you label the data 199 00:16:48.919 --> 00:16:52.799 or anything or even know what pattern you're looking for. It will just look 200 00:16:52.080 --> 00:16:57.399 you link them together and figured out would you link them? Sorry, yeah, 201 00:16:59.240 --> 00:17:00.759 it is kind of crazy. So would you link those together? Like 202 00:17:02.399 --> 00:17:07.000 would a company set up a novelty first and then pipe two your streaming graph. 203 00:17:07.640 --> 00:17:11.559 Yes. In fact, we've talked a lot about advanced persistent threats, 204 00:17:11.559 --> 00:17:14.920 but there's another really tough one to find, which is an insider threat, 205 00:17:15.279 --> 00:17:19.279 which is when you have an actual person who is a legitimate user who is 206 00:17:19.319 --> 00:17:25.839 nonetheless doing something they shouldn't. And there was a big contest recently to try 207 00:17:25.880 --> 00:17:27.920 and find you know, what's the most efficient way to find insider threats, 208 00:17:29.440 --> 00:17:33.039 and we pretty much found a far more efficient way to do that. We 209 00:17:33.079 --> 00:17:38.519 can do it in a few seconds using first streaming graph to find a particular 210 00:17:38.519 --> 00:17:44.519 pattern and then novelty to find anomalist behavior. And you can you can absolutely 211 00:17:44.880 --> 00:17:49.119 stream them together and make them do do their thing separately and put it together 212 00:17:49.160 --> 00:17:53.920 and come up with something exceptional. Yeah, like in a workflow. Basically, 213 00:17:53.960 --> 00:17:56.200 this is cool stuff. We're coming up on our first break, folks. 214 00:17:56.200 --> 00:18:00.759 But we've been sitting here talking about streaming graph. It's fast technology. 215 00:18:00.759 --> 00:18:03.680 With Paige Roberts of that dot. We've got Sanji Mohona Sangmo and that was 216 00:18:03.720 --> 00:18:07.000 Alex Husky chiming in from Mexican Mobile. Will be right back. Don't touch 217 00:18:07.039 --> 00:18:18.839 that down. We're listening to Inside Analysis. Welcome back to Inside Analysis. 218 00:18:19.319 --> 00:18:29.039 Here's your host, Eric Tabanac page. I have a question without talking about 219 00:18:29.200 --> 00:18:33.279 the data persist for Faull Tolgrand persisting the data in case of our failure, 220 00:18:33.759 --> 00:18:37.079 if I just want to be I just want to talk about that in memory 221 00:18:37.119 --> 00:18:44.759 piece, what do you by any chance have an ability to replicate from one 222 00:18:44.799 --> 00:18:49.799 cluster to cluster, memory to memory for maybe data residency reasons or through port 223 00:18:51.039 --> 00:18:55.039 or something like that. I honestly don't know the answer to that. I 224 00:18:55.079 --> 00:19:02.240 think at this point it's like, Brian, are you who invented this offer 225 00:19:02.319 --> 00:19:03.400 to be on the show? And I'm like, oh, oh, I 226 00:19:03.440 --> 00:19:07.680 wish I was here. Yeah, I'm not. I'm not really certain. 227 00:19:07.680 --> 00:19:11.079 I'd have to get back to you on that one, Okay, no worries, 228 00:19:11.160 --> 00:19:14.920 Okay, because I know, like some company like Redit's, you know, 229 00:19:15.039 --> 00:19:22.440 they do that, you know, cross replication across clusters. Because three 230 00:19:22.519 --> 00:19:26.799 years in memory sounds like a long time. It sounds like a long time. 231 00:19:26.839 --> 00:19:29.359 But we've done it, We've tested it, you know it works. 232 00:19:29.920 --> 00:19:36.359 We have I mean, we have some of the more advanced uh cybersecurity folks 233 00:19:36.519 --> 00:19:41.240 doing it now, so we know it can be done. The I think 234 00:19:41.640 --> 00:19:47.400 probably the answer to your first question is if someone needs that, if we 235 00:19:47.440 --> 00:19:52.680 have a We're a small enough company that if a if a large customer comes 236 00:19:52.680 --> 00:19:56.279 in and says, we want this, but we need this capability, We'll 237 00:19:56.279 --> 00:20:02.599 build it. That's been It's kind of the difference between a big company and 238 00:20:02.640 --> 00:20:06.960 a large and a small company is the small company goes okay and builds it. 239 00:20:07.200 --> 00:20:10.839 The large company goes I got five hundred other people and need something else, 240 00:20:11.599 --> 00:20:15.920 you can just wait, right. We actually have an interesting question from 241 00:20:15.920 --> 00:20:18.079 an attendee in our virtual studio audience here, and folks, if you want 242 00:20:18.079 --> 00:20:22.440 to be in the virtual studio audience, just top online to inside analysis dot 243 00:20:22.440 --> 00:20:26.559 com and register for one of these events. But one attendee's asking, what 244 00:20:26.599 --> 00:20:30.680 about competition? Would X A Beam be a competitor? I mean, I 245 00:20:30.720 --> 00:20:33.599 haven't seen anyone doing exactly what you're doing, so I don't personally know of 246 00:20:33.640 --> 00:20:37.359 any competitors. But who do you run into other companies when you're out there? 247 00:20:37.400 --> 00:20:41.839 Tell us about the competitive landscape page. There aren't a lot of competitors 248 00:20:41.880 --> 00:20:48.160 because it's such a new technology. I think some people are trying to do 249 00:20:48.240 --> 00:20:55.000 something similar. Memograph is one that I've seen that's sort of approaching something like 250 00:20:55.039 --> 00:21:03.640 this. I think the LinkedIn liquid uh, where they're their graph capability that 251 00:21:03.680 --> 00:21:11.240 they're doing behind the scenes on LinkedIn is probably pretty similar. That's it. 252 00:21:11.599 --> 00:21:15.599 I mean, that's all I can think of a few of I think stream 253 00:21:15.720 --> 00:21:21.079 graph, there's stream stream set. I think there was stream sets. There's 254 00:21:21.119 --> 00:21:26.960 a lot of event stream processors, uh fleet yeah, stream stream sets. 255 00:21:26.079 --> 00:21:30.920 Guys like that, but very few of them are doing I don't think any 256 00:21:30.960 --> 00:21:37.279 of them are doing the graph paradigm alerts. But is that that's centering on 257 00:21:37.319 --> 00:21:41.079 the alerts that you is that what we want to we want to Gardner, 258 00:21:41.200 --> 00:21:45.319 right, is alerts quick warning to shut something down or whatever. They're not 259 00:21:45.359 --> 00:21:48.960 doing that, what you guys are you know, they're just not They're not 260 00:21:48.119 --> 00:21:52.599 doing it as a graph, and they're not they're doing it with time windows. 261 00:21:52.599 --> 00:21:55.640 I think, Sanji, you've mentioned pretty much all of the other events 262 00:21:55.640 --> 00:22:00.920 stream processors are limited by their time windows, and they have to be. 263 00:22:00.000 --> 00:22:03.799 It's just the way they're designed. And we were designed from the ground up 264 00:22:03.839 --> 00:22:08.680 to not have that limitation. So it's very it's different in that way. 265 00:22:08.720 --> 00:22:14.000 And the other difference is that all of the other events stream processors sort of 266 00:22:14.000 --> 00:22:18.200 perceive data as almost a rowan column, as if it were about to go 267 00:22:18.319 --> 00:22:25.319 into a normal database, whereas we perceive it as a graph. And one 268 00:22:25.319 --> 00:22:30.440 of the advantages of that is the categorical thing. So if you want to 269 00:22:30.440 --> 00:22:36.640 look at IDs, IP, addresses, names, you know, all the 270 00:22:36.640 --> 00:22:41.839 things that are categorical as opposed to you, you know, temperature, which 271 00:22:41.880 --> 00:22:45.160 is you know, up and down it's got a numeric range. If it's 272 00:22:45.240 --> 00:22:49.440 not a numeric data, most of the state of the art is to take 273 00:22:49.519 --> 00:22:56.279 that data and convert it into numeric and it's huge and bloated and sparse and 274 00:22:56.319 --> 00:23:03.400 really hard and takes a long time to annaly lies, whereas graph data is 275 00:23:03.480 --> 00:23:08.119 already in a category and we just analyze it immediately as is. And this 276 00:23:08.400 --> 00:23:15.440 makes it much better for finding things that are for answering certain questions, and 277 00:23:15.480 --> 00:23:19.559 in particular questions that are categorical in nature, like who did this, Where 278 00:23:19.559 --> 00:23:23.640 did you? Where did you do it? What what you know? When? 279 00:23:23.960 --> 00:23:27.759 When was that? When did that happen? What is the relationship between 280 00:23:27.759 --> 00:23:33.519 this person and that person? All of these are our categorical questions, and 281 00:23:34.200 --> 00:23:40.440 right right as we do the graph should it makes sense. I see some 282 00:23:41.279 --> 00:23:45.799 maybe inspiration from Mark logic. It's still out there under a different name. 283 00:23:45.839 --> 00:23:49.599 Now what did they get? But they were big on the the semi structured, 284 00:23:51.359 --> 00:23:55.079 not having to be wrong on column, not having to be sequel friendly. 285 00:23:55.200 --> 00:23:57.519 But they can still make associations the way you are. But they weren't 286 00:23:57.559 --> 00:24:03.119 dwelling on the streaming aspect. Well. In the the Mark logicts of the 287 00:24:03.160 --> 00:24:07.799 world also do. Uh. I mean they pretty much we expect our data 288 00:24:07.839 --> 00:24:12.559 to come in in that semi structured form like Jason or something along those lines, 289 00:24:12.640 --> 00:24:18.319 log formats that sort of thing, but when and then we immediately turn 290 00:24:18.400 --> 00:24:22.200 it into a graph. So the semi structured formats are not really ideally suited 291 00:24:22.240 --> 00:24:29.799 to find those relationships and patterns across times and individuals and you know, IP 292 00:24:29.920 --> 00:24:34.079 addresses and networks and all that sort of thing. So they're they're designed to 293 00:24:34.119 --> 00:24:41.039 be sort of self contained so that I can send a single message and it 294 00:24:41.079 --> 00:24:44.799 has all the information for you to understand that message. So they're really good 295 00:24:44.799 --> 00:24:48.920 at that, but they're not so good at finding the relationships between multiple things. 296 00:24:49.599 --> 00:24:56.279 So it's interesting. So what you think is that the input is because 297 00:24:56.279 --> 00:25:03.640 the input is cough cop also kin so so payload is Jason. But you 298 00:25:03.680 --> 00:25:08.759 are then ingesting that and converting it into a graph model. That's correct. 299 00:25:08.759 --> 00:25:14.799 That's that's what the ingest query is for. It's pretty much as the Jason 300 00:25:14.880 --> 00:25:18.160 comes in, which parts of it do you want to be a node? 301 00:25:18.200 --> 00:25:19.839 Which parts of it do you want to be a relationship? What should be 302 00:25:19.839 --> 00:25:25.000 a property? How do I take this data and turn it into a graph. 303 00:25:25.160 --> 00:25:30.000 That's that's what is a graph model? Is it a property graph or 304 00:25:30.160 --> 00:25:34.279 is it an IDF. I think the answer to that is IDF. But 305 00:25:34.359 --> 00:25:41.200 I don't vote me. I'm not one hundred percent certain. Well, Sanji, 306 00:25:41.319 --> 00:25:42.759 I think you. I think we need to preview, we need to 307 00:25:42.759 --> 00:25:48.160 get all of our techies on there and ive deep on that. Yeah, 308 00:25:48.240 --> 00:25:53.000 because cipher was bolted by NEO four J and cipher is a property graph. 309 00:25:53.200 --> 00:25:57.960 So yes, yes, And Sparkle if you said you sports sparkle, then 310 00:25:59.079 --> 00:26:03.720 that sounds like it maybe an rd EF. Yeah, and that again you're 311 00:26:03.759 --> 00:26:08.359 you're asking the wrong person on that. But cipher is our main language that 312 00:26:08.400 --> 00:26:11.799 we support. We're looking at supporting some others, but we're looking more at 313 00:26:11.839 --> 00:26:17.720 like graph q L and some others. I don't think we're looking at sparkle 314 00:26:17.799 --> 00:26:22.279 because and I think that answers your question actually, because it's not the right 315 00:26:22.359 --> 00:26:26.759 kind of graph. So I knew that, but I did not know the 316 00:26:26.680 --> 00:26:33.960 technical terms. Yeah, we for financial financial fraud. I'm sorry, Eric, 317 00:26:33.000 --> 00:26:36.160 go ahead, No, you go ahead. That's good. That's good. 318 00:26:36.200 --> 00:26:40.880 Well, all right, let's say let's say a big company like the 319 00:26:41.200 --> 00:26:45.359 representative here, but I'm an advocate for honesty. So so if they're interested 320 00:26:45.440 --> 00:26:52.640 in, let's say, finding fraud within or the company were outside, you 321 00:26:52.640 --> 00:26:56.759 know, the people we interact with invoices and all that, how does h 322 00:26:56.279 --> 00:27:02.200 your screaming graph help find a financial fraud? You've kind of like a banquet. 323 00:27:02.640 --> 00:27:06.319 But I just wondered, if I don't know about Benfort's law, the 324 00:27:06.440 --> 00:27:10.720 incorporate that sort of algorithm in there already, or are you able to without 325 00:27:10.759 --> 00:27:15.160 that kind of thing in some kind of innately find that these patterns of cheating 326 00:27:15.200 --> 00:27:18.920 going on? Well, I think that's it. There's two things going on 327 00:27:19.000 --> 00:27:22.240 there. One is, if you're trying to look for a particular pattern and 328 00:27:22.279 --> 00:27:26.880 you already know what fraud looks like, it's like, you know what a 329 00:27:26.920 --> 00:27:32.160 fraudulent actor is likely to do first, second, third, fourth, fifth, 330 00:27:32.559 --> 00:27:36.319 And if if you then find somebody doing all of those, you can 331 00:27:36.359 --> 00:27:40.519 immediately go, oh, that's fraud. And the thing we do there is 332 00:27:40.559 --> 00:27:45.319 we're using pretty much the same patterns as everybody else, except we don't have 333 00:27:45.400 --> 00:27:48.880 to take the categorical data, change it to numeric, then train something, 334 00:27:49.279 --> 00:27:52.319 then come back and predict. You can actually do it in line. You 335 00:27:52.359 --> 00:27:57.960 can do the analysis on much more quickly. So a lot of what we 336 00:27:59.079 --> 00:28:06.519 do is not necessarily different fraud detection. It's faster fraud detection. And the 337 00:28:06.640 --> 00:28:11.880 other aspect of that is because we have novelty. A lot of times, 338 00:28:11.920 --> 00:28:14.920 if you don't know what fraud looks like, if you don't know what you're 339 00:28:14.960 --> 00:28:18.519 looking for, you can feed it into novelty, and novelty will find the 340 00:28:18.559 --> 00:28:25.960 things that are unusual that are different. Like here's a good example. If 341 00:28:26.000 --> 00:28:32.839 I am have just paid for a hotel room and dinner in Paris and then 342 00:28:33.480 --> 00:28:41.440 lunch, I pay for lunch work, You're gonna the anomalist thing is the 343 00:28:41.480 --> 00:28:47.839 New York and you're going that's probably a fraudulent transaction. On the other hand, 344 00:28:48.279 --> 00:28:52.599 if I know that my pattern is, you know, multiple purchases in 345 00:28:52.640 --> 00:28:56.559 a single location and then purchases in a different location, then I might be 346 00:28:56.599 --> 00:29:00.000 looking for that pattern, but I'll probably get a lot of false pots. 347 00:29:00.839 --> 00:29:03.720 You know, I got on a plane and troubled somewhere else. You know 348 00:29:03.839 --> 00:29:07.599 that sort of thing. I travel a lot, so I used to get 349 00:29:08.000 --> 00:29:12.039 false positives a lot, and we've gotten better over time about not catching that 350 00:29:12.079 --> 00:29:15.839 sort of thing. So I don't you don't get my card turned down when 351 00:29:15.880 --> 00:29:19.039 I'm in you know, some other country doing I mean there's some other city 352 00:29:19.160 --> 00:29:26.240 doing a conference or something like that. The novelty is really good at figuring 353 00:29:26.279 --> 00:29:33.920 out unique is not necessarily novel and finding something that's when lay novel is very 354 00:29:33.960 --> 00:29:38.240 different. RUD be great to see if you have templates, let's say, 355 00:29:38.279 --> 00:29:42.160 by know, you might have been working with big businesses that have tons of 356 00:29:42.160 --> 00:29:47.200 invoices and that sort of thing, and maybe have a template where where not 357 00:29:47.279 --> 00:29:51.000 everything has to be custom made. We do. We have what we call 358 00:29:51.079 --> 00:29:56.599 recipes in the koin open source capability so that you can just pretty much pull 359 00:29:56.640 --> 00:30:02.839 it up and it's used. It uses publicly available data. There's been enough 360 00:30:03.200 --> 00:30:10.359 like fraud contests, cybler security contrastests, things like that out there that there's 361 00:30:10.400 --> 00:30:15.680 a lot of publicly available data which we know that data has the information to 362 00:30:15.839 --> 00:30:18.880 find the problem, and it's just a matter of how good are you at 363 00:30:18.880 --> 00:30:22.880 finding it. So we can just pull in that data and we have a 364 00:30:22.920 --> 00:30:32.279 recipe which is essentially a self packaged code bundle that you can operate yourself on 365 00:30:32.319 --> 00:30:37.839 your own laptop and see how it works. And those are those are those 366 00:30:37.880 --> 00:30:44.039 are really cool for learning how the tech works and its capabilities. Yeah, 367 00:30:44.079 --> 00:30:48.759 Alex was a musing earlier before we hit the record button that it's hard to 368 00:30:48.759 --> 00:30:51.480 find time to play with things, but this looks like something that's a lot 369 00:30:51.480 --> 00:30:53.319 of fun to play with because you do have to kind of play around and 370 00:30:53.519 --> 00:30:57.920 I found an interesting concept here as I'm just trying to wrap my head around 371 00:30:59.000 --> 00:31:02.799 it myself. Here's a question that I bet that dot is very good at 372 00:31:02.839 --> 00:31:06.960 and very other solutions are not good at, which would be something like find 373 00:31:07.000 --> 00:31:10.960 all the people who interacted with this potentially fraudulent account in the last hour. 374 00:31:11.759 --> 00:31:14.119 Right. So, a traditional solution, you're not going to get there. 375 00:31:14.160 --> 00:31:15.799 You can't get you could run reports, it's going to take hours, it's 376 00:31:15.839 --> 00:31:19.960 going to take longcount you just won't get there. Whereas this, because it 377 00:31:21.039 --> 00:31:25.440 is graph oriented and you're noticing all these nodes and edges and you're constantly streaming, 378 00:31:25.799 --> 00:31:27.759 that's the kind of question you're get to answer to quickly. And you 379 00:31:27.799 --> 00:31:33.160 know, from my experience, once you understand the kinds of questions you can 380 00:31:33.240 --> 00:31:37.160 ask from an analytical tool, that's when the possibilities open up, right, 381 00:31:37.200 --> 00:31:45.400 Because I think there's a lot of sort of built in lack of enthusiasm about 382 00:31:45.440 --> 00:31:49.279 asking questions that can't be answered. And so if you know a question could 383 00:31:49.279 --> 00:31:52.000 be answered, you start to ask it. But if you think it's going 384 00:31:52.079 --> 00:31:55.599 to be a long running query or you're just going to get in trouble for 385 00:31:55.599 --> 00:31:56.519 doing it, you just don't go down that road. You don't know that 386 00:31:56.519 --> 00:32:01.279 it's possible once you see that these things are years people absolutely right. I 387 00:32:01.279 --> 00:32:07.279 mean, I think that we've trained our analysts to think that certain things can 388 00:32:07.279 --> 00:32:10.880 be asked and certain things cannot. And this is one of the technologies that 389 00:32:12.200 --> 00:32:15.079 you have to kind of change your mind and go, I can ask that. 390 00:32:15.880 --> 00:32:19.920 I can ask not just you know, how many people has this person 391 00:32:20.359 --> 00:32:23.400 interacted within an hour, but how many how many has it interacted in the 392 00:32:23.519 --> 00:32:28.000 last year. It's like, I've found this person. Now I know they're 393 00:32:28.440 --> 00:32:32.680 they're a frauductor. It's like, oh, but you know in the last 394 00:32:32.759 --> 00:32:37.200 last year, I didn't know that there's you know, fifty other people they've 395 00:32:37.279 --> 00:32:42.519 interacted with since then. And I can get that answer in seconds. Yeah, 396 00:32:42.799 --> 00:32:46.720 I think this is the streaming part is super exciting. That graph piece. 397 00:32:47.079 --> 00:32:52.119 In fact, the recent graph databases became quite very known many years ago 398 00:32:52.519 --> 00:32:59.519 was because of this massive scandle if you remember, called Panama Papers. So 399 00:32:59.599 --> 00:33:07.759 somebody uh exposed a treasure trove of documents called Panama Papers, and these journalists 400 00:33:07.759 --> 00:33:13.240 got hold of those documents, but they contell memos and all that, but 401 00:33:13.720 --> 00:33:20.119 forever to actually go to forever until some journalists discovered Draft database and said, 402 00:33:20.200 --> 00:33:23.519 let me just ingest it into graft database, and the whole story unraveled. 403 00:33:23.960 --> 00:33:29.880 But it took a long time. And now comes that dot and quine, 404 00:33:30.200 --> 00:33:35.119 which is saying that what if this data was in real time streaming, we 405 00:33:35.200 --> 00:33:42.160 can find all the relationships and patterns, uh even go back into the history 406 00:33:43.720 --> 00:33:47.319 and these insights. Yeah, that's right. Well, folks were up on 407 00:33:47.319 --> 00:33:50.599 our next break here, but we're going to pick that up right after the 408 00:33:50.599 --> 00:33:52.119 break. This is really interesting stuff. Don't touch out how folks, you're 409 00:33:52.119 --> 00:34:04.720 listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host, 410 00:34:05.279 --> 00:34:10.280 Eric Tavanaugh. All right, folks, back here on Inside Analysis talking to 411 00:34:10.400 --> 00:34:15.840 an expert panel. We've got Paige Roberts of that Dot. We've got Sonjif 412 00:34:15.880 --> 00:34:21.400 Mohan Sangmo, and our good buddy h Alexander Husky from Exon Mobile representing himself. 413 00:34:21.440 --> 00:34:23.519 Just asking some fun questions, and Paige, I'm gonna throw this one 414 00:34:23.559 --> 00:34:28.519 back to you because it just it finally opped in my mind. The real 415 00:34:28.639 --> 00:34:32.119 value here is getting through to people, to the analysts that they really can 416 00:34:32.320 --> 00:34:37.320 ask these very interesting, gritty questions. And you know, the unveiling of 417 00:34:37.360 --> 00:34:42.480 this, the timing is actually pretty interesting because there's something else happening in the 418 00:34:42.519 --> 00:34:46.679 AI world called large language models, which have taken the world by storm, 419 00:34:46.760 --> 00:34:51.400 quite frankly and for good reason. And I've spent a lot of time researching 420 00:34:51.440 --> 00:34:54.599 these things to understand exactly how they work. And they use probabilistic math, 421 00:34:54.639 --> 00:34:59.679 and they convert text into vectors basically, so you have vector databases and you 422 00:34:59.719 --> 00:35:01.719 do all these similarity searches, and that's how they're doing what they do. 423 00:35:02.199 --> 00:35:05.960 But then you have these rag models that kind of spin around on very interesting 424 00:35:05.960 --> 00:35:10.599 stuff. The point being you can ask all sorts of interesting questions and get 425 00:35:10.599 --> 00:35:15.840 interesting answers, not always accurate with large language models, but at least you 426 00:35:15.880 --> 00:35:20.920 can start asking these very interesting questions and getting prosaic responses, and that's somewhat 427 00:35:20.960 --> 00:35:23.840 analogs analogous to what you folks are doing, and that you can ask these 428 00:35:23.920 --> 00:35:30.719 really detailed questions that traditional analytical systems would choke on for hours or days or 429 00:35:30.719 --> 00:35:36.360 would just fall over sideways. And that is the exciting part. So I 430 00:35:36.440 --> 00:35:38.519 think it was sanchiev you mentioned at the end of the last segment. Yes 431 00:35:38.599 --> 00:35:42.679 you can find this thing. Now, Oh look at this actor, and 432 00:35:42.760 --> 00:35:45.719 now you can build a whole new set of queries around that actor. Well, 433 00:35:45.760 --> 00:35:50.199 who else, who else interacts with this person on a regular basis? 434 00:35:50.480 --> 00:35:52.440 How many times has this person come in? How many times do people like 435 00:35:52.519 --> 00:35:57.840 him come in? You can really start to explore what's happening once you get 436 00:35:57.880 --> 00:36:00.639 that foothold. And as you've said, with these two different products, one 437 00:36:00.760 --> 00:36:02.159 dynamically comes up with new stuff, the other you have to tell it. 438 00:36:02.519 --> 00:36:07.360 But my point is that once you use this technology to find the actor, 439 00:36:07.440 --> 00:36:12.880 whether it's a bad or good actor, then you can ask amazingly rich questions, 440 00:36:13.039 --> 00:36:15.000 very detailed that other systems are never going to be able to handle. 441 00:36:15.360 --> 00:36:19.920 That's a huge game changer. What do you think, Paige, Yeah, 442 00:36:20.039 --> 00:36:24.960 I think it the power of being able to ask I call it, you 443 00:36:25.000 --> 00:36:29.239 know, query the future, being able to query the past, the present, 444 00:36:29.320 --> 00:36:35.039 and the future simultaneously. So if I just discovered something interesting, I 445 00:36:35.079 --> 00:36:39.920 can then relate it to things that happened a year ago and things that haven't 446 00:36:39.960 --> 00:36:44.960 quite happened it you know that I kind of expect to happen within the next 447 00:36:44.960 --> 00:36:50.880 week or two. So I can see say this is a financial broadstraer, 448 00:36:51.280 --> 00:36:53.719 and rather than you know, maybe I don't know where he's coming from. 449 00:36:54.000 --> 00:36:59.280 I can then look at everybody that he's related to over the last year and 450 00:36:59.320 --> 00:37:05.079 then maybe keep that up and as the time goes on, see who he 451 00:37:05.159 --> 00:37:07.800 acts with next and figure out where he is. You know. You know 452 00:37:07.840 --> 00:37:12.119 what this is reminding me of is you think about all these TV shows and 453 00:37:12.199 --> 00:37:15.199 movies where they have the detectives, right, and they're all in the room 454 00:37:15.239 --> 00:37:17.519 in the conference room, and they have this big graph on the wall that 455 00:37:17.599 --> 00:37:21.880 has pictures and information and they're like looking at the graph and like, huh, 456 00:37:21.920 --> 00:37:24.000 like this guy's connected to that guy and all that stuff. But you 457 00:37:24.159 --> 00:37:29.280 have but you have it in an engine that can run on a Raspberry Pie. 458 00:37:29.480 --> 00:37:34.079 That's how lean it is, and you can absorb absolutely gobsmacking amounts of 459 00:37:34.119 --> 00:37:37.440 data in order to do it. So it is like that investigative graph in 460 00:37:37.480 --> 00:37:43.119 the conference room at the Downtown office basically. But it's a technology which can 461 00:37:43.159 --> 00:37:45.719 pull in massive amounts from all sorts of sources. Right, you can set 462 00:37:45.760 --> 00:37:50.440 up COFFA topics on whatever the heck you want. You're not limited to the 463 00:37:50.480 --> 00:37:53.000 types of data or even the volumes of data. Right, You're not even 464 00:37:53.039 --> 00:37:58.360 limited just to streaming data. I mean, we have a lot of people 465 00:37:58.360 --> 00:38:04.239 who pull in of files or whatever that they had setting around that might be 466 00:38:04.320 --> 00:38:08.920 related to the data that's streaming in, and you can build your graph with 467 00:38:09.119 --> 00:38:15.519 that data so that you have the context, you have everything that's around the 468 00:38:15.599 --> 00:38:20.400 data that's streaming in, and you can see how everything relates to everything. 469 00:38:20.840 --> 00:38:28.480 It's really handy for like things like optimizing networks and finding problems there because you 470 00:38:28.519 --> 00:38:32.400 can analyze the CDM data, the data coming in from all of your different 471 00:38:32.440 --> 00:38:37.679 networks, all of these different IP addresses, maybe the data that you had 472 00:38:37.719 --> 00:38:43.880 sitting around already, and you can put that all together and find new information 473 00:38:44.039 --> 00:38:49.880 that you didn't even think about looking for before. Maybe. Yeah, you're 474 00:38:49.920 --> 00:38:53.639 able to build out a case on whatever it is that comes across the trans 475 00:38:53.880 --> 00:38:57.280 And that's the key. That's kind of what I'm getting at here, is 476 00:38:57.280 --> 00:39:00.199 that even human beings how we perceive the world. We have a set of 477 00:39:00.559 --> 00:39:06.280 assumptions that we've made that we've learned. What if Mark Twain said, biases 478 00:39:06.360 --> 00:39:09.519 that set of prejudices you've you've developed over the first eighteen years of your life 479 00:39:09.599 --> 00:39:12.920 or something. You have a certain way of looking at the world, and 480 00:39:12.920 --> 00:39:15.199 that's what you accept as normal. But when and I'll give you another example, 481 00:39:15.239 --> 00:39:19.440 and then maybe we'll bring Sanjeepe back into this. I've noticed this myself. 482 00:39:19.480 --> 00:39:22.079 When you hear a new word, and if you're fifty plus years old, 483 00:39:22.159 --> 00:39:23.840 it's not that often you hear a new word or a new term or 484 00:39:23.840 --> 00:39:27.199 something and you learn that we're like, oh wow, I never learned that 485 00:39:27.519 --> 00:39:30.800 word before, Like kwine like a quine, right, so you learn this 486 00:39:30.840 --> 00:39:34.559 wine is actually quine has actually been around. It's it's named after a guy, 487 00:39:35.079 --> 00:39:38.719 right. I looked at up that the guy. There's also another thing 488 00:39:38.840 --> 00:39:44.840 named after the same guy. That's an application that generates its own source code. 489 00:39:44.960 --> 00:39:50.760 We're not, but we're named after the same Yeah. Well, at 490 00:39:50.800 --> 00:39:52.480 the point being you hear you learn this new word, and all of a 491 00:39:52.480 --> 00:39:55.079 sudden, you hear that word like seven times the next ten days. You're 492 00:39:55.079 --> 00:39:58.159 like, what is going on I'd never heard this word before, and now 493 00:39:58.159 --> 00:40:00.840 I've heard it seven times. Now you know, now your model is trained 494 00:40:00.920 --> 00:40:05.280 on that word, and so you recognize that as a pattern, you recognize 495 00:40:05.280 --> 00:40:08.559 it as something noteworthy. That's true, right, So this is why that's 496 00:40:08.559 --> 00:40:15.039 actually let's go ahead out. It's actually the reticulate reticulated, reticulating mechanism in 497 00:40:15.079 --> 00:40:19.519 your brain that does that. It's networking. In other words, it associates 498 00:40:19.559 --> 00:40:27.440 all that sort of thing, probably streaming and articulating time. Yeah, in 499 00:40:27.480 --> 00:40:30.719 fact real time. So I want to actually talk about this. You know, 500 00:40:30.760 --> 00:40:37.119 how these tones come into existence and then in our mind we we've made 501 00:40:37.199 --> 00:40:40.519 these these hard connections, but they may not always be correct. Like when 502 00:40:40.519 --> 00:40:45.719 we think streaming, we always think real time. And I let's say you 503 00:40:45.760 --> 00:40:50.159 are bringing this up and page. You mentioned that it may not be real 504 00:40:50.280 --> 00:40:55.159 time. It could be batch upload of documents for instance, or it may 505 00:40:55.199 --> 00:41:00.960 be real time. But if the real time comes into a cat topic and 506 00:41:00.039 --> 00:41:05.960 does not get consumed for a certain period of time, it's no longer real 507 00:41:06.079 --> 00:41:09.039 time. It's streaming data. So real time in streaming, I kind of 508 00:41:09.159 --> 00:41:15.119 use analogis sleep. But there may not be a data that is at rest 509 00:41:15.559 --> 00:41:21.199 in a database is obviously not streaming, but the moment I started using it, 510 00:41:21.599 --> 00:41:25.719 then the data is in motion. Yeah right, I actually I think 511 00:41:27.039 --> 00:41:30.079 I did. I don't know. Years back, I did a thing called 512 00:41:30.159 --> 00:41:34.159 the Really Real Meaning of real time because it seems like everybody I talked to 513 00:41:34.280 --> 00:41:39.559 had a different definition. I asked a prominent analyst not too long ago, 514 00:41:39.559 --> 00:41:44.320 and he said, well, within fifteen minutes, and I was just like, 515 00:41:45.199 --> 00:41:49.880 okay. I think at some point we had a customer that was like, 516 00:41:50.280 --> 00:41:54.000 I want it to check data every hour in real time, and I 517 00:41:54.079 --> 00:41:59.599 was just like, I'm not sure you we know what this word means. 518 00:42:00.079 --> 00:42:05.320 Yeah, because the types of real times, the two when people say real 519 00:42:05.440 --> 00:42:08.519 time, there's engineering real time and then there's a business real time. Engineering 520 00:42:08.800 --> 00:42:17.480 real time is actually only possible buyomachines, and it's microseconds or maybe maini seconds. 521 00:42:19.000 --> 00:42:24.679 Humans humans are just too complicated to make such fast decisions. The business 522 00:42:24.719 --> 00:42:29.320 real time could be under fifteen minutes because it depends on business use case. 523 00:42:29.760 --> 00:42:32.280 Yeah, yeah, and I think it depends a lot. But I think 524 00:42:32.360 --> 00:42:42.000 the idea of being able to find a fraudster, find a cybersecurity risk, 525 00:42:42.519 --> 00:42:50.840 find that person and stop them before they have done something bad is important. 526 00:42:50.960 --> 00:42:54.679 I mean, you've gone from like the whole the whole idea of fraud detection 527 00:42:54.920 --> 00:43:00.599 versus broad prevention is time. That's the only difference. It's like they're identical 528 00:43:00.679 --> 00:43:06.119 things. It's just one you're finding after the fact and the other one you're 529 00:43:06.159 --> 00:43:09.000 finding fast enough that you can do something about it. And I think that's 530 00:43:09.039 --> 00:43:14.920 the real power of real time is being able to go, hey, that 531 00:43:15.440 --> 00:43:20.280 I caught that advanced persistent threat who's been sitting here for a year the moment 532 00:43:20.320 --> 00:43:24.400 they tried to steal something. It's like that is that is really powerful, 533 00:43:24.480 --> 00:43:30.039 that ability to go from I detected it. It's a little late, but 534 00:43:30.119 --> 00:43:35.360 I figured it out to I stopped it the moment they tried to do something 535 00:43:35.400 --> 00:43:39.039 wrong. And I think that's the real to me, that's a definition of 536 00:43:39.079 --> 00:43:45.039 real time is doing things fast enough that you can make a difference. Mm 537 00:43:45.159 --> 00:43:49.559 hmmm. Yeah, Well you're also motivating the analysts. I mean, this 538 00:43:49.719 --> 00:43:54.480 is one of my standard soapbox issues that morale is the most important characteristic of 539 00:43:54.559 --> 00:43:58.960 any organization because when morale is high, good things happen. When morell's low, 540 00:43:59.039 --> 00:44:00.800 bad things happen. It doesn't matter how much money, you have, 541 00:44:00.880 --> 00:44:05.079 how many resources, what you're doing? None of that stuff matters and more 542 00:44:05.079 --> 00:44:07.840 ale, it's very difficult to quantify, to understand or to qualify. How 543 00:44:07.840 --> 00:44:10.960 do you know? I mean you can tell by looking at various metrics. 544 00:44:12.000 --> 00:44:15.679 But the point is this gets people excited. And look, let's face it, 545 00:44:15.679 --> 00:44:19.679 cybersecurity is a very very challenging environment. I mean, we'll talk about 546 00:44:19.679 --> 00:44:22.880 it all the time, because you know, things are happening and if you 547 00:44:22.920 --> 00:44:25.239 have no mechanism for being able to get to them, it's very frustrating and 548 00:44:25.280 --> 00:44:29.679 people kind of go into road processes and just sort of give up, and 549 00:44:29.719 --> 00:44:31.280 that is not an acceptable answer. Well, let's pick this up. I 550 00:44:31.280 --> 00:44:34.840 have one more question from the studio audiens. We'll do for the podcast bonus 551 00:44:34.880 --> 00:44:37.360 segment coming up in two seconds. We'll be right back. You're listening to 552 00:44:37.440 --> 00:44:43.840 Inside Analysis. All right, folks, Time for the podcast bonus segment here 553 00:44:43.840 --> 00:44:46.239 and a fascinating show. Hats off to all of our guests today. Paige 554 00:44:46.320 --> 00:44:51.119 Robertson at that dot look them up online, Sanjiv mohon Off, Sangmo and 555 00:44:51.400 --> 00:44:55.039 Alex Husky calling in from the back end fields way out there in North Dakota. 556 00:44:55.119 --> 00:44:59.599 That's why he's got some connectivity problems down again, he's way out there, 557 00:44:59.679 --> 00:45:01.760 no go to. It is really big and there's a lot going on 558 00:45:01.840 --> 00:45:07.480 out there. But Page we had a great question strike it's the Moon. 559 00:45:07.800 --> 00:45:14.639 Great question from someone saying this sounds like it's an awesome solution for what's called 560 00:45:14.800 --> 00:45:21.840 user and entity behavior analytics, which again speaks to the fact that human beings 561 00:45:21.840 --> 00:45:23.760 as users, for example, can do all kinds of things. I mean, 562 00:45:23.760 --> 00:45:27.320 it's not like a list of five things I could do as a human. 563 00:45:27.360 --> 00:45:29.960 There's an infinite list of things I can do, and there's an infinite 564 00:45:30.639 --> 00:45:35.800 array of behavioral patterns that could be identified. And traditional databases are not good 565 00:45:35.800 --> 00:45:38.679 at that kind of thing, certainly, not relational databases or object stores or 566 00:45:38.679 --> 00:45:43.039 all these different things. They hold stuff and that's their job, and they're 567 00:45:43.039 --> 00:45:46.519 designed to hold things and then deliver them upon demand. But even the analytical 568 00:45:46.519 --> 00:45:51.679 engines are are more relational in nature, and they're not graph in nature. 569 00:45:51.719 --> 00:45:54.519 And graph is fascinating because again it's got nodes and edges. The node could 570 00:45:54.559 --> 00:45:58.320 be any entity. You could have a million nodes, you could have more 571 00:45:58.360 --> 00:46:02.960 than that. And beavis also are so variant, and I think that's what's 572 00:46:04.000 --> 00:46:07.199 so exciting about this streaming graph. From that thought is that it is so 573 00:46:07.599 --> 00:46:13.000 malleable. Nothing is unwieldy to it. What do you think about that? 574 00:46:14.280 --> 00:46:16.679 I think, yes, I heard, But I think when I talk about 575 00:46:17.280 --> 00:46:22.880 it's one of the challenges when I talk about categorical data, it's such an 576 00:46:22.360 --> 00:46:27.320 esoteric term that a lot of people are like, Yeah, what's the big 577 00:46:27.360 --> 00:46:35.199 deal. What we're talking about is users, entities IP addresses. It's talking 578 00:46:35.239 --> 00:46:40.760 out about user and entity behavior. That's exactly what it is. And being 579 00:46:40.800 --> 00:46:47.440 able to analyze that in real time as the data appears right as opposed to 580 00:46:49.960 --> 00:46:57.599 excuse me having to transform that data into something numeric and then run an algorithm 581 00:46:57.639 --> 00:47:00.480 across the numeric data, which is huge, and it takes forever and then 582 00:47:00.599 --> 00:47:06.199 and then I can eventually figure it out, maybe a few hours later, 583 00:47:06.360 --> 00:47:12.000 a few days later. The user and entity behavior that happens, now I 584 00:47:12.039 --> 00:47:16.559 can immediately analyze it. And that that is the power of streaming graph. 585 00:47:16.840 --> 00:47:25.239 Is the relationships the entities, the actions the people doing a thing. Is 586 00:47:25.559 --> 00:47:35.119 what a graph is designed to represent, is entities behaving. If that makes 587 00:47:35.159 --> 00:47:38.119 sense, I have alreadys heard it as iob as opposed to the acronym that 588 00:47:38.159 --> 00:47:43.280 the user used. But it's the same thing. It's that idea of being 589 00:47:43.599 --> 00:47:49.840 to analyze categorical data without transforming it. First. Well, now that I 590 00:47:49.840 --> 00:47:52.519 could speak to some of that, maybe that if you can hear me, 591 00:47:52.559 --> 00:47:55.480 all right, But the behavior, the user behavior, all that stuff is 592 00:47:55.519 --> 00:47:59.800 also known as heuristics, I think. And they've been doing that, let's 593 00:47:59.840 --> 00:48:05.119 say, the world's fastest database in memory. What they do a lot of 594 00:48:05.119 --> 00:48:08.400 that. That's how they figure out how to customize ads for you based on 595 00:48:08.599 --> 00:48:15.800 what you lingered on with your microspike. I'm writing a technical book with Riley 596 00:48:15.840 --> 00:48:20.800 on that with the air so aerospike exactly. So. I mean they're one 597 00:48:20.840 --> 00:48:25.320 of the first in memory of flash kind of outfits. But that whole idea 598 00:48:25.440 --> 00:48:29.280 of Okay, the guy is mousing over this, we're going to feed him 599 00:48:29.280 --> 00:48:31.079 this ad. Well, that applies also to hate so and so is trying 600 00:48:31.119 --> 00:48:35.760 to charge so and so's card for something that so and so doesn't you know 601 00:48:35.800 --> 00:48:40.159 there are four times in California using my credit card or something, and I 602 00:48:40.199 --> 00:48:44.280 know that's been done, but you guys are making it more accessible. I 603 00:48:44.320 --> 00:48:50.400 think that's I think I don't know about accessibility. I think speed is really 604 00:48:50.440 --> 00:48:55.159 our secret secret sauce. I think it's because it's a young technology. It 605 00:48:55.159 --> 00:49:00.199 doesn't have a beautiful ter interface. It has one and you can actually see 606 00:49:00.199 --> 00:49:04.679 what's going on, which I think is I love being able to see what's 607 00:49:04.719 --> 00:49:09.360 happening, and that's marvelous. But I think the the real, the real 608 00:49:09.440 --> 00:49:15.239 key is speed. It's it's the same I mean Aerospike's advantages that they can 609 00:49:15.320 --> 00:49:19.239 do speed at scale. That is what we can do. We can drew 610 00:49:19.320 --> 00:49:25.360 graph analysis at speed at scale, so that pretty much gives you a different 611 00:49:25.440 --> 00:49:32.199 kind of capability of event streaming and you know, you might stream the event 612 00:49:32.320 --> 00:49:37.960 into aerospike and then do something interesting with it there. It's like that this 613 00:49:37.079 --> 00:49:42.559 is the same kind of technology. I think progression that's happening a lot. 614 00:49:43.000 --> 00:49:45.719 Is it used to be. You know, it's might to you days to 615 00:49:45.159 --> 00:49:49.039 to do a query. It might take you, you know, once a 616 00:49:49.079 --> 00:49:52.639 week to update your data, and then you're you're you're lucky if you're you 617 00:49:52.639 --> 00:49:57.840 know, you were really uh snazzy if your dashboards were only a day old. 618 00:49:58.400 --> 00:50:04.679 And it's like now if it's becoming it's like that took you ten seconds 619 00:50:04.760 --> 00:50:09.480 down. That's slow. I know, it's like there's there's very This is 620 00:50:09.519 --> 00:50:15.239 a very different world and we're trying to accomplish some things that that require that 621 00:50:15.000 --> 00:50:20.880 upper level, that that next gen capability, and I think streaming graph is 622 00:50:20.920 --> 00:50:23.639 a really good example of that. We've just taken it to the next level. 623 00:50:24.119 --> 00:50:30.199 Yeah. Well, you know Aerospike a spike was handling streaming use cases 624 00:50:30.400 --> 00:50:35.480 in key value and then now divided graph. So I can see that how 625 00:50:35.559 --> 00:50:42.639 this technology is becoming the de facto standard. Yeah. I think I'm going 626 00:50:42.679 --> 00:50:45.400 to have to tell the Aerospike guys that they got to mention on the ship 627 00:50:45.599 --> 00:50:51.159 all right, So I was going to say one more thing is fine because 628 00:50:51.199 --> 00:50:54.280 it's speedy and it's smart. Is that under the hood at Talentier or how 629 00:50:54.280 --> 00:50:58.960 do they tell us about bad guys? You know, starting a Chinese leaving 630 00:50:58.960 --> 00:51:01.480 the dog and the same for Hawaii, you know, like, yeah, 631 00:51:01.599 --> 00:51:06.639 I don't I can't talk all about most of them. But we're public with 632 00:51:06.679 --> 00:51:09.599 CrowdStrike. I mean, at this point, we've got one of the largest 633 00:51:10.159 --> 00:51:15.679 corporations in the world is embedding our software. Uh, we just got some 634 00:51:16.000 --> 00:51:23.840 funding from a US military branch. There's a lot of things going on that 635 00:51:24.519 --> 00:51:29.840 a lot of them are allowed to talk about. I mean pretty much all 636 00:51:29.840 --> 00:51:34.800 I can tell you, folks. Look all these folks up online that dot. 637 00:51:34.880 --> 00:51:37.639 I love the story behind that dot, by the way, connecting all 638 00:51:37.679 --> 00:51:40.519 the dots. That dot is an IP address, that dot is a user. 639 00:51:42.360 --> 00:51:45.079 Look these guys up online that dot streaming graph of course, Sanjif Mohan 640 00:51:45.199 --> 00:51:49.679 of Sangmo and h Alex Husky thank you for your time for wanting to be 641 00:51:49.679 --> 00:51:52.559 in the show. Send me an email info at inside Analysis dot com. 642 00:51:52.599 --> 00:51:54.679 This does conclude our program for the day. What a fantastic show. We'll 643 00:51:54.679 --> 00:51:59.800 talk you next time. Folks. Take care by miss something today, yesterday, 644 00:52:00.119 --> 00:52:05.559 last week. Check out our podcasts at WWWKCAA radio dot com. We 645 00:52:05.679 --> 00:52:12.519 leave no listener behind. Redlands. Ranch Market is a unique, full surface 646 00:52:12.559 --> 00:52:16.760 international grocery store that specializes in authentic food items from Mexico, India, and 647 00:52:16.880 --> 00:52:22.719 from many Mediterranean and Asian countries, including popular items from the US. 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Right here at CACAA, that station that leaves 707 00:57:37.920 --> 00:57:46.440 no listener behind. NBC News Radio, I'm Chris Garragio. The latest round 708 00:57:46.519 --> 00:57:52.760 of talks aimed at a ceasefire in Gaza stalled today. Correspondent Megan Fitzgerald has 709 00:57:52.800 --> 00:57:58.119 more on developments in Cairo. We heard from Prime Minister Benjamin Netanyahu today doubling 710 00:57:58.199 --> 00:58:02.079 down, essentially saying that ending this war is off the table. As we 711 00:58:02.159 --> 00:58:07.360 also know that he said that deal or no deal, that planned ground incursion 712 00:58:07.480 --> 00:58:13.079 into Rafa will be happening. Both Israel and Hamas trade a blame over the 713 00:58:13.079 --> 00:58:16.280 impass, which came despite word that negotiators were close to a breakthrough. CIA 714 00:58:16.360 --> 00:58:21.800 Director Bill Burns is reportedly traveling to cutter as the US continues to push for 715 00:58:21.920 --> 00:58:24.920 progress. Negotiators were said to have been working on a new framework involving the 716 00:58:24.960 --> 00:58:30.320 release of hostages kidnapped from Israel in exchange for a pause in the fighting. 717 00:58:30.639 --> 00:58:36.360 UCLA is resuming regular operations starting tomorrow. The university announced today faculty and staff 718 00:58:36.360 --> 00:58:39.519 will be able to resume in person classes, but did leave the option to 719 00:58:39.639 --> 00:58:44.719 conduct them online until May tenth. Officials also said the law school would still 720 00:58:44.719 --> 00:58:50.280 hold exams as regularly scheduled. The announcement recommended people avoid a roy squad given 721 00:58:50.280 --> 00:58:53.480 the cleanup efforts that are underway there. The university will maintain a police presence 722 00:58:53.519 --> 00:58:59.960 on campus. South Dakota Governor Christy Nome continues to face criticism over details of 723 00:59:00.039 --> 00:59:04.519 her upcoming memoir. Appearing on CBS's Face the Nation, Nome faced a grilling 724 00:59:04.639 --> 00:59:08.760 over controversial claims that include having met North Korean leader Kim Jong un. As 725 00:59:08.760 --> 00:59:13.800 soon as it was brought to my attention, we went forward and have made 726 00:59:13.800 --> 00:59:16.400 some edits. So I'm glad that this book is being released in a couple 727 00:59:16.400 --> 00:59:20.880 of days and that those edits will be in place and that people will will 728 00:59:20.880 --> 00:59:24.000 have the updated version. The Republican governor refused to say directly whether she met 729 00:59:24.039 --> 00:59:29.400 with the dictator. She also defended a passage in the book where she details 730 00:59:29.480 --> 00:59:32.400 how she shot and killed her dog, calling it a dangerous animal that was 731 00:59:32.480 --> 00:59:37.199 killing livestock. Nome was considered to be on Donald Trump's shortlist for vice president. 732 00:59:37.360 --> 00:59:45.760 I'm Chris Karragio, NBC News Radio, NBC News on CACAA Lowel sponsored 733 00:59:45.760 --> 00:59:51.199 by Teamsters Local nineteen thirty two, protecting the Future of Working Families Teamsters nineteen 734 00:59:51.239 --> 01:00:00.239 thirty two dot org. You're listening to an encore presentation of this program k 735 01:00:00.440 --> 01:00:06.760 C A A The Inland Talk Express. Thank you for tuning in for this 736 01:00:06.960 --> 01:00:08.400 edition of Justice Watch with a Turn