1 00:00:10,960 --> 00:00:13,720 Hello everybody and welcome back to the Decipher Podcast. 2 00:00:13,720 --> 00:00:16,000 I'm Dennis Fisher. I'm pleased today to have 3 00:00:16,000 --> 00:00:19,640 Shravish, the CEO and Co founder of Trust Cloud with me. 4 00:00:19,640 --> 00:00:22,520 How are you today, Shravish? Dennis, I'm doing great. 5 00:00:22,520 --> 00:00:25,600 Great to meet a fellow Bostonian and really happy and thankful 6 00:00:25,600 --> 00:00:27,400 for you to inviting me on the podcast. 7 00:00:27,640 --> 00:00:29,440 Yeah, absolutely. It's my pleasure. 8 00:00:29,720 --> 00:00:32,400 It's always nice to have another Bostonian on the podcast, 9 00:00:32,400 --> 00:00:34,640 although I'm, I'm not in the city. 10 00:00:34,640 --> 00:00:37,600 You're closer than I am. But we'll we'll definitely meet 11 00:00:37,600 --> 00:00:40,360 up in person one of these days. Once the weather actually gets 12 00:00:40,360 --> 00:00:45,200 nice here, we can get that done. It's beautiful and it's the mid 13 00:00:45,200 --> 00:00:46,200 60s. I'll take it. 14 00:00:46,600 --> 00:00:49,600 Yeah, this is this is our one week of spring that we get in 15 00:00:49,600 --> 00:00:52,000 New England. So then it just goes straight 16 00:00:52,000 --> 00:00:55,000 into summer. That's just how it works here. 17 00:00:55,560 --> 00:00:59,360 So I wanted to start a little bit with your your background, 18 00:00:59,360 --> 00:01:02,480 if you don't mind, and tell me a little bit about how you sort of 19 00:01:02,480 --> 00:01:06,360 got to where you are. Did you were you like a tech kid 20 00:01:06,360 --> 00:01:08,520 growing up? Were you a a computer nerd? 21 00:01:08,520 --> 00:01:10,920 Like I kind of was, but not completely. 22 00:01:10,920 --> 00:01:15,280 But I'm always interested to see how people, you know what, what 23 00:01:15,280 --> 00:01:18,160 entry point they had to this to this career. 24 00:01:19,240 --> 00:01:21,400 At Dennis, I'm a fifth generation entrepreneur. 25 00:01:21,560 --> 00:01:25,440 I guess I followed a family family tradition of starting 26 00:01:25,440 --> 00:01:28,080 companies. And the interesting thing in my 27 00:01:28,080 --> 00:01:32,200 family is every generation really hated what the previous 28 00:01:32,200 --> 00:01:35,600 generation did. And so instead of taking on the 29 00:01:35,600 --> 00:01:38,200 family business, we ended up starting our own thing in a 30 00:01:38,240 --> 00:01:42,240 business and in an area that was very different than anything 31 00:01:42,240 --> 00:01:43,640 that the previous generations did. 32 00:01:43,640 --> 00:01:46,560 And so there isn't a technology bone in my family. 33 00:01:46,560 --> 00:01:48,280 I did not grow up with computers. 34 00:01:48,280 --> 00:01:49,720 I did not grow up with technology. 35 00:01:49,720 --> 00:01:54,040 I grew up in in India. And as a 17 year old kid, I 36 00:01:54,040 --> 00:01:58,520 decided to come to Texas, which is if you put your finger on the 37 00:01:58,520 --> 00:02:01,840 globe on one side and you put your finger on the other side. 38 00:02:02,040 --> 00:02:04,880 Austin, TX is exactly the opposite side of the world that 39 00:02:04,880 --> 00:02:06,240 I grew. Up was going to say yeah. 40 00:02:07,160 --> 00:02:11,000 And so I ended up in Texas and they asked me what I wanted to 41 00:02:11,000 --> 00:02:13,440 study and there was this thing called computers that was taking 42 00:02:13,440 --> 00:02:16,480 off in the mid 90s. And so I said computer science 43 00:02:16,480 --> 00:02:21,920 and then I became a nerd where it was actually in college that 44 00:02:21,920 --> 00:02:24,000 I got my first computer and started coding. 45 00:02:24,000 --> 00:02:28,040 And since then I've built 2 businesses that were venture 46 00:02:28,040 --> 00:02:30,440 backed with successful exits and now I'm doing my third one. 47 00:02:31,200 --> 00:02:34,680 Oh my gosh, what a, that's a really boiled down version, I'm 48 00:02:34,680 --> 00:02:36,840 sure of what's a, a, a very cool story. 49 00:02:36,840 --> 00:02:40,360 But I've met a lot of people in this industry and others that 50 00:02:40,360 --> 00:02:43,880 are, you know, second or third or fourth generation in a 51 00:02:43,880 --> 00:02:45,840 business. But usually it's a business that 52 00:02:46,080 --> 00:02:50,040 was started by those older generations, not like 5 53 00:02:50,040 --> 00:02:53,720 generations starting their own business because everybody else 54 00:02:53,720 --> 00:02:55,600 hated what the other ones were. That's right. 55 00:02:55,680 --> 00:02:58,800 My parents were extremely successful entrepreneurs in the 56 00:02:58,800 --> 00:03:01,720 shipping industry. They ran the largest container 57 00:03:01,720 --> 00:03:03,360 feeder operator in the Bay of Bengal. 58 00:03:04,200 --> 00:03:08,680 And I grew up as a kid around ports, and I saw my fair share 59 00:03:08,680 --> 00:03:13,480 of container terminals and ships and how that entire business 60 00:03:13,480 --> 00:03:14,920 worked. And after I got into that, I 61 00:03:14,920 --> 00:03:17,160 said I'd never want to do anything with shipping for the 62 00:03:17,160 --> 00:03:23,360 rest of my life. It seems extremely complex and 63 00:03:24,440 --> 00:03:28,280 labor intensive in just like a million things could go wrong at 64 00:03:28,280 --> 00:03:30,240 any time. It is, but it's still the 65 00:03:30,240 --> 00:03:33,000 lifeblood of the world, right? Like with things that are going 66 00:03:33,000 --> 00:03:36,400 on in the, in the current political climate that we're in, 67 00:03:36,400 --> 00:03:39,200 with the state of Hormuz and everything like that, one sliver 68 00:03:39,200 --> 00:03:43,360 of ocean water is affecting the entire world economy. 69 00:03:43,360 --> 00:03:47,480 So shipping is still an integral part of how we operate and run 70 00:03:47,480 --> 00:03:50,800 our day-to-day lives. But to your point, it's it's one 71 00:03:50,800 --> 00:03:52,600 of those things that have existed for a couple of 72 00:03:52,600 --> 00:03:55,840 millennia. Someone still has to do it, but 73 00:03:55,840 --> 00:03:58,000 it's hard work. It's really true. 74 00:03:58,000 --> 00:04:01,160 It's one of those things where you don't think about it very 75 00:04:01,160 --> 00:04:04,440 much unless, you know, like you, you were steeped in and you grew 76 00:04:04,440 --> 00:04:06,720 up with it. But you think about it. 77 00:04:06,720 --> 00:04:11,800 If say you go to the store and there aren't any, you know, pick 78 00:04:11,800 --> 00:04:15,160 a product that day, you're like, oh, that's because this ship 79 00:04:15,160 --> 00:04:18,920 didn't get to where it needed to go 4 weeks ago because there was 80 00:04:18,920 --> 00:04:22,800 a storm in the South Pacific. And that's why you don't have, 81 00:04:23,160 --> 00:04:25,560 you know, laundry detergent today. 82 00:04:25,600 --> 00:04:29,880 That's, you know, it's, that's a whole long supply chain that we 83 00:04:29,880 --> 00:04:32,840 deal with in software too. It's a different thing, but same 84 00:04:32,840 --> 00:04:34,520 sort of thing. One little thing goes wrong and 85 00:04:34,520 --> 00:04:36,880 it has these downstream effects for a very long time. 86 00:04:37,640 --> 00:04:39,840 And, and I think the way you described it is very apartment 87 00:04:39,840 --> 00:04:42,520 because even though I didn't like to get into the shipping 88 00:04:42,520 --> 00:04:47,800 industry, the thing I really absorbed through osmosis of just 89 00:04:47,800 --> 00:04:51,560 observing how the industry worked is how distributed but 90 00:04:51,560 --> 00:04:56,280 yet connected that industry was. And so as a computer scientist, 91 00:04:56,640 --> 00:05:00,520 I kept thinking about this problem of distributed but yet 92 00:05:00,520 --> 00:05:04,920 connected networks and how you can build businesses on top of 93 00:05:04,960 --> 00:05:08,560 that level of complexity. Because if you can solve for 94 00:05:08,560 --> 00:05:13,000 that distributed yet connected complexity, then you're solving 95 00:05:13,000 --> 00:05:14,880 a really hard problem that everybody's struggling with. 96 00:05:14,880 --> 00:05:19,840 So all three of my startups have that as a common theme across 97 00:05:19,840 --> 00:05:23,880 each idea. That's fascinating because those 98 00:05:24,360 --> 00:05:28,640 that distributed complexity problem is one of those computer 99 00:05:28,640 --> 00:05:32,040 science problems and networking problems that people have been 100 00:05:32,040 --> 00:05:36,520 trying to solve since we've had networks and computers, you 101 00:05:36,520 --> 00:05:40,280 know, and there's different parts of it and it's nobody's 102 00:05:40,280 --> 00:05:42,160 going to solve the problem itself. 103 00:05:42,560 --> 00:05:46,600 But it's, it's a long standing computer science and networking 104 00:05:46,600 --> 00:05:50,560 issue that especially people in the security world are still 105 00:05:50,560 --> 00:05:53,880 trying to tackle, obviously from many different angles. 106 00:05:54,960 --> 00:05:58,960 So when, what was the going back a little bit, what was the 107 00:05:58,960 --> 00:06:03,400 culture shock like when you got to Austin, TX from India? 108 00:06:03,400 --> 00:06:06,560 I just, I was in Austin last week, as a matter of fact, just 109 00:06:06,680 --> 00:06:09,440 just by chance. And it's a place I love, but it 110 00:06:09,520 --> 00:06:11,880 is not. I can't imagine it's very 111 00:06:11,880 --> 00:06:14,600 similar to India. You want the R rated version or 112 00:06:14,600 --> 00:06:15,840 do you want the PG rated version? 113 00:06:15,880 --> 00:06:17,400 What kind of what kind of podcast? 114 00:06:17,560 --> 00:06:19,560 This is this is not a family podcast. 115 00:06:21,360 --> 00:06:23,040 Well, you have to put this in context, right? 116 00:06:23,040 --> 00:06:25,360 So this was the mid 90s. I've never travelled outside the 117 00:06:25,360 --> 00:06:30,680 country and I'd never been to the US, And my entire perception 118 00:06:30,680 --> 00:06:34,720 of the US was based around TV shows that I saw, you know, 119 00:06:34,720 --> 00:06:38,840 things from Doogie Howser and The Wonder Years to, Oh my God, 120 00:06:38,840 --> 00:06:41,600 Knight Rider and The A-Team and like, stuff like that, right? 121 00:06:41,600 --> 00:06:44,320 And so I didn't know what to expect when I came to this 122 00:06:44,320 --> 00:06:47,080 country. And I got dropped in Austin, TX 123 00:06:48,640 --> 00:06:51,360 end of August when it was the height of the summer. 124 00:06:52,240 --> 00:06:55,120 And you're walking around Austin, TX for the first time on 125 00:06:55,120 --> 00:07:04,640 campus and you see young men and women who look like Greek gods 126 00:07:04,640 --> 00:07:07,400 and goddesses. And they're just walking around. 127 00:07:07,400 --> 00:07:11,000 And there is a huge party culture. 128 00:07:11,000 --> 00:07:14,760 There's, there's still, you know, a lot of innovation, 129 00:07:14,760 --> 00:07:17,240 technology, academia that is happening. 130 00:07:17,240 --> 00:07:21,400 But if there's an equal amount of partying and drinking and 131 00:07:21,400 --> 00:07:23,560 going to football games and like all of these things. 132 00:07:23,560 --> 00:07:27,120 And so there was a rude shock for me on both ends, 1, the 133 00:07:27,120 --> 00:07:30,560 rigor of academia was both inspiring and intimidating. 134 00:07:31,400 --> 00:07:36,000 And then in the other, how much fun people are having was 135 00:07:36,000 --> 00:07:39,360 absolutely phenomenal. In fact, a quick story on how I 136 00:07:39,360 --> 00:07:43,400 ended up in Texas. It was based on advice I had 137 00:07:43,400 --> 00:07:45,440 from a family friend of my dad's. 138 00:07:45,440 --> 00:07:49,560 He said now that you've decided to pick, you want to study 139 00:07:49,560 --> 00:07:52,320 computer science. Here's the top 25 computer 140 00:07:52,320 --> 00:07:54,080 science schools in the US. Sure. 141 00:07:54,920 --> 00:07:57,720 And then he pulled out a Playboy magazine and he said, here's the 142 00:07:57,720 --> 00:08:01,280 top 25 party schools in the US And I created a data-driven 143 00:08:01,280 --> 00:08:06,440 decision before it was data science was a thing of picking 144 00:08:06,440 --> 00:08:08,920 the intersection of that list. And Texas was the number one on 145 00:08:08,920 --> 00:08:10,600 that list. And when you combine those two 146 00:08:10,600 --> 00:08:13,360 lists, and that's why I applied to that one school and I got in 147 00:08:13,360 --> 00:08:16,160 and. I was going to say that the 148 00:08:16,160 --> 00:08:18,880 overlap of those two lists is probably 1 school. 149 00:08:19,200 --> 00:08:21,120 It was. It was probably UTI. 150 00:08:21,960 --> 00:08:25,280 Think there was a Florida some like something, but yes, those 151 00:08:25,480 --> 00:08:27,880 basically there was, I think there was 2 schools there. 152 00:08:27,960 --> 00:08:29,360 UT was by far the full one, yeah. 153 00:08:30,000 --> 00:08:31,240 It makes sense. Yeah. 154 00:08:31,840 --> 00:08:38,400 And it's it's funny because it Austin like is such AI mean not 155 00:08:38,400 --> 00:08:41,240 to turn this into some kind of Texas podcast, but like it's a 156 00:08:41,640 --> 00:08:45,040 college town. It's a very like weird creative 157 00:08:45,040 --> 00:08:47,960 arts town, but it's also the capital of Texas. 158 00:08:48,280 --> 00:08:51,600 So it has like all of these weird kind of overlapping 159 00:08:51,600 --> 00:08:56,840 cultures that, you know, get along in some cases and don't 160 00:08:56,840 --> 00:08:59,080 always get along in other cases. Yeah. 161 00:08:59,120 --> 00:09:02,480 In fact, when I when I landed in Austin, the theme in Austin at 162 00:09:02,480 --> 00:09:05,880 the time was the slogan for Austin, if you will, at the time 163 00:09:06,400 --> 00:09:08,440 was Keep Austin Weird. Yeah, I think it. 164 00:09:08,440 --> 00:09:12,120 Stole that and we all had T-shirts that said that and we 165 00:09:12,120 --> 00:09:14,160 wore it and we wore it proudly. Yeah. 166 00:09:14,640 --> 00:09:18,280 So you, you mentioned that was the first time you got a 167 00:09:18,280 --> 00:09:21,480 computer was when you got to to college. 168 00:09:21,760 --> 00:09:25,040 Yeah, my, my, my Texas e-mail was the first e-mail address I'd 169 00:09:25,040 --> 00:09:26,720 ever gotten. Oh my gosh. 170 00:09:27,000 --> 00:09:30,000 I never personally. I never owned a computer 171 00:09:30,000 --> 00:09:34,480 personally till I think I was almost 30 years old. 172 00:09:34,600 --> 00:09:37,320 I've always either had university computers or I've had 173 00:09:37,400 --> 00:09:38,920 work computers. I've never had a personal 174 00:09:38,920 --> 00:09:41,400 computer of mine ever in my life till I was about 30 years old. 175 00:09:41,800 --> 00:09:45,480 Wow. And all my computer time was in 176 00:09:45,480 --> 00:09:49,800 the labs on campus till 2:00 AM and 3:00 AM. 177 00:09:49,880 --> 00:09:51,120 Trying to figure this whole thing out. 178 00:09:52,000 --> 00:09:56,920 So what were the when you decided to study computer 179 00:09:56,920 --> 00:10:01,080 science, was there anything specific about it that appealed 180 00:10:01,080 --> 00:10:03,240 to you? Was it just sort of the way that 181 00:10:03,240 --> 00:10:07,120 your mind works, like kind of the analytical, you know, I, I 182 00:10:07,120 --> 00:10:09,120 feel like this is something I'd be good at, or was there 183 00:10:09,120 --> 00:10:10,520 something else that really appealed to you? 184 00:10:11,000 --> 00:10:13,360 It was, I mean, I, I was both a computer science and a math 185 00:10:13,360 --> 00:10:15,880 major. Math was my strong skill set, 186 00:10:15,880 --> 00:10:17,800 continues to be a strong skill set of mine. 187 00:10:17,800 --> 00:10:22,760 And applied mathematics was, was, was a field that a lot of 188 00:10:22,760 --> 00:10:24,280 people were looking at at the time. 189 00:10:24,280 --> 00:10:27,240 And computers was the way that they were expressing modern 190 00:10:27,240 --> 00:10:29,480 applied mathematics. And so that was the the 191 00:10:29,480 --> 00:10:31,960 combination of getting a math degree and a computer science 192 00:10:31,960 --> 00:10:37,120 degree at the same time. I didn't think I was going to 193 00:10:37,120 --> 00:10:40,240 become wholly in the computer science space. 194 00:10:40,600 --> 00:10:42,760 I thought math was going to be my calling when I was younger. 195 00:10:42,760 --> 00:10:47,000 But here we are, more money. More money in computer science 196 00:10:47,000 --> 00:10:48,960 and math. Yeah, I was going to say the the 197 00:10:48,960 --> 00:10:53,320 career paths for a mathematician are pretty limited unless you're 198 00:10:53,320 --> 00:10:54,120 really. Good. 199 00:10:54,120 --> 00:10:55,920 And I wasn't that good. Right. 200 00:10:55,960 --> 00:11:00,160 I, I feel like you're either in academia or you get some sort of 201 00:11:00,160 --> 00:11:04,240 applied math job at, you know, a defense contractor or something 202 00:11:04,240 --> 00:11:05,880 along those lines and, you know, a. 203 00:11:06,080 --> 00:11:07,840 Lot of my friends ended up at banks. 204 00:11:08,320 --> 00:11:12,800 This was also the height of the.com era and so, you know, 205 00:11:12,800 --> 00:11:15,640 all the big banks were hiring people from my cohort of 206 00:11:15,800 --> 00:11:19,920 mathematics and statistics majors to just come and applied 207 00:11:19,920 --> 00:11:23,160 quantum to quant, become quad jocks essentially in all the 208 00:11:23,160 --> 00:11:27,600 banks, which is also a big, big, big career move for those folks. 209 00:11:28,040 --> 00:11:29,880 And all those people are running hedge funds now. 210 00:11:30,200 --> 00:11:33,400 They are. Some of them are managing 211 00:11:33,480 --> 00:11:37,160 hundreds of billions of dollars. One of them is managing a number 212 00:11:37,160 --> 00:11:40,680 that starts with with T, So it's, yeah, God, some pretty 213 00:11:40,680 --> 00:11:42,840 significant roles. That's mind bending. 214 00:11:43,120 --> 00:11:48,040 Jeez. So what made you, when you not 215 00:11:48,040 --> 00:11:50,480 thinking that you were going to be an entrepreneur or you 216 00:11:50,480 --> 00:11:53,040 thought you were going to be a mathematician, what what was it 217 00:11:53,040 --> 00:11:55,240 just sort of the family heritage that led you down the 218 00:11:55,240 --> 00:11:56,920 entrepreneurial path after school? 219 00:11:57,680 --> 00:11:59,760 I'm always yeah, that's exactly it. 220 00:11:59,760 --> 00:12:02,320 You know, I think whether you like it or not, your what your 221 00:12:02,320 --> 00:12:05,120 parents and your family does does rub off, rub off on most of 222 00:12:05,120 --> 00:12:07,640 us. We are formed by our experiences 223 00:12:07,640 --> 00:12:12,800 as children, both good and bad. And I think what I saw in my 224 00:12:12,800 --> 00:12:16,480 parents was a deep passion for what they did. 225 00:12:16,640 --> 00:12:19,440 You know, when they truly love something and made it their 226 00:12:20,440 --> 00:12:24,400 life's mission, mission to build that thing, there's a lot of 227 00:12:24,400 --> 00:12:27,080 satisfaction in in what they did. 228 00:12:27,080 --> 00:12:30,480 It never felt like work. It felt like more a passion and 229 00:12:30,480 --> 00:12:32,400 yet a responsibility. Sure. 230 00:12:33,280 --> 00:12:38,800 And it just so happened that during my undergraduate years I 231 00:12:38,800 --> 00:12:41,880 was working with, one of the professors I was working with 232 00:12:41,880 --> 00:12:45,120 was a very famous professor in the world of distributor 233 00:12:45,120 --> 00:12:47,400 computing, a gentleman by the name of Doctor Jim Brown. 234 00:12:48,400 --> 00:12:54,000 And he introduced me to this concept of SETI at home, which 235 00:12:54,000 --> 00:12:58,120 was a screensaver that people were putting on their PCs. 236 00:12:58,560 --> 00:13:00,040 That's a throwback. Wow to. 237 00:13:01,200 --> 00:13:05,240 To try to find if there was extraterrestrial life in the 238 00:13:05,240 --> 00:13:08,960 universe. And I got introduced to a couple 239 00:13:08,960 --> 00:13:12,600 of entrepreneurs who are thinking of taking that idea and 240 00:13:12,600 --> 00:13:16,080 making it into enterprise software. 241 00:13:17,240 --> 00:13:20,680 And I started working on that project with them. 242 00:13:20,680 --> 00:13:24,560 And we ended up starting a company called United Devices. 243 00:13:25,480 --> 00:13:28,520 And the gentleman who started the City at Home project ended 244 00:13:28,520 --> 00:13:31,560 up being our founding CTO. And so if you've ever run City 245 00:13:31,560 --> 00:13:33,280 at Home, you've run my code. I was. 246 00:13:33,920 --> 00:13:36,320 I. I built this significant part of 247 00:13:36,320 --> 00:13:38,520 that screensaver. That's crazy. 248 00:13:39,000 --> 00:13:42,920 And, and then we then took that and also built another 249 00:13:42,920 --> 00:13:46,680 not-for-profit project to help find a cure for cancer. 250 00:13:46,680 --> 00:13:49,600 So we partnered with the National Foundations of Cancer 251 00:13:49,600 --> 00:13:53,440 Research and the University of Oxford, and we ran a grid. 252 00:13:53,480 --> 00:13:56,640 It was called a grid computer with three and a half million 253 00:13:56,640 --> 00:13:59,600 PCs all over the world networked into one supercomputer so you 254 00:13:59,600 --> 00:14:02,720 could run molecular discovery for Cancer Research. 255 00:14:03,120 --> 00:14:04,880 I remember this. This is. 256 00:14:05,360 --> 00:14:11,360 Yeah, that's crazy, 'cause this was for, there's, believe it or 257 00:14:11,360 --> 00:14:14,320 not, probably people that listen to this, that will not remember 258 00:14:14,320 --> 00:14:18,880 SETI because they're too young. But it it was the search for 259 00:14:18,880 --> 00:14:25,120 Extraterrestrial intelligence and essentially was a yeah, it 260 00:14:25,120 --> 00:14:28,320 was a I'll let you describe it. But it was using distributed 261 00:14:28,320 --> 00:14:31,840 computing power among millions of people around the world to do 262 00:14:31,840 --> 00:14:36,040 little essentially like micro problems, right, to like look 263 00:14:36,040 --> 00:14:39,600 for to crunch data. This is before we had all the 264 00:14:39,600 --> 00:14:42,680 compute power we have now. But yeah, we screensaver the. 265 00:14:42,840 --> 00:14:45,200 Next was you had this big satellite called the Arecibo 266 00:14:45,200 --> 00:14:47,840 satellite, which was bringing signals from outer space. 267 00:14:47,840 --> 00:14:52,920 And most signals from outer space have no patterns to it 268 00:14:53,000 --> 00:14:56,240 only, only artificial signals will have patterns to it. 269 00:14:56,240 --> 00:14:59,280 And So what we're essentially doing is we're analyzing every 270 00:14:59,280 --> 00:15:02,160 signal coming from outer space to find if there's any patterns 271 00:15:03,200 --> 00:15:07,760 in the signals which become a hint that they're being 272 00:15:07,760 --> 00:15:12,000 generated by an artificial life form or an extraterrestrial life 273 00:15:12,000 --> 00:15:16,520 form rather. And so you needed computing 274 00:15:16,520 --> 00:15:19,280 capacity that didn't exist on the planet to do that analysis. 275 00:15:19,280 --> 00:15:21,880 And that's why we built this idea of a screensaver that 276 00:15:22,440 --> 00:15:24,120 people downloaded. At one point, I think we had 277 00:15:24,120 --> 00:15:27,640 about 750,000 machines operating concurrently on this. 278 00:15:29,160 --> 00:15:32,560 And then we took it up a notch with our company where we had 279 00:15:32,560 --> 00:15:34,880 one part of our business which was selling this concept to 280 00:15:34,880 --> 00:15:38,120 enterprises to deploy inside their enterprises to aggregate 281 00:15:38,120 --> 00:15:40,520 all the computing capacity inside their enterprise for 282 00:15:41,680 --> 00:15:44,720 confidential research. And then we had this public grid 283 00:15:44,720 --> 00:15:48,000 of three and a half million PCs that we're doing a lot of 284 00:15:48,000 --> 00:15:53,960 not-for-profit research with academia as well as we even for 285 00:15:54,160 --> 00:16:00,600 at one time we even took all of the micro fish, journalist data, 286 00:16:00,600 --> 00:16:04,840 newspaper articles, etcetera. And we converted into digital 287 00:16:04,840 --> 00:16:10,200 form using this network. We did denial of service attack 288 00:16:10,200 --> 00:16:15,280 testing, we did encryption, cracking all sorts of use cases 289 00:16:15,280 --> 00:16:17,840 right on that public grid. Yeah, it's funny. 290 00:16:17,840 --> 00:16:21,360 Those were you just described essentially the three or four 291 00:16:21,360 --> 00:16:24,120 main use cases for the grid computing, which was going to be 292 00:16:24,120 --> 00:16:29,000 the next big thing, you know, before the cloud, you know, 293 00:16:29,800 --> 00:16:31,640 Yeah. There's a big part of me that 294 00:16:31,720 --> 00:16:37,720 that that is disappointed that I didn't come up with the phrase 295 00:16:37,720 --> 00:16:41,120 cloud computing because, because this whole time as we were 296 00:16:41,120 --> 00:16:44,640 building this company, every time we would draw a market 297 00:16:44,640 --> 00:16:47,960 extra diagram on the board or in a slide, there would always be a 298 00:16:47,960 --> 00:16:49,320 cloud in the diagram. Yep. 299 00:16:50,280 --> 00:16:53,920 But we focused on the interconnectedness AKA the grid, 300 00:16:53,960 --> 00:16:56,440 but we didn't focus on this cloud that we were trying to 301 00:16:56,440 --> 00:16:59,440 call it cloud computing. I think you're in good company. 302 00:16:59,440 --> 00:17:02,400 There are people that wish they had come up with the idea of or 303 00:17:02,400 --> 00:17:05,880 the phrase cloud computing. Yeah, I, I remember that very 304 00:17:05,880 --> 00:17:10,640 clearly in when I was at a tech magazine in the early 2000s, 305 00:17:10,920 --> 00:17:14,040 Literally every whiteboard diagram of some somebody coming 306 00:17:14,040 --> 00:17:17,640 in to pitch us, you know, it had a cloud here and just a bunch of 307 00:17:17,640 --> 00:17:19,400 arrows. And then like little, you know, 308 00:17:19,560 --> 00:17:22,640 PC icons or something, you're just like, OK, I get it, that's 309 00:17:22,640 --> 00:17:25,640 the Internet up there. But now, yeah, now it's just 310 00:17:25,760 --> 00:17:29,200 Amazon or Google or IBM or whatever it happens to be. 311 00:17:29,600 --> 00:17:32,520 And in fact, we actually built a precursor to the cloud with a 312 00:17:32,520 --> 00:17:35,600 few telecom companies like British Telecom was was one of 313 00:17:35,600 --> 00:17:38,840 our customers in that company. And we deployed our software 314 00:17:38,840 --> 00:17:43,440 inside the BT network with their data centers and created on 315 00:17:43,440 --> 00:17:48,280 demand computed storage for BT customers where they could click 316 00:17:48,280 --> 00:17:54,200 a button and say I want so many servers and databases storage 317 00:17:54,200 --> 00:17:55,560 and so on. And then they would get that 318 00:17:55,560 --> 00:17:57,120 provision that they would get access to it. 319 00:17:57,320 --> 00:17:59,760 So we built all of that. Again, we didn't call it cloud, 320 00:17:59,760 --> 00:18:01,840 we called it utility computing. Yep. 321 00:18:02,040 --> 00:18:03,400 Utility computing? Yeah, sure. 322 00:18:03,800 --> 00:18:09,000 But but yeah, we, we, we, we, we built that company and then that 323 00:18:09,000 --> 00:18:16,400 company got acquired and then the iPhone was launched and just 324 00:18:16,400 --> 00:18:18,080 grew like a weed all of a sudden. 325 00:18:18,840 --> 00:18:25,040 And my next thesis was enterprises are going to go 326 00:18:25,040 --> 00:18:27,360 through a digital transformation exercise where they're going 327 00:18:27,360 --> 00:18:32,840 from web to mobile and IoT. And, but all this data that they 328 00:18:32,840 --> 00:18:36,600 had all over the enterprise was not designed for this new form 329 00:18:36,600 --> 00:18:40,080 factor. So how do you build essentially 330 00:18:40,080 --> 00:18:44,680 A middleware that connected to legacy authentication and data 331 00:18:44,680 --> 00:18:49,440 related systems and make them available for more real time use 332 00:18:49,440 --> 00:18:54,240 cases, offline use cases, mobile use cases, etcetera? 333 00:18:55,280 --> 00:18:58,240 So again, if you see the theme here, you know, on one end I was 334 00:18:58,320 --> 00:19:01,280 harnessing distributed networks and computers and storage. 335 00:19:01,880 --> 00:19:05,000 And in this scenario I was aggregating distributed data and 336 00:19:05,000 --> 00:19:07,880 identity and making it available for a new form factor. 337 00:19:09,200 --> 00:19:13,840 I am sensing a common theme given that your your current 338 00:19:13,840 --> 00:19:17,560 venture is called Trust Cloud. So where did the where did the 339 00:19:17,560 --> 00:19:21,840 germ for this idea start? Yeah. 340 00:19:21,840 --> 00:19:26,560 So after my previous start up was, was also acquired, I took 341 00:19:26,560 --> 00:19:31,160 some time off and I was reminiscing on that, that 342 00:19:31,160 --> 00:19:35,360 journey of about 15761516 years of building two companies. 343 00:19:36,120 --> 00:19:38,960 And the thing I kept coming back to was a piece of advice that my 344 00:19:38,960 --> 00:19:42,600 parents gave me when I started my first company, which was we 345 00:19:42,600 --> 00:19:44,240 don't know anything about technology. 346 00:19:44,880 --> 00:19:47,680 So we kind of advise you on what it is to build a software 347 00:19:47,680 --> 00:19:51,120 company. But what we will tell you is as 348 00:19:51,120 --> 00:19:53,400 an entrepreneur, the only way you're going to sleep well at 349 00:19:53,400 --> 00:19:56,320 night is if you tell somebody you're going to do something, do 350 00:19:56,320 --> 00:19:59,280 it. And if you're not able to do it, 351 00:19:59,280 --> 00:20:02,960 just be upfront and let them know as quickly as possible and 352 00:20:02,960 --> 00:20:05,440 deal with the consequences. Like don't play any games. 353 00:20:06,400 --> 00:20:11,600 Trust is the only currency you have in the land, in the long 354 00:20:11,600 --> 00:20:13,840 journey of life as well as in in work. 355 00:20:14,320 --> 00:20:16,440 Excellent advice. And I lived that. 356 00:20:16,440 --> 00:20:18,360 I lived that day in and day out. I lived that. 357 00:20:18,360 --> 00:20:19,920 I'd made sense to me. I lived that. 358 00:20:19,920 --> 00:20:25,280 And I think for me, the evidence that that is true is now in my 359 00:20:25,280 --> 00:20:29,520 third company, customers, investors and colleagues from 360 00:20:29,520 --> 00:20:31,800 both my previous companies are not working with me for the 361 00:20:31,800 --> 00:20:34,520 third time, right? So that is like the evidence 362 00:20:34,520 --> 00:20:39,080 that you did it the right way. And so when I was thinking of 363 00:20:39,080 --> 00:20:43,120 that idea, I was asking myself, we have a system of record for 364 00:20:43,120 --> 00:20:49,000 everything in business, but nobody's really built something 365 00:20:49,000 --> 00:20:50,760 that is the system of record for trust. 366 00:20:50,760 --> 00:20:56,240 No one has codified trust as a machine readable and machine 367 00:20:56,240 --> 00:21:01,200 inspectable object and I was intrigued by that concept. 368 00:21:01,200 --> 00:21:05,000 You know, can you codify trust? Can you build an API for trust? 369 00:21:05,000 --> 00:21:06,640 And at the time blockchain was a thing. 370 00:21:06,640 --> 00:21:09,360 Can you build the Ledger for trust? 371 00:21:09,720 --> 00:21:12,800 Yeah. So these were all ideas sort of 372 00:21:12,800 --> 00:21:16,520 swirling in my head, and I talked to a lot of people. 373 00:21:16,960 --> 00:21:18,920 You know, the advantage of building two companies is you 374 00:21:18,920 --> 00:21:22,440 end up building a fairly large network of people from different 375 00:21:22,840 --> 00:21:24,920 backgrounds, experiences, titles, etcetera. 376 00:21:26,960 --> 00:21:28,840 And I would ask them questions like, what does trust mean to 377 00:21:28,840 --> 00:21:30,760 you? What, what, what are your trust 378 00:21:30,760 --> 00:21:33,880 obligations? How do you meet them? 379 00:21:33,880 --> 00:21:35,040 How do you know you're meeting them? 380 00:21:35,640 --> 00:21:38,080 Do you measure them like, and it was I was getting all sorts of 381 00:21:38,080 --> 00:21:42,040 answers across the board. I actually lost my faith in 382 00:21:42,040 --> 00:21:46,000 humanity quite a bit during that that period because people 383 00:21:46,000 --> 00:21:48,640 talked a good game around trust. But then when you actually 384 00:21:48,640 --> 00:21:52,680 understood what they were doing to meet their obligations and 385 00:21:52,680 --> 00:21:54,600 prove they were meeting their obligations was actually not 386 00:21:54,600 --> 00:21:58,920 much and they were kicking the can down the road essentially at 387 00:21:59,160 --> 00:22:03,320 the time. But there was 1 persona that I 388 00:22:03,320 --> 00:22:07,280 found was actually wanting that problem solved. 389 00:22:07,280 --> 00:22:09,680 Most people were willing to like make it someone else's problem 390 00:22:09,680 --> 00:22:12,560 or wait. But there was 1 persona which 391 00:22:12,560 --> 00:22:18,640 was either the CIO or the CSO in a company where this problem of 392 00:22:18,640 --> 00:22:21,880 trust was now becoming front and Center for them across multiple 393 00:22:21,880 --> 00:22:25,440 dimensions. One dimension was audits. 394 00:22:25,840 --> 00:22:30,120 There was more and more pressure on on completing and maintaining 395 00:22:30,600 --> 00:22:33,840 compliance to both regulatory and commercials standards. 396 00:22:34,560 --> 00:22:37,160 Another area was risk. They were buying a lot of 397 00:22:37,160 --> 00:22:39,920 security tools, but they didn't know what risks they were living 398 00:22:39,920 --> 00:22:43,800 on. And, and once he so beautifully 399 00:22:43,800 --> 00:22:45,400 explained it to me, he said looks rubbish. 400 00:22:45,400 --> 00:22:48,640 I've got every security tool you can think of and I'm getting 401 00:22:48,640 --> 00:22:52,400 smoke alarms going off from each one of those tools, but I don't 402 00:22:52,400 --> 00:22:54,480 know where the fire is and I don't know which fire to work 403 00:22:54,480 --> 00:22:56,080 on. Yeah, they don't know how to 404 00:22:56,080 --> 00:22:59,240 prioritize it in in budget. Right, Yeah, yeah. 405 00:22:59,480 --> 00:23:05,280 And then the other area is there's this whole customer side 406 00:23:05,360 --> 00:23:08,920 where if I have a regulator or a customer who's who's demanding 407 00:23:08,920 --> 00:23:12,160 contractually that I will do these things. 408 00:23:12,160 --> 00:23:14,320 And now every, for example, if you look at any customer 409 00:23:14,320 --> 00:23:17,720 contract, it has a security addendum and a data processing 410 00:23:17,720 --> 00:23:19,400 agreement and a privacy addendum. 411 00:23:19,920 --> 00:23:23,040 And now in the last 2-3 years an AI addendum. 412 00:23:24,200 --> 00:23:26,680 So now companies are making contractual commitments that 413 00:23:26,680 --> 00:23:29,280 they're going to do these things from a security, privacy and AI 414 00:23:29,280 --> 00:23:32,360 standpoint. And that is tied to liability 415 00:23:32,360 --> 00:23:36,280 with the contract. So all the Csos were saying my 416 00:23:36,280 --> 00:23:40,120 business is sitting on hundreds of millions if not billions of 417 00:23:40,120 --> 00:23:43,360 dollars of liability because I know for a fact that we're not 418 00:23:43,360 --> 00:23:45,040 doing those things that we've said we're going to. 419 00:23:45,080 --> 00:23:49,200 Do, yeah, yeah, right. So that was a problem. 420 00:23:49,200 --> 00:23:51,920 I was like, that is a distributed computing problem. 421 00:23:52,160 --> 00:23:54,520 I I know how to solve that problem. 422 00:23:54,720 --> 00:23:59,200 So unlike what you may assume, Dennis, I actually could not 423 00:23:59,200 --> 00:24:02,440 spell security or could not spell GRC before I started 424 00:24:02,440 --> 00:24:04,960 Trustcloud. I have no background in this 425 00:24:04,960 --> 00:24:08,440 space. But it sounds like you 426 00:24:08,440 --> 00:24:13,760 fundamentally understand what the at least one of the 427 00:24:13,760 --> 00:24:17,040 foundational problems in the security world is, which is 428 00:24:18,160 --> 00:24:19,480 trust. But it, you know, there's a 429 00:24:19,480 --> 00:24:24,240 million different names for that thing of like, can I trust that 430 00:24:24,240 --> 00:24:28,120 this person or this device says that they are who they assert 431 00:24:28,120 --> 00:24:31,440 themselves to be? Can I trust that this company or 432 00:24:31,440 --> 00:24:33,920 my customer is going to do what they say they're going to do? 433 00:24:34,400 --> 00:24:38,000 You know, can I trust this data that my customer or my supplier 434 00:24:38,000 --> 00:24:40,640 gave me? Can I trust this software that 435 00:24:40,640 --> 00:24:42,600 I'm running? The answer to that is no, you 436 00:24:42,600 --> 00:24:48,400 can't, you know, but I am really fascinated by the fact that you 437 00:24:48,400 --> 00:24:52,960 went and asked a bunch of people like how do you codify trust or 438 00:24:52,960 --> 00:24:55,440 how do you define trust? Because I was going to ask you 439 00:24:55,440 --> 00:24:58,400 that question. How were you defining trust when 440 00:24:58,400 --> 00:25:02,160 you went about, you know, creating the idea for this? 441 00:25:02,720 --> 00:25:06,480 For this question, so my Co founders and I and these are 442 00:25:06,480 --> 00:25:08,800 people that I've now worked with across two companies. 443 00:25:08,840 --> 00:25:11,520 They're also all software engineers by background in 444 00:25:11,520 --> 00:25:14,760 training. We actually got together and we 445 00:25:14,760 --> 00:25:18,360 wrote after a period of a couple of months of brainstorming, we 446 00:25:18,360 --> 00:25:23,280 actually wrote that an internal memo called the kernel of trust 447 00:25:23,360 --> 00:25:26,480 OS. We, we basically thought about 448 00:25:26,480 --> 00:25:30,080 trust as an operating system. And we said, if I was writing an 449 00:25:30,080 --> 00:25:32,440 operating system, going back to my operating system classes as a 450 00:25:32,440 --> 00:25:34,920 computer science student, everything starts with the 451 00:25:34,920 --> 00:25:36,880 kernel and then you sort of build on top of that. 452 00:25:37,120 --> 00:25:39,880 So, so how do you, what, what defines the kernel of trust was 453 00:25:39,880 --> 00:25:43,200 the, was the underlying thesis. And what it really comes down 454 00:25:43,200 --> 00:25:46,440 to, I'm, I'm going to make it, I'm giving you the Super 455 00:25:46,440 --> 00:25:52,320 simplified version is it always starts with an objective, right? 456 00:25:52,320 --> 00:25:54,280 Every trust obligation has an objective. 457 00:25:54,320 --> 00:25:59,880 I am going to keep your data secure or I'm going to meet my 458 00:25:59,880 --> 00:26:04,240 contractual obligations with you or I'm going to help grow our 459 00:26:04,240 --> 00:26:06,480 revenue, right? It starts with some sort of 460 00:26:06,480 --> 00:26:07,520 objective like that. Sure. 461 00:26:08,240 --> 00:26:11,920 And the next step to that is you're then trying to say, OK, 462 00:26:11,920 --> 00:26:16,040 for me to not meet these objectives, there's a set of 463 00:26:16,040 --> 00:26:20,920 risks that are in place that are if any of those risks are 464 00:26:20,920 --> 00:26:23,200 elevated, then I'm not going to meet that objective. 465 00:26:24,400 --> 00:26:28,040 And so you need to figure out how to describe these risks in 466 00:26:28,040 --> 00:26:32,200 machine readable form. And then you need to have things 467 00:26:32,200 --> 00:26:34,960 like the likelihood of these risks, the impact these risks 468 00:26:34,960 --> 00:26:37,120 have, What is the consequence of these risks? 469 00:26:37,120 --> 00:26:38,480 What is the financial obligation like? 470 00:26:38,520 --> 00:26:40,040 All of these parameters are on risks. 471 00:26:40,480 --> 00:26:42,280 Yeah. Once you understand the risk for 472 00:26:42,280 --> 00:26:45,560 each risk, you then put mitigations in place, like what 473 00:26:45,560 --> 00:26:47,480 are the things you have to have to avoid this risk from 474 00:26:47,480 --> 00:26:50,800 happening? No mitigation is 100% or very 475 00:26:50,800 --> 00:26:52,680 few mitigations are 100% foolproof. 476 00:26:52,680 --> 00:26:53,880 So you have to weight these risks. 477 00:26:53,920 --> 00:26:55,240 You have to weight these controls. 478 00:26:55,840 --> 00:26:57,960 They call them controls. You weight these controls and 479 00:26:58,680 --> 00:27:02,240 you measure if you're meeting these mitigation obligations. 480 00:27:02,240 --> 00:27:04,120 And the way you measure them is through data. 481 00:27:04,480 --> 00:27:08,800 You have to collect data because these mitigations apply to a 482 00:27:08,800 --> 00:27:11,640 resurface. That resurface should be a set 483 00:27:11,640 --> 00:27:13,040 of contracts, should be an asset. 484 00:27:13,040 --> 00:27:17,120 It could be devices, it could be an application, and you have to 485 00:27:17,120 --> 00:27:19,800 collect data to make sure that those mitigations are operating 486 00:27:19,800 --> 00:27:22,280 effectively to protect those services. 487 00:27:23,120 --> 00:27:25,640 And usually there's always some issues. 488 00:27:26,240 --> 00:27:30,720 And once you have an issue, it has to calculate the impact of 489 00:27:30,720 --> 00:27:33,560 that issue and spin it out as your answer. 490 00:27:33,960 --> 00:27:36,320 And then you're sort of going back and evaluating it against 491 00:27:36,320 --> 00:27:39,720 the original objective and saying, because this issue 492 00:27:39,720 --> 00:27:44,200 exists and this impact, do I need to start budgeting, 493 00:27:44,200 --> 00:27:47,720 prioritizing, taking action, reporting, whatever the 494 00:27:47,720 --> 00:27:49,920 consequences? You have to then sort of create 495 00:27:49,920 --> 00:27:53,480 that feedback loop. And that became the idea that we 496 00:27:53,480 --> 00:27:59,680 said if we can go build this idea, then you can essentially 497 00:27:59,680 --> 00:28:03,720 build the AWS of trust where you're building all the building 498 00:28:03,720 --> 00:28:07,720 blocks that allow any stakeholder in the business, the 499 00:28:07,720 --> 00:28:14,440 CSO, the CRO, the CHRO, the CIO, the CEO, the board to take any 500 00:28:14,440 --> 00:28:16,320 trust obligation and build on top of this. 501 00:28:16,320 --> 00:28:20,800 So right now we're focused on the Cecil as our ICP, but we 502 00:28:20,800 --> 00:28:25,520 have companies where the CFO is using Trust Lab for things like 503 00:28:25,520 --> 00:28:28,280 Sarbanes-Oxley. Yeah, yeah. 504 00:28:28,640 --> 00:28:32,040 We have customers in the food and manufacturing space. 505 00:28:32,920 --> 00:28:34,840 We have customers in the Pharmaceutical industry that is 506 00:28:34,840 --> 00:28:38,240 using US for trust obligations around FDA compliance. 507 00:28:38,640 --> 00:28:40,200 Sure. Nothing to do with, you know, 508 00:28:40,200 --> 00:28:43,240 nothing purely to do with security. 509 00:28:43,400 --> 00:28:45,720 It has all sorts of other controls and risks and so on. 510 00:28:46,400 --> 00:28:49,080 So we're slowly expanding into other markets, but our primary 511 00:28:49,080 --> 00:28:53,640 bean and butter is how do we help Cisos list their 512 00:28:53,640 --> 00:28:57,400 objectives? How do they map risks to those 513 00:28:57,400 --> 00:29:01,400 objectives? And how do they then deploy 514 00:29:01,400 --> 00:29:05,360 controls and continuously monitor them and measure them to 515 00:29:05,360 --> 00:29:09,760 determine which risks are elevated and therefore which 516 00:29:09,760 --> 00:29:12,320 objectives need to be prioritized and budgeted better? 517 00:29:13,920 --> 00:29:16,720 You know, it's, there's a couple interesting things about what 518 00:29:16,720 --> 00:29:21,360 you just described. 1 is that Seesos and I, I know a bunch of 519 00:29:21,360 --> 00:29:26,520 seesos and many of whom are, you know, mutual friends of me and 520 00:29:26,560 --> 00:29:32,800 Jen, your, your CMO and they are some of the most stressed out, 521 00:29:33,800 --> 00:29:36,920 like sleepless people you will ever meet. 522 00:29:36,920 --> 00:29:39,920 I'm sure you're, you're noticing this in your customers, 523 00:29:40,320 --> 00:29:42,920 especially people that see So's that are in like tightly 524 00:29:42,920 --> 00:29:47,440 regulated industries like financial services or, or food 525 00:29:47,440 --> 00:29:52,360 or healthcare or any of those. Because they, they know for 526 00:29:52,360 --> 00:29:56,880 certain, whether it's through, you know, one Ave. or another, 527 00:29:56,880 --> 00:29:59,040 they're sitting at a bunch of liability as you described 528 00:29:59,040 --> 00:30:02,240 earlier that they can't really control. 529 00:30:02,240 --> 00:30:06,040 You know, they can do the best they can possibly do, but 530 00:30:06,040 --> 00:30:08,080 there's just stuff that's out of their hands. 531 00:30:08,080 --> 00:30:11,960 And if one little thing goes wrong, they get a bunch of 532 00:30:11,960 --> 00:30:14,280 regulators walking through the door and they get a bunch of 533 00:30:14,280 --> 00:30:16,800 customer lawsuits and they get all that kind of stuff. 534 00:30:17,240 --> 00:30:19,840 And if you can take away even a little bit of that stress from 535 00:30:19,840 --> 00:30:22,600 their lives, you're their best friend in the world. 536 00:30:22,880 --> 00:30:25,320 Like they they couldn't be happier, I would think. 537 00:30:25,560 --> 00:30:27,800 Yeah, everything you said is accurate. 538 00:30:27,800 --> 00:30:30,360 And so when I observed what you just said, I would ask the 539 00:30:30,360 --> 00:30:33,240 question why? Like I'm a, by nature, I'm a 540 00:30:33,240 --> 00:30:35,200 very curious person. So I'm always asking like, tell 541 00:30:35,200 --> 00:30:36,760 me, tell me more why, why, why, why? 542 00:30:37,520 --> 00:30:41,240 And it really whittled down to four simple truths, which it 543 00:30:41,360 --> 00:30:45,600 baffles me that these are still unsolved for a majority to see. 544 00:30:45,600 --> 00:30:50,680 So the first simple truth is your job is defined as proving a 545 00:30:50,680 --> 00:30:53,280 negative. Yes. 546 00:30:54,040 --> 00:30:55,240 Correct. Yep. 547 00:30:56,200 --> 00:30:57,760 Right. Impossible task. 548 00:30:57,960 --> 00:31:05,560 Cesos need a way to then figure out how do I display how I'm 549 00:31:05,600 --> 00:31:09,840 positively affecting the business instead of only being 550 00:31:09,840 --> 00:31:13,440 asked to prove the negative. That was one learning. 551 00:31:13,960 --> 00:31:17,840 The second learning was if something does happen that is 552 00:31:17,840 --> 00:31:22,800 bad, it's already happened in the past, right? 553 00:31:22,800 --> 00:31:26,760 It happened a long time ago, and they don't know that it happened 554 00:31:26,760 --> 00:31:27,920 and when it happened. Why? 555 00:31:28,080 --> 00:31:31,320 Because most of their assessments are being done with 556 00:31:31,320 --> 00:31:33,000 surveys and security questionnaires. 557 00:31:33,920 --> 00:31:37,360 Oh, absolutely right. It's like going to a dentist and 558 00:31:37,360 --> 00:31:40,960 the dentist asks you, Dennis, do you floss twice a day? 559 00:31:40,960 --> 00:31:42,280 Do you brush your teeth twice a day? 560 00:31:42,600 --> 00:31:46,080 And you say yes, but of course, takes your essay and starts 561 00:31:46,080 --> 00:31:47,840 looking at your teeth. And they know the reality, 562 00:31:47,880 --> 00:31:48,280 right? Yeah. 563 00:31:48,680 --> 00:31:51,120 They know online. And it's already happened in the 564 00:31:51,120 --> 00:31:53,760 past. And so you need to give them 565 00:31:53,800 --> 00:32:00,120 real time risk data, real time telemetry that allows them to 566 00:32:00,840 --> 00:32:03,360 change their perspective from looking at the past to now. 567 00:32:03,360 --> 00:32:08,040 How do I look into the future? So that was observation #2 568 00:32:08,720 --> 00:32:14,080 observation #3 is they have a really important job. 569 00:32:15,440 --> 00:32:18,520 They're the custodians of their intellectual property, their 570 00:32:18,520 --> 00:32:22,680 customer data. They are highly underfunded. 571 00:32:23,680 --> 00:32:26,480 Oh yeah, and understaffed. And yeah. 572 00:32:26,920 --> 00:32:27,960 Right. Why? 573 00:32:28,000 --> 00:32:34,840 Because they have not been able to articulate in a business 574 00:32:34,840 --> 00:32:39,480 friendly fashion, the business case to fix some of those 575 00:32:39,480 --> 00:32:42,960 issues. Most Seesos are very technical. 576 00:32:42,960 --> 00:32:45,160 They're great at what they do. They're not able to speak the 577 00:32:45,160 --> 00:32:50,800 language of business. Yeah, in, excuse me, I think 578 00:32:50,800 --> 00:32:54,560 some of that is beginning to change, you know, that you are 579 00:32:54,560 --> 00:32:58,600 seeing, I think a newer generation of Seesos that maybe 580 00:32:58,680 --> 00:33:02,160 came up through Business School, but also have some technical 581 00:33:02,600 --> 00:33:07,840 chops and that sort of thing. But you know that doesn't fix 582 00:33:07,840 --> 00:33:11,360 the budget problem that you just described, that you know that 583 00:33:11,360 --> 00:33:14,120 doesn't give them a magic pile of money to go fix things. 584 00:33:14,440 --> 00:33:16,240 That's right. And that brings me to the fourth 585 00:33:16,240 --> 00:33:19,840 one, which is the information security program is seen as a 586 00:33:19,840 --> 00:33:21,920 cost center. Yeah, absolutely. 587 00:33:21,920 --> 00:33:27,240 And I think it's important to reframe it as this is a value 588 00:33:27,240 --> 00:33:29,040 add to the business. This is actually a profit 589 00:33:29,040 --> 00:33:31,760 center. This is actually helping support 590 00:33:31,760 --> 00:33:33,720 revenue growth. If you don't do the things we 591 00:33:33,720 --> 00:33:35,320 need to do, we cannot close deals. 592 00:33:35,320 --> 00:33:38,760 It is actually reducing risk. And if you look at a balance 593 00:33:38,760 --> 00:33:41,000 sheet, it is actually reducing the risk. 594 00:33:41,000 --> 00:33:43,520 Liabilities in a balance sheet like these are the types of 595 00:33:43,520 --> 00:33:47,000 conversations that need to happen relative to the invest 596 00:33:47,000 --> 00:33:49,880 investment that is going into the security program to show 597 00:33:49,880 --> 00:33:52,280 that the security program is actually green, not red. 598 00:33:53,240 --> 00:33:58,520 Yeah, I think the the really difficult challenge is that a 599 00:33:58,520 --> 00:34:01,040 lot of Csos when they go talk to their boards when it's time to 600 00:34:01,040 --> 00:34:05,120 ask for more money or whatever it happens to be, the best thing 601 00:34:05,120 --> 00:34:08,080 that they can say is that nothing bad happened in the last 602 00:34:08,080 --> 00:34:12,000 year or the last quarter. Like nobody, there's no disaster 603 00:34:12,080 --> 00:34:16,400 on our you know, we didn't have to file any eight KS disclosing 604 00:34:16,400 --> 00:34:18,800 other events or, you know, we weren't the news. 605 00:34:19,080 --> 00:34:22,520 Like everything was calm. Like that's great, but we're not 606 00:34:22,520 --> 00:34:24,800 giving you any more money. You know, it's it's kind of like 607 00:34:25,159 --> 00:34:28,000 if if you did that with that, with just this little bit amount 608 00:34:28,000 --> 00:34:30,320 of money, then we don't need to give you any more. 609 00:34:30,920 --> 00:34:32,520 Like you're doing fine. Yeah. 610 00:34:32,920 --> 00:34:35,080 And you left the caveat tennis in that statement, right? 611 00:34:35,080 --> 00:34:37,880 It is to the best of my knowledge, I think that 612 00:34:37,880 --> 00:34:40,679 happened. Yeah, as far as I know now, 613 00:34:41,080 --> 00:34:45,120 there could be an attacker in our network right now that we'll 614 00:34:45,120 --> 00:34:47,800 only find out about four months from now at a very bad, 615 00:34:48,120 --> 00:34:52,320 inopportune moment. But I we don't know that yet. 616 00:34:52,400 --> 00:34:54,400 Yeah. Because I look, I, I have great 617 00:34:54,400 --> 00:34:58,480 empathy for them because not a single siso I've ever met will 618 00:34:58,480 --> 00:35:01,040 ever say I am secure. And I think they're right. 619 00:35:01,160 --> 00:35:03,240 They shouldn't say I am secure, right? 620 00:35:03,240 --> 00:35:05,120 They're absolutely right. Because you never know. 621 00:35:05,120 --> 00:35:08,440 There's always a bunch of so-called unknown unknowns in 622 00:35:08,440 --> 00:35:15,520 this in this industry, but the current processes is pretty low 623 00:35:15,520 --> 00:35:20,480 confidence in assessing risk or understanding the security 624 00:35:20,480 --> 00:35:24,600 posture. I work we work with some of the 625 00:35:24,600 --> 00:35:31,160 largest enterprises in the world and they are still assessing 626 00:35:31,160 --> 00:35:34,120 their risk posture by sending out surveys once a quarter or 627 00:35:34,120 --> 00:35:36,240 once a year. Do it totally, yeah. 628 00:35:36,720 --> 00:35:40,200 And they're basing their entire strategy and risk assessments 629 00:35:40,880 --> 00:35:43,640 based on assuming that the person responding a is giving 630 00:35:43,640 --> 00:35:50,080 the truth and B sampling evidence and basing verifying 631 00:35:50,080 --> 00:35:54,720 their responses with sampling is good enough to believe what 632 00:35:54,720 --> 00:35:58,160 they're saying. How many of your customers or 633 00:35:58,200 --> 00:36:00,680 you know, just the the folks that you talked to, you know, 634 00:36:00,680 --> 00:36:05,200 potential customers do you think really have a handle on what 635 00:36:05,200 --> 00:36:10,760 they should be measuring or, you know, looking for in their 636 00:36:10,960 --> 00:36:12,680 organization? It's a great question. 637 00:36:12,680 --> 00:36:14,880 I'm actually going to say most of them actually know what to 638 00:36:14,880 --> 00:36:16,920 measure. They do OK, that's good. 639 00:36:16,920 --> 00:36:19,920 They do know what to measure. And, and, and they've been 640 00:36:19,920 --> 00:36:22,560 thinking about this. They're, as you know, they're 641 00:36:22,560 --> 00:36:25,400 all part of various groups and small networks. 642 00:36:25,400 --> 00:36:28,760 They're, they're exchanging ideas, they're collaborating. 643 00:36:29,200 --> 00:36:33,160 They know what needs to be done. What's stopping them is #1 644 00:36:35,160 --> 00:36:40,080 there's not been a modern set of tools that has become mainstream 645 00:36:40,840 --> 00:36:42,800 that is enabling all of them to do it. 646 00:36:42,960 --> 00:36:47,960 So that's Part 1. Part 2 is the tools need to 647 00:36:47,960 --> 00:36:52,600 solve for enterprise complexity, and large enterprises are mostly 648 00:36:52,600 --> 00:36:55,840 snowflakes. So it is also hard for software 649 00:36:55,840 --> 00:37:01,160 to be built where every customer engagement looks like a unique 650 00:37:01,320 --> 00:37:03,880 environment, right? It is a hard problem, Very hard. 651 00:37:04,520 --> 00:37:06,760 Problem. And then the third thing which 652 00:37:06,760 --> 00:37:11,600 has stopped them from doing it is because there's a lot of 653 00:37:11,600 --> 00:37:17,000 inertia in this practice of risk and compliance and governance 654 00:37:17,000 --> 00:37:21,320 and all this that has been built over the last 20 years. 655 00:37:21,520 --> 00:37:25,680 And Csos have been traditionally not owned that function. 656 00:37:26,480 --> 00:37:29,920 That function is usually reported into the chief risk 657 00:37:29,920 --> 00:37:32,440 officer, the chief legal officer, the chief audit officer 658 00:37:32,440 --> 00:37:35,840 now are trying to inherit some portions of these function. 659 00:37:36,720 --> 00:37:43,560 And there's a tension in replacing manual work, humans 660 00:37:43,560 --> 00:37:47,160 that have invested in the job tools that have been basically 661 00:37:47,560 --> 00:37:49,240 imprinted into the enterprise fabric. 662 00:37:49,240 --> 00:37:50,520 It's hard to take these tools out. 663 00:37:50,520 --> 00:37:52,880 So there's a lot of inertia that they're dealing with. 664 00:37:52,880 --> 00:37:54,880 And that takes time and effort to to change. 665 00:37:56,520 --> 00:37:58,600 Yeah, that's, that's extremely true. 666 00:37:58,600 --> 00:38:02,880 I mean, the, the security field is relatively young compared to 667 00:38:02,880 --> 00:38:05,920 computer science anyway. And the compliance part of it is 668 00:38:05,920 --> 00:38:07,920 even younger than the security part. 669 00:38:08,240 --> 00:38:11,080 You know, it's, you know, depending on the industry you're 670 00:38:11,080 --> 00:38:14,360 in, 25 years old or, or maybe 30 at the most. 671 00:38:14,360 --> 00:38:19,640 So people are still wrapping their arms around exactly how to 672 00:38:19,640 --> 00:38:22,920 go about all of this. And I would think that 673 00:38:23,480 --> 00:38:27,760 automating some of that or all of that for them can be a big 674 00:38:27,920 --> 00:38:30,440 kind of weight off of their shoulders of like, OK, that's 675 00:38:30,440 --> 00:38:35,560 one thing that I don't have to be stressed about it. 2:00 AM. 676 00:38:35,960 --> 00:38:37,680 It is. And like honestly, like when I 677 00:38:37,680 --> 00:38:41,120 talk to Caesars, I what I tell them is I'm like, look, you guys 678 00:38:41,120 --> 00:38:42,400 are focusing on the wrong problem. 679 00:38:43,840 --> 00:38:47,800 You're focusing on compliance because compliance is being 680 00:38:47,800 --> 00:38:52,000 asked to you by the product owners, the product managers and 681 00:38:52,000 --> 00:38:54,920 your CEO because you need compliance to do business in 682 00:38:54,920 --> 00:38:56,880 certain verticals or certain geographies. 683 00:38:57,640 --> 00:39:00,880 But that's the wrong problem. What you should be focusing is 684 00:39:00,880 --> 00:39:04,600 on risks. Lack of compliance is 1 risk, 685 00:39:06,120 --> 00:39:07,760 right? So risk should feed into your 686 00:39:07,760 --> 00:39:10,120 compliance framework because at the end of the day, all of these 687 00:39:10,120 --> 00:39:11,880 are business obligations that you're making. 688 00:39:12,400 --> 00:39:15,480 Me being compliant with AB and C regulations is one such 689 00:39:15,480 --> 00:39:19,240 objective, one such obligation. So think of everything as a risk 690 00:39:19,240 --> 00:39:25,600 problem, and if you can apply telemetry and automation, then 691 00:39:25,800 --> 00:39:28,960 the same telemetry and automation, if if you're using a 692 00:39:28,960 --> 00:39:32,640 product that is designed well, can point it to solving for 693 00:39:32,640 --> 00:39:34,600 risk. It can also point it for solving 694 00:39:34,600 --> 00:39:36,920 for compliance. It can point it to solving for 695 00:39:37,200 --> 00:39:40,800 anything else in the future. Much like the human body has one 696 00:39:40,800 --> 00:39:45,400 set of data, I'm giving that data to an oncologist to test 697 00:39:45,400 --> 00:39:48,800 for cancer and giving it to my GP for my health check, and I'm 698 00:39:48,800 --> 00:39:50,320 giving it to a diabetologist, right? 699 00:39:50,320 --> 00:39:52,920 It's the same data. And I think if you think about 700 00:39:52,920 --> 00:39:57,480 things as objectives and risks in a consistent fashion, then 701 00:39:57,480 --> 00:40:02,200 you do the work once and you apply to many, and that helps 702 00:40:02,200 --> 00:40:05,360 you solve for a big problem most Sisos are having right now, 703 00:40:05,360 --> 00:40:11,280 which is team burnout. Oh, yeah, yeah, that's, that's a 704 00:40:11,280 --> 00:40:15,720 massive 1. There's a, a few younger people 705 00:40:15,720 --> 00:40:20,200 I know in the industry who are, I mean, like under 35 years old 706 00:40:20,320 --> 00:40:23,960 and have been in cybersecurity for 10 years at the most and are 707 00:40:24,000 --> 00:40:27,840 absolutely fried. You know, cannot, cannot 708 00:40:27,840 --> 00:40:30,840 possibly think about doing this for another 30 years. 709 00:40:31,600 --> 00:40:35,360 Just because, you know, even in a mature security organization, 710 00:40:36,280 --> 00:40:39,480 they still don't have enough resources to deal with all of 711 00:40:39,480 --> 00:40:41,600 the issues that they face every day. 712 00:40:41,880 --> 00:40:43,640 They don't have enough staff, they don't have enough money. 713 00:40:43,640 --> 00:40:46,280 They don't have the right tools or enough tools or whatever the 714 00:40:46,280 --> 00:40:48,400 case may be. And the scope is changing 715 00:40:48,400 --> 00:40:50,800 quarterly. At best. 716 00:40:50,880 --> 00:40:53,080 And sometimes it's like monthly, yeah. 717 00:40:53,720 --> 00:40:56,480 Like I have, you know, one of the areas in our business is we 718 00:40:56,480 --> 00:41:01,080 help Cisos and their teams use AI to accurately complete 719 00:41:01,080 --> 00:41:02,920 security questionnaires. That's one of the capabilities 720 00:41:02,920 --> 00:41:07,720 we have in our platform. And I, I, I'm fascinated by this 721 00:41:07,720 --> 00:41:10,360 problem because I always feel like every startup should have a 722 00:41:10,360 --> 00:41:12,880 villain. And the villain in the Trust 723 00:41:12,880 --> 00:41:14,240 Club movie is a security questionnaire. 724 00:41:14,240 --> 00:41:15,600 Like we hate security questioners. 725 00:41:15,600 --> 00:41:17,760 We think they should be abolished from the pace of the 726 00:41:17,760 --> 00:41:19,880 earth. I would be disappointed if my 727 00:41:19,880 --> 00:41:21,720 kids live in a world where security questioners still 728 00:41:21,720 --> 00:41:23,080 existed. Hey, man. 729 00:41:24,040 --> 00:41:30,640 But I find people to this day still their sole job in life is 730 00:41:30,640 --> 00:41:33,520 to wake up coming to work, turn on their computers and answer 731 00:41:33,520 --> 00:41:34,880 security questionnaires the whole day. 732 00:41:35,240 --> 00:41:37,400 Brutal. Rinse and repeat every day. 733 00:41:37,600 --> 00:41:41,600 You know, weekly in and out. And they have to do between 200 734 00:41:41,600 --> 00:41:43,160 and 300 security questionnaires a year. 735 00:41:43,160 --> 00:41:45,840 Like Seesos. Some seesos I've talked to have 736 00:41:45,840 --> 00:41:49,600 a KPI that is measuring the number of security 737 00:41:49,600 --> 00:41:52,840 questionnaires a human being isn't answering manually every 738 00:41:52,840 --> 00:41:56,920 day. Oh God, what a tremendous waste 739 00:41:56,920 --> 00:41:59,080 of time and resources, that is. That's right. 740 00:42:00,680 --> 00:42:03,000 That's unreal. But they have to do it because 741 00:42:03,000 --> 00:42:05,120 it ties to revenue, and if we don't do it, then the deal's not 742 00:42:05,120 --> 00:42:07,560 going to close. Yeah, yeah. 743 00:42:08,920 --> 00:42:13,080 How much of the conversations you're having with your 744 00:42:13,080 --> 00:42:15,360 customers, you mentioned AI. Thankfully, this is the first 745 00:42:15,360 --> 00:42:17,640 time we've mentioned this in 42 minutes. 746 00:42:17,640 --> 00:42:22,560 I appreciate that very much. And I mean, I guess this isn't 747 00:42:22,560 --> 00:42:25,560 directly tied to, to the problem you guys are trying to solve, 748 00:42:25,560 --> 00:42:29,080 but how much of the conversations you're having with 749 00:42:29,080 --> 00:42:32,960 the Csos and other customers is about the way that AI is 750 00:42:32,960 --> 00:42:35,880 affecting how they do business every day? 751 00:42:35,880 --> 00:42:40,280 Because it's it's sort of affecting every, every aspect of 752 00:42:40,560 --> 00:42:42,960 enterprises these days, especially in security. 753 00:42:43,080 --> 00:42:44,440 Yeah. I mean, there's, there's four 754 00:42:44,440 --> 00:42:45,880 pillars, right? It's very simple. 755 00:42:46,080 --> 00:42:50,200 It's 4 pillars. 1 is AI is bringing new security threats 756 00:42:50,200 --> 00:42:52,120 into their business. So how are you going to solve 757 00:42:52,120 --> 00:42:56,240 for that #2 is AI is bringing new compliance obligations to 758 00:42:56,240 --> 00:42:57,800 your business. How are you going to solve for 759 00:42:57,800 --> 00:43:01,440 that? Number 3 is AI is bringing data 760 00:43:01,840 --> 00:43:04,760 and intellectual property related issues into your 761 00:43:04,760 --> 00:43:06,760 business. How are you going to solve for 762 00:43:06,760 --> 00:43:09,640 that? And then the 4th is AI is 763 00:43:09,680 --> 00:43:12,640 promising an improvement in productivity to your business 764 00:43:12,640 --> 00:43:15,200 because all your peers are doing it, so you're feeling the 765 00:43:15,200 --> 00:43:17,800 pressure to adopt it. So how do you prove that AI is 766 00:43:17,800 --> 00:43:19,640 positively affecting your business because your board is 767 00:43:19,640 --> 00:43:21,200 asking you to do it, right? Like those are the fourth thing, 768 00:43:21,760 --> 00:43:22,600 four things that they have to do. 769 00:43:22,600 --> 00:43:24,760 Now there's a few more things apart from that, but I'm like 770 00:43:24,760 --> 00:43:27,920 bubbling up the the top four things that I'm hearing, you 771 00:43:28,000 --> 00:43:30,600 know, all the time. And the irony is they have to 772 00:43:30,600 --> 00:43:36,360 now do this, these four things while they're still brittle in 773 00:43:36,360 --> 00:43:38,760 all the other areas and all the other debt that they've 774 00:43:38,760 --> 00:43:41,200 inherited for all these years. So now there's they've been 775 00:43:41,200 --> 00:43:43,880 locked on, on top of all the brittleness they have right now 776 00:43:43,880 --> 00:43:47,520 in their business. Yeah, I think the the last one 777 00:43:47,520 --> 00:43:52,400 you mentioned of the board is saying what are we doing about 778 00:43:52,440 --> 00:43:54,080 AI? How are we using AI? 779 00:43:54,080 --> 00:43:56,000 Are we putting AI into our products? 780 00:43:56,400 --> 00:43:59,080 How are we dealing with AI enabled threats depending on 781 00:43:59,080 --> 00:44:00,840 what your, your core business is. 782 00:44:01,320 --> 00:44:06,360 And the seeso who knows all about this is like, well, we're 783 00:44:06,360 --> 00:44:11,360 doing AB and C but we don't have enough money to do EF and G, you 784 00:44:11,400 --> 00:44:13,520 know, or or D or whatever the alphabet is. 785 00:44:13,920 --> 00:44:19,640 So AI is like creating more and more and more of these issues 786 00:44:19,640 --> 00:44:22,520 and headaches for organizations all the time. 787 00:44:22,520 --> 00:44:26,120 And they're snowballing, you know, to a, to a degree. 788 00:44:26,120 --> 00:44:29,200 That's like the pace of that snowball coming down the hill is 789 00:44:29,200 --> 00:44:30,440 crazy. That's right. 790 00:44:31,040 --> 00:44:33,960 And I think the thing that people are not asking for as 791 00:44:33,960 --> 00:44:37,640 part of that conversation, and I see this because right now it's 792 00:44:37,760 --> 00:44:40,480 we're in the, we're, we're sort of in that phase of everything 793 00:44:40,480 --> 00:44:49,520 is the wild, Wild West right now is accuracy, which, which in I, 794 00:44:49,560 --> 00:44:51,760 I use the word assurance and impact. 795 00:44:52,040 --> 00:44:57,120 I think ultimately what I see the maturities was doing is 796 00:44:57,120 --> 00:44:59,200 they're taking a measured approach where they're saying 797 00:44:59,800 --> 00:45:02,560 we're not going to say no to AI. We believe it is going to change 798 00:45:02,560 --> 00:45:04,520 the business and we need to get on the bandwagon. 799 00:45:05,240 --> 00:45:08,440 But the way we're going to do it is we're going to measure AI for 800 00:45:08,440 --> 00:45:11,560 assurance. So assurance means are the 801 00:45:11,560 --> 00:45:13,680 results accurate? Are they secure? 802 00:45:13,880 --> 00:45:16,600 Are they meeting my compliance, my compliant objectives? 803 00:45:16,880 --> 00:45:21,240 And can I have deterministic observability in like what the 804 00:45:21,280 --> 00:45:23,680 AI did? So in case something changes, I 805 00:45:23,680 --> 00:45:25,200 can go back and roll it back, right? 806 00:45:25,520 --> 00:45:27,880 So that's one area, which is assurance. 807 00:45:28,320 --> 00:45:31,200 The other one is impact. Is it really delivering the 808 00:45:31,200 --> 00:45:33,240 automation that it promised that? 809 00:45:33,240 --> 00:45:35,880 Great question is. It is it really enabling 810 00:45:35,880 --> 00:45:37,880 productivity or it's actually giving my team more work? 811 00:45:38,640 --> 00:45:41,920 What ROI has it delivered relative to the investment that 812 00:45:41,920 --> 00:45:42,360 I made? Right. 813 00:45:42,360 --> 00:45:46,000 So like, like I used the short and I said AI is really 814 00:45:46,000 --> 00:45:47,640 assurance and impact is the real AI. 815 00:45:47,640 --> 00:45:50,160 If you can't deliver assurance and impact, AI is useless. 816 00:45:51,320 --> 00:45:55,240 Yeah. And the issue of it just 817 00:45:55,240 --> 00:45:57,560 creating more work for teams is real. 818 00:45:57,640 --> 00:46:01,160 That is absolutely real. And it's not just well, 819 00:46:01,160 --> 00:46:03,960 evaluating which tool to use or all of that. 820 00:46:04,640 --> 00:46:07,040 It depending on the context in which you're using it. 821 00:46:07,520 --> 00:46:11,280 If you're using it in a SoC, for example, to analyze incoming 822 00:46:11,320 --> 00:46:14,400 threats and try and triage what's happening, you still need 823 00:46:14,400 --> 00:46:18,960 a human person to double go back and check the A is results and 824 00:46:19,000 --> 00:46:21,720 make sure that what they said was a false positive was 825 00:46:21,720 --> 00:46:23,960 actually a false positive or vice versa. 826 00:46:24,480 --> 00:46:28,800 You know it, It's not, it's certainly not a cure all in in 827 00:46:29,240 --> 00:46:31,800 you know, some cases it creates other issues. 828 00:46:32,040 --> 00:46:35,080 It does. And like AI, the modern current 829 00:46:35,120 --> 00:46:38,400 view of AI is still based on probabilistic models, computer 830 00:46:38,400 --> 00:46:40,600 science models that are built on probabilistic algorithms. 831 00:46:40,600 --> 00:46:44,040 And so it's not deterministic. And there are lots of use cases 832 00:46:44,040 --> 00:46:47,080 that require determinism. And therefore, to your point, a 833 00:46:47,080 --> 00:46:49,320 human has to be involved. And so like, for example, at 834 00:46:49,320 --> 00:46:52,560 Trust Cloud, we've coined the storm internally for our 835 00:46:52,560 --> 00:46:55,880 technology where we've built our own AI capabilities. 836 00:46:56,440 --> 00:46:59,640 We call it Plaid, which stands for People LED assurance and 837 00:46:59,640 --> 00:47:04,480 impact driven. And if you can't create a 838 00:47:04,480 --> 00:47:07,640 methodology of that kind in whatever AI you're selling to 839 00:47:07,640 --> 00:47:11,320 your customers, then it's going to fail in the enterprise. 840 00:47:11,320 --> 00:47:15,720 Because if you don't have the people LED experience, then 841 00:47:15,720 --> 00:47:18,360 there's going to be use cases where the AI will do some bad 842 00:47:18,360 --> 00:47:21,200 things and that's going to create bad outcomes for the 843 00:47:21,200 --> 00:47:22,880 business that's going to affect them and you. 844 00:47:23,920 --> 00:47:28,120 But if you don't have assurance and impact baked in, then the AI 845 00:47:28,120 --> 00:47:31,080 might do bad things, but also it's not going to deliver on the 846 00:47:31,080 --> 00:47:33,240 value that you promised. And so that's almost a 847 00:47:33,240 --> 00:47:36,720 philosophy that we have at Trust Cloud and everybody should come 848 00:47:36,720 --> 00:47:38,280 up with their own version of the philosophy. 849 00:47:38,280 --> 00:47:40,640 I think Plaid is a great philosophy, but they need the 850 00:47:40,640 --> 00:47:43,000 theme is correct, right? There needs to be people that it 851 00:47:43,000 --> 00:47:46,600 needs to be accurate and assured and it needs to deliver impact. 852 00:47:47,080 --> 00:47:49,480 Yeah, that's a great way of looking at it. 853 00:47:49,480 --> 00:47:54,080 It's a, it's a perspective that I don't think enough people have 854 00:47:54,080 --> 00:47:58,000 right now on when they're rolling out these AI tools and 855 00:47:58,000 --> 00:48:02,400 just trying to get them to solve every single problem that they 856 00:48:02,400 --> 00:48:04,840 have and, you know, hoping for the best. 857 00:48:06,240 --> 00:48:08,960 And that's not really a great strategy. 858 00:48:09,320 --> 00:48:12,720 By the way, I just want to say don't get me wrong, I am all in 859 00:48:12,720 --> 00:48:14,720 on AI. No, I get you. 860 00:48:14,720 --> 00:48:17,760 Yeah, I'm. All in, I think it's going to 861 00:48:17,760 --> 00:48:19,560 change the world. 10 years from now, we're going to look back 862 00:48:19,560 --> 00:48:21,840 and get them. Oh my God this is amazing. 863 00:48:23,240 --> 00:48:25,520 Well, that's funny because that's kind of the, the way I 864 00:48:25,520 --> 00:48:28,440 was going to wrap it up was to ask you, you know, if we, if we 865 00:48:28,440 --> 00:48:34,280 talk again in, in a year, say a year from now, next April, how 866 00:48:34,280 --> 00:48:37,400 different do you think the landscape will look for, say, 867 00:48:37,400 --> 00:48:40,880 your business and your customers, you know, with the, 868 00:48:41,360 --> 00:48:44,240 the pace of change with AI right now? 869 00:48:44,800 --> 00:48:48,160 I I'll answer it in two parts. I'll first start with like what 870 00:48:48,160 --> 00:48:50,640 I think is going to happen in the industry in general or what 871 00:48:50,640 --> 00:48:52,440 I hope is going to happen in the industry in general. 872 00:48:53,040 --> 00:48:55,200 And then I'll give you the answer specifically to trust 873 00:48:55,200 --> 00:48:58,080 what Trustcard does. Look, in the industry in 874 00:48:58,080 --> 00:49:00,560 general, there's so many companies where models are 875 00:49:00,560 --> 00:49:05,080 improving in parallel across multiple use cases, where we're 876 00:49:05,080 --> 00:49:08,120 going to live in a world where there's going to be multiple 877 00:49:08,720 --> 00:49:13,600 multi billion, in some cases trillion dollar markets that are 878 00:49:13,600 --> 00:49:17,920 fundamentally being reshaped because of AI, right? 879 00:49:18,280 --> 00:49:21,480 In the last two weeks, we have seen not only what happens in 880 00:49:21,480 --> 00:49:23,400 software development, but we've seen what happened in 881 00:49:23,400 --> 00:49:26,920 vulnerabilities with the Project Glass Wing and Mythos 882 00:49:26,920 --> 00:49:29,520 announcements. I think we're going to see that 883 00:49:29,520 --> 00:49:33,200 where the entire legal industry is going to deal with a 884 00:49:33,200 --> 00:49:35,920 significant amount of disruption in the next 12 months. 885 00:49:37,080 --> 00:49:42,320 I think some part of the legal stack is going to get disrupted 886 00:49:42,640 --> 00:49:46,280 with AI and therefore a lot of jobs are going to get disrupted 887 00:49:46,560 --> 00:49:49,800 with AII. Think manufacturing shipping, 888 00:49:49,840 --> 00:49:56,080 like a lot of industries where human labor is involved in a lot 889 00:49:56,080 --> 00:49:58,400 of paper pushing. Yeah. 890 00:49:59,000 --> 00:50:01,200 AI is going to take over like a lot of those things. 891 00:50:01,200 --> 00:50:05,280 So I think at a macro level multiple multi billion dollar 892 00:50:05,280 --> 00:50:08,840 industries are going to see seismic shifts happen in what AI 893 00:50:08,840 --> 00:50:10,680 is going to do. Now it's going to be a step one 894 00:50:10,680 --> 00:50:13,840 seismic shift because then it's going to take some time to bake 895 00:50:13,840 --> 00:50:17,000 and operationalize. But these models are going to 896 00:50:17,000 --> 00:50:20,000 get so advanced when people see what they're capable of, it's 897 00:50:20,000 --> 00:50:24,440 going to be amazing. Another big industry is 898 00:50:24,440 --> 00:50:29,120 entertainment, music and video. Like Trust Now is running ads 899 00:50:30,840 --> 00:50:34,240 around funny things. C source tell us that I then 900 00:50:34,240 --> 00:50:39,200 take and make into an ad and you see the ad and it looks 100% 901 00:50:39,200 --> 00:50:46,280 real, completely built with AI. Yeah, it's, it's impossible to 902 00:50:46,280 --> 00:50:49,480 tell now, even for people like us that live in this world and 903 00:50:49,480 --> 00:50:53,040 like are paying attention. And I'm skeptical of everything 904 00:50:53,040 --> 00:50:56,760 that I see now, unfortunately. But there's stuff you look at. 905 00:50:56,760 --> 00:51:02,600 You're like I, I have no idea. That looks 100%, you know human 906 00:51:03,560 --> 00:51:05,600 to me. I was showing these ads to 907 00:51:05,680 --> 00:51:08,920 somebody in our network and they looked at it and said, Hey, who 908 00:51:08,920 --> 00:51:10,320 is this actor? We, we use the same. 909 00:51:10,320 --> 00:51:12,920 We have a, a Siso persona called Michael King. 910 00:51:12,920 --> 00:51:14,680 Michael King shows up in all the ads. 911 00:51:15,560 --> 00:51:18,000 So the person looked at me and said, Hey, who's this person? 912 00:51:18,000 --> 00:51:19,480 Who's this actor? And I look at him and go, 913 00:51:19,560 --> 00:51:22,280 that's, that's an AI. That's an AI characterizes 914 00:51:22,320 --> 00:51:24,280 really can't tell the difference. 915 00:51:26,320 --> 00:51:28,240 But then you look from a trust load standpoint. 916 00:51:28,720 --> 00:51:31,400 So what does trust load do? We've we've built the product 917 00:51:31,400 --> 00:51:35,280 specifically focused on Sisos. That's all three problems for 918 00:51:35,280 --> 00:51:37,320 them. Number one, we give them an 919 00:51:37,320 --> 00:51:40,800 accurate view of their risk posture by continuously 920 00:51:40,800 --> 00:51:44,160 monitoring all their controls so they don't have to manually do 921 00:51:44,160 --> 00:51:45,800 risk assessments. That's Part 1. 922 00:51:46,440 --> 00:51:49,520 Part 2 is we take those same continuously controlled, 923 00:51:50,440 --> 00:51:53,160 continuously monitored controls and map it to compliance, and we 924 00:51:53,160 --> 00:51:55,000 automate all their compliance readiness workflows. 925 00:51:55,840 --> 00:51:58,400 And then #3 is when they get questionnaires from their 926 00:51:58,400 --> 00:52:03,120 regulators and their customers, we use AI to take the data of 927 00:52:03,600 --> 00:52:05,960 information that we have with their evidence and their 928 00:52:06,160 --> 00:52:08,120 controls and their policies and so on. 929 00:52:08,920 --> 00:52:13,800 And we create accurate responses in a few minutes for all of the 930 00:52:13,800 --> 00:52:17,720 questionnaires that come by. And so we tie all that together 931 00:52:17,720 --> 00:52:21,920 and we say what used to be called GRC, which stood for 932 00:52:21,920 --> 00:52:25,560 governance, risk and compliance, should now stand from a CISO 933 00:52:25,560 --> 00:52:32,360 standpoint, should stand for growth resilience and risk 934 00:52:32,360 --> 00:52:36,000 reduction. And that becomes the new 935 00:52:36,360 --> 00:52:39,200 language of GRC, right? You're showing that you're 936 00:52:39,200 --> 00:52:41,680 growing your business. You're showing that you're 937 00:52:41,680 --> 00:52:44,560 increasing your resilience and therefore reducing risk. 938 00:52:45,200 --> 00:52:48,520 And #3 the AI is now automating a lot of your work flows. 939 00:52:48,520 --> 00:52:52,000 And so you're enabling cost optimization in your business 940 00:52:52,080 --> 00:52:54,680 with all the automation. Like that becomes the definition 941 00:52:54,680 --> 00:52:58,440 of how Cisos can show the business impact of their 942 00:52:58,440 --> 00:53:01,120 security program by supporting growth, resilience and cost 943 00:53:01,120 --> 00:53:04,800 optimization. Yeah, well, a lot of CSO's need 944 00:53:04,800 --> 00:53:08,920 that help, you know, justifying their not justifying. 945 00:53:08,920 --> 00:53:10,000 It's not the right word, but yeah. 946 00:53:11,880 --> 00:53:13,520 Yeah, I will. I will. 947 00:53:13,520 --> 00:53:14,760 I have plenty of friends in that world. 948 00:53:14,760 --> 00:53:18,360 So, yeah, Charles, thank you so much for your time. 949 00:53:18,360 --> 00:53:20,120 And this is really, I really enjoyed this. 950 00:53:20,480 --> 00:53:23,280 It was great hearing, hearing your background and your story 951 00:53:23,360 --> 00:53:26,680 and and getting to hear about what Trust Cloud is building. 952 00:53:26,680 --> 00:53:29,600 So thanks so much for your time. And I look forward to seeing you 953 00:53:29,600 --> 00:53:31,640 more and more in Boston now that we know we're. 954 00:53:32,240 --> 00:53:33,160 Pretty close. Absolutely. 955 00:53:33,600 --> 00:53:35,520 Yeah, for sure. Thanks again. 956 00:53:35,760 --> 00:53:37,360 Thank you for having me. Take care.