1 00:00:03,960 --> 00:00:07,320 Speaker 1: Hello, Hello, Welcome to Smart Talks with IBM, a podcast 2 00:00:07,360 --> 00:00:12,560 Speaker 1: from Pushkin Industries, iHeart Radio and IBM. I'm Malcolm Glapwell. 3 00:00:13,240 --> 00:00:16,480 Speaker 1: This season, we're diving back into the world of artificial intelligence, 4 00:00:16,800 --> 00:00:20,320 Speaker 1: but with a focus on the powerful concept of open 5 00:00:21,040 --> 00:00:26,599 Speaker 1: its possibilities, implications, and misconceptions. We'll look at openness from 6 00:00:26,600 --> 00:00:29,720 Speaker 1: a variety of angles and explore how the concept is 7 00:00:29,760 --> 00:00:33,800 Speaker 1: already reshaping industries, ways of doing business and our very 8 00:00:33,880 --> 00:00:38,400 Speaker 1: notion of what's possible. On today's episode, I'm joined by 9 00:00:38,479 --> 00:00:42,440 Speaker 1: Jason Kelly, the global Managing Partner for IBM Strategic Partners 10 00:00:42,479 --> 00:00:46,599 Speaker 1: and Ecosystems, and by Christy Fredericks, the Senior Vice president 11 00:00:46,880 --> 00:00:52,000 Speaker 1: and Chief Partnership Officer at Palo Alto Networks. We discussed 12 00:00:52,000 --> 00:00:56,920 Speaker 1: how their partnership in the cybersecurity space helps strengthen enterprises 13 00:00:57,120 --> 00:01:01,440 Speaker 1: by focusing on seamless cybersecurity solutions tailored to meet the 14 00:01:01,480 --> 00:01:07,280 Speaker 1: evolving threat landscape. By leveraging AI and automation, this collaboration 15 00:01:07,400 --> 00:01:12,560 Speaker 1: aims to modernize security programs, improve response times, and produce risks. 16 00:01:13,840 --> 00:01:17,280 Speaker 1: Jason and Christie both bring a tremendous amount of experience 17 00:01:17,600 --> 00:01:21,039 Speaker 1: and expertise to the subject I think you're really going 18 00:01:21,120 --> 00:01:36,160 Speaker 1: to enjoy this one. Jason Christy, Welcome to Smart Talks 19 00:01:36,160 --> 00:01:38,080 Speaker 1: with IBM. Thank you for joining me. 20 00:01:38,360 --> 00:01:38,760 Speaker 2: Thank you. 21 00:01:38,920 --> 00:01:39,760 Speaker 3: It's great to be here. 22 00:01:40,120 --> 00:01:43,560 Speaker 1: We are here to discuss cybersecurity and the partnership between 23 00:01:43,600 --> 00:01:46,840 Speaker 1: IBM and Palo Alto Networks. But before we get there, 24 00:01:47,080 --> 00:01:48,760 Speaker 1: I wanted you guys to tell me a little bit 25 00:01:49,360 --> 00:01:54,040 Speaker 1: about yourself. Jason, start with you. I see on your 26 00:01:54,720 --> 00:01:58,640 Speaker 1: resume west Point, which makes we think there's some interesting 27 00:01:58,680 --> 00:02:01,680 Speaker 1: things going on. How did you get to west Point? 28 00:02:02,240 --> 00:02:04,720 Speaker 2: West Point? West Point was the decision. First, it was 29 00:02:04,760 --> 00:02:07,880 Speaker 2: it was affordable back in the day. But I had 30 00:02:07,880 --> 00:02:09,720 Speaker 2: a sense of service. My father was a World War 31 00:02:09,760 --> 00:02:12,760 Speaker 2: Two vet, so I grew up on the weekends watching 32 00:02:12,840 --> 00:02:16,800 Speaker 2: World War two video. You know he's army as well. Yeah, 33 00:02:16,840 --> 00:02:21,360 Speaker 2: and so I thought, oh that'd be exciting, and I 34 00:02:21,360 --> 00:02:24,640 Speaker 2: thought I do some type of service. Went there and 35 00:02:24,680 --> 00:02:27,400 Speaker 2: now I have the biggest family, extended family I could 36 00:02:27,440 --> 00:02:30,760 Speaker 2: ever have. So it was very exciting. Played football lucked out, 37 00:02:31,600 --> 00:02:37,120 Speaker 2: meaning I wasn't recruited. I walked on and that kept 38 00:02:37,120 --> 00:02:39,040 Speaker 2: me there because it gave me something and out left 39 00:02:39,080 --> 00:02:42,400 Speaker 2: with all the other pressures defensive back, I was I 40 00:02:42,560 --> 00:02:44,600 Speaker 2: was great at knocking the ball down, not the best 41 00:02:44,639 --> 00:02:45,240 Speaker 2: at catching it. 42 00:02:45,320 --> 00:02:48,600 Speaker 1: Yeah, and then you were a ranger. 43 00:02:49,480 --> 00:02:51,880 Speaker 2: I was. I was privileged to be a US Army 44 00:02:51,919 --> 00:02:55,079 Speaker 2: airborne ranger station, but did most of my time in 45 00:02:55,120 --> 00:02:59,080 Speaker 2: northern Italy. We're part of the it's eighty second airbarnship post. 46 00:02:59,160 --> 00:03:02,400 Speaker 2: Oh yeah, people say seriously, like, you know, you were, 47 00:03:02,400 --> 00:03:05,200 Speaker 2: you were, you were drinking wine and having bred you know. 48 00:03:05,639 --> 00:03:08,919 Speaker 2: But it was a part of a NATO force there 49 00:03:08,960 --> 00:03:11,520 Speaker 2: at the time. Yeah, so exciting. 50 00:03:11,760 --> 00:03:15,400 Speaker 1: How did you get from there to IBM? 51 00:03:15,880 --> 00:03:18,400 Speaker 2: A long path. As I came out of the military, 52 00:03:18,639 --> 00:03:26,280 Speaker 2: I started manufacturing retail housing and did a quick stint, 53 00:03:26,320 --> 00:03:30,400 Speaker 2: took a leave of absence from industry, and did a 54 00:03:30,560 --> 00:03:33,680 Speaker 2: stint of yet again public service in the state of 55 00:03:33,720 --> 00:03:38,360 Speaker 2: Tennessee with economic development, and got a whiff of how 56 00:03:38,400 --> 00:03:41,840 Speaker 2: fun it could be to do things around data and media. 57 00:03:42,160 --> 00:03:45,120 Speaker 2: Started a small media firm what we would now call 58 00:03:45,160 --> 00:03:49,480 Speaker 2: a digital firm, sold it and said I wanted to 59 00:03:49,520 --> 00:03:51,880 Speaker 2: go do it again somewhere, but I wanted to go 60 00:03:51,920 --> 00:03:54,520 Speaker 2: to a big company. And the family at IBM brought 61 00:03:54,600 --> 00:03:56,760 Speaker 2: me in and yet to let me go. 62 00:03:56,960 --> 00:03:58,120 Speaker 1: That was how many years ago? 63 00:04:00,040 --> 00:04:00,680 Speaker 2: Two decades? 64 00:04:00,840 --> 00:04:01,280 Speaker 3: Oh wow? 65 00:04:01,600 --> 00:04:04,080 Speaker 2: Yeah, so, I know I look amazingly young on but yes, 66 00:04:05,400 --> 00:04:08,880 Speaker 2: you must have. And IBM was my fifth career and 67 00:04:09,000 --> 00:04:11,360 Speaker 2: and and I've enjoyed a since and that's what I 68 00:04:11,680 --> 00:04:14,120 Speaker 2: what I do. They build teams, grow new parts of 69 00:04:14,120 --> 00:04:16,320 Speaker 2: the company and get to work with some of the 70 00:04:16,320 --> 00:04:19,040 Speaker 2: most brilliant people on the face of the planet, as 71 00:04:19,040 --> 00:04:22,960 Speaker 2: well as partners like Christy that just keep it exciting. 72 00:04:23,279 --> 00:04:25,800 Speaker 1: Christy, you're I was delighted to learn that you are Canadian. 73 00:04:25,960 --> 00:04:31,680 Speaker 1: Yes here, Yeah, but you so you were a consultant 74 00:04:31,680 --> 00:04:33,080 Speaker 1: for a long time at Bane. 75 00:04:33,440 --> 00:04:36,680 Speaker 3: Yes, yeah. I joined Bain Consulting intending to spend a 76 00:04:36,680 --> 00:04:38,760 Speaker 3: couple of years there learn the ropes and then go 77 00:04:38,800 --> 00:04:42,359 Speaker 3: get my first real job. But the value personally to 78 00:04:42,400 --> 00:04:44,360 Speaker 3: my growth and development, and then that we were able 79 00:04:44,400 --> 00:04:46,960 Speaker 3: to bring our clients. I ended up there for sixteen years, 80 00:04:47,560 --> 00:04:51,080 Speaker 3: and then post Bain went on to another my first 81 00:04:51,120 --> 00:04:53,880 Speaker 3: product company, a new relic, and then it's come full 82 00:04:53,920 --> 00:04:57,280 Speaker 3: circle at Powlta Networks. But at Bain it was all 83 00:04:57,320 --> 00:05:00,760 Speaker 3: about bringing expertise across different industries to help our clients 84 00:05:01,000 --> 00:05:04,600 Speaker 3: improve whatever they needed to improve and bringing that expertise 85 00:05:04,640 --> 00:05:07,039 Speaker 3: to bear and then you have the product lens and 86 00:05:07,080 --> 00:05:08,840 Speaker 3: you think, Okay, we're going to build the absolute best 87 00:05:08,839 --> 00:05:11,440 Speaker 3: product to help our customers do what they need to 88 00:05:11,440 --> 00:05:14,200 Speaker 3: get done. And then I joined pal Alto about six 89 00:05:14,240 --> 00:05:18,360 Speaker 3: seven months ago in a partnership's role and I'm delighted 90 00:05:18,360 --> 00:05:20,719 Speaker 3: to be able to work with amazing consulting companies like 91 00:05:20,760 --> 00:05:22,640 Speaker 3: IBM where we bring both to bear. 92 00:05:23,160 --> 00:05:26,320 Speaker 1: How long have IBM and Paulo Alto Network's been partners? 93 00:05:26,960 --> 00:05:29,880 Speaker 2: So we've been We've been working together for quite quite 94 00:05:29,880 --> 00:05:33,400 Speaker 2: a long time, but we made it official, meaning we 95 00:05:33,720 --> 00:05:35,839 Speaker 2: got married as strategic partners last year. 96 00:05:36,000 --> 00:05:38,039 Speaker 1: Oh I see. So what is it that each of 97 00:05:38,080 --> 00:05:41,239 Speaker 1: you bring to the table? What's each side special? 98 00:05:41,320 --> 00:05:44,200 Speaker 2: So it's great that you asked that because about a 99 00:05:44,240 --> 00:05:48,160 Speaker 2: decade ago, are now CEO Arvin Christ says, you know, 100 00:05:48,160 --> 00:05:50,800 Speaker 2: it wouldn't be great if we just had this one 101 00:05:50,839 --> 00:05:53,320 Speaker 2: focus with this what does IBM do? And you have 102 00:05:53,400 --> 00:05:55,720 Speaker 2: this whole list, and he says, let's make it simple. 103 00:05:56,320 --> 00:06:01,039 Speaker 2: We are a multi cloud, hybrid cloud AIIC company. And 104 00:06:01,120 --> 00:06:03,480 Speaker 2: so when you say that, it sounds very simple, but 105 00:06:03,520 --> 00:06:07,480 Speaker 2: then people, what the hell is that? What your hybrid cloud? Well, 106 00:06:08,160 --> 00:06:10,039 Speaker 2: both of those two things have a lot of data 107 00:06:10,080 --> 00:06:13,240 Speaker 2: involved and a lot of those mean that that data 108 00:06:13,320 --> 00:06:17,320 Speaker 2: is going to sit in multiple places and distributed environments. Well, 109 00:06:17,360 --> 00:06:20,680 Speaker 2: if you're able to tie those things together with multiple partners, 110 00:06:21,040 --> 00:06:25,160 Speaker 2: you also have to make sure that it's secure because 111 00:06:25,560 --> 00:06:28,760 Speaker 2: in the direction that we're going, where data is now 112 00:06:29,000 --> 00:06:31,479 Speaker 2: being consumed in many different places and it is the 113 00:06:31,520 --> 00:06:35,960 Speaker 2: fuel behind AI as we know, then you say, ah, well, 114 00:06:36,480 --> 00:06:38,960 Speaker 2: who does that well and who does it in a 115 00:06:39,000 --> 00:06:41,800 Speaker 2: way that's getting rid of seams, the seams that could 116 00:06:41,800 --> 00:06:45,840 Speaker 2: be across multiple products, multiple product SATs even And that's 117 00:06:45,880 --> 00:06:46,800 Speaker 2: where Powell comes in. 118 00:06:47,680 --> 00:06:51,440 Speaker 3: I think the conventional wisdom and cybersecurity was always you 119 00:06:51,520 --> 00:06:54,880 Speaker 3: need all the new tools, right, you need it every threat. 120 00:06:54,920 --> 00:06:56,680 Speaker 3: It's like, whack them all. Every threat that pops up, 121 00:06:56,720 --> 00:07:00,200 Speaker 3: you get the tool that's purpose built for that specific thing. Well, 122 00:07:00,240 --> 00:07:02,760 Speaker 3: fast forward to you know, the RSA conference this year, 123 00:07:02,800 --> 00:07:06,400 Speaker 3: there were four thousand vendors on the floor. You look 124 00:07:06,400 --> 00:07:09,680 Speaker 3: at an average company, there's hundreds of cybersecurity tools. It 125 00:07:09,800 --> 00:07:13,640 Speaker 3: introduces a level of complexity that is really hard to 126 00:07:13,680 --> 00:07:19,360 Speaker 3: manage you as a user. Query and application, right, that 127 00:07:19,480 --> 00:07:23,200 Speaker 3: query can go through a bunch of different pings from 128 00:07:23,240 --> 00:07:25,080 Speaker 3: one cloud to the next. It goes into and out 129 00:07:25,080 --> 00:07:27,840 Speaker 3: of assaas application. It maybe running along a network. You 130 00:07:27,840 --> 00:07:29,520 Speaker 3: may be accessing it from your phone, which is an 131 00:07:29,600 --> 00:07:32,360 Speaker 3: unmanaged device. It's got to go in and out. And 132 00:07:32,400 --> 00:07:35,080 Speaker 3: if you say, okay, I've got to secure that phone, 133 00:07:35,680 --> 00:07:37,880 Speaker 3: I've got to secure the network, I've got it. And 134 00:07:37,880 --> 00:07:39,760 Speaker 3: then all of a sudden you've got sort of firewalls, 135 00:07:39,800 --> 00:07:43,520 Speaker 3: software and hardwelfiles popping up everywhere. You've got cloud security, 136 00:07:44,080 --> 00:07:46,360 Speaker 3: and it's you've probably heard of this concept of zero trust, 137 00:07:46,360 --> 00:07:47,920 Speaker 3: which is every time you have to check and say 138 00:07:47,920 --> 00:07:49,680 Speaker 3: are you allowed in here? Are you allowed in here? 139 00:07:49,960 --> 00:07:52,520 Speaker 3: The number of places that can fall down it just 140 00:07:52,560 --> 00:07:56,239 Speaker 3: becomes overwhelming. So you end up with either alerts firing, 141 00:07:56,720 --> 00:07:59,520 Speaker 3: you know, every two seconds that you have to then 142 00:07:59,560 --> 00:08:02,600 Speaker 3: go invest again, most of which are false positives or 143 00:08:03,160 --> 00:08:06,440 Speaker 3: you miss something right. And so that was the conventionalism 144 00:08:06,560 --> 00:08:08,160 Speaker 3: was we've got to buy all these tools, and now 145 00:08:08,200 --> 00:08:11,360 Speaker 3: you've got overwhelmed CIOs and CSOs with hundreds of tools, 146 00:08:11,400 --> 00:08:14,520 Speaker 3: and Palo Alto strategy has been, look, we're going to 147 00:08:14,600 --> 00:08:17,640 Speaker 3: create a platform where everything can be stitched together, everything 148 00:08:17,680 --> 00:08:21,200 Speaker 3: can speak the same language, and we can sort of 149 00:08:22,040 --> 00:08:25,000 Speaker 3: manage throughout the architecture and watch, you know, this call 150 00:08:25,040 --> 00:08:29,280 Speaker 3: as as it's passing through all these different checkpoints, and 151 00:08:29,520 --> 00:08:31,040 Speaker 3: we can do it in a way that you still 152 00:08:31,040 --> 00:08:32,920 Speaker 3: have the confidence that it's best to breed right, so 153 00:08:32,960 --> 00:08:35,600 Speaker 3: you're not making any trade offs. But it's not so 154 00:08:35,640 --> 00:08:39,920 Speaker 3: simple just to get from the spaghetti to the seamless architecture. 155 00:08:39,960 --> 00:08:43,440 Speaker 3: You need, oftentimes to re engineer your business processes. You 156 00:08:43,520 --> 00:08:46,719 Speaker 3: have to re architect your digital environment. And so that's 157 00:08:46,720 --> 00:08:49,600 Speaker 3: where we partner with a company like IBM to bring 158 00:08:49,600 --> 00:08:51,760 Speaker 3: that expertise and say we're going to help you not 159 00:08:51,920 --> 00:08:54,800 Speaker 3: just deploy the best cybersecurity architecture, but really get your 160 00:08:54,880 --> 00:08:57,680 Speaker 3: environment ready to have this zero testomer. 161 00:08:57,360 --> 00:09:01,000 Speaker 2: As well as all of those players that cross at spaghetti. 162 00:09:01,679 --> 00:09:04,360 Speaker 2: Because when you start thinking about all the other partners 163 00:09:04,400 --> 00:09:06,360 Speaker 2: that you work with, if you're you think of an 164 00:09:06,360 --> 00:09:09,360 Speaker 2: industry perspective, you're going to have an ERP. It could 165 00:09:09,360 --> 00:09:12,080 Speaker 2: be an Oracle, it could be an SAP. You're not 166 00:09:12,080 --> 00:09:13,720 Speaker 2: going to have one cloud, as I mentioned, it's gonna 167 00:09:13,720 --> 00:09:18,200 Speaker 2: be possibly multiple clouds. You'll have some AWS maybe Microsoft Azure, 168 00:09:18,200 --> 00:09:20,360 Speaker 2: and then even even some Google in there, and then 169 00:09:20,400 --> 00:09:23,320 Speaker 2: your own that you've built in your private over there, uh, 170 00:09:24,240 --> 00:09:28,200 Speaker 2: some an IBM cloud. You'll have those multiple clouds, and 171 00:09:28,240 --> 00:09:31,000 Speaker 2: then you also will have you know, fit for purpose. 172 00:09:31,120 --> 00:09:33,640 Speaker 2: Oh I need a I need a salesforce in there 173 00:09:33,880 --> 00:09:36,800 Speaker 2: for my customer focusing. I need I'm doing some graphics, 174 00:09:36,800 --> 00:09:38,800 Speaker 2: so I have Adobe, so I just as I can 175 00:09:38,960 --> 00:09:42,440 Speaker 2: name name name, all of those then have to be 176 00:09:43,120 --> 00:09:46,080 Speaker 2: re engineered. Seriously. I mean, come on, Malcolm, You're gonna 177 00:09:46,080 --> 00:09:48,480 Speaker 2: sit there. You think how long that would take. So 178 00:09:48,920 --> 00:09:52,679 Speaker 2: if you haven't done that before, you're going to have 179 00:09:52,720 --> 00:09:54,320 Speaker 2: to go to each one of those individually, or you 180 00:09:54,360 --> 00:09:57,400 Speaker 2: can work with a company that can tie those things together, 181 00:09:57,480 --> 00:10:01,240 Speaker 2: because we are also strategic partners with them. So that's 182 00:10:01,280 --> 00:10:04,360 Speaker 2: where you start to say, Okay, I see how this 183 00:10:04,480 --> 00:10:09,120 Speaker 2: comes together. You have to make sure that your ecosystem 184 00:10:09,200 --> 00:10:12,040 Speaker 2: is going to be stronger than your competitor's ecosystem, and 185 00:10:12,120 --> 00:10:14,439 Speaker 2: you have to be secure in what you're doing because 186 00:10:14,480 --> 00:10:17,760 Speaker 2: as you add more players or products, you create seams, 187 00:10:18,320 --> 00:10:20,920 Speaker 2: and you want to make sure there's fewer seams and 188 00:10:20,960 --> 00:10:25,800 Speaker 2: that there's zero trust across that capability you're building. And 189 00:10:25,840 --> 00:10:28,280 Speaker 2: that's why the compliment between the two companies. 190 00:10:28,440 --> 00:10:30,480 Speaker 1: We'll take a step back from a moment before we 191 00:10:30,559 --> 00:10:33,320 Speaker 1: sort of launch. Once get into the specifics of what 192 00:10:33,400 --> 00:10:37,240 Speaker 1: you guys are doing. I'm curious at this moment in 193 00:10:37,360 --> 00:10:43,720 Speaker 1: twenty twenty four, how nervous should we be about cybersecurity? 194 00:10:43,920 --> 00:10:48,080 Speaker 1: So compared it to five years ago or ten years ago, 195 00:10:48,600 --> 00:10:50,600 Speaker 1: are we are you less nervous than you were five 196 00:10:50,679 --> 00:10:53,320 Speaker 1: years ago or more nervous or all of changes going 197 00:10:53,360 --> 00:10:57,800 Speaker 1: on right now increasing vulnerability or decreasing it? 198 00:10:58,200 --> 00:11:01,680 Speaker 2: I would say Chris, also, I think we share the 199 00:11:01,720 --> 00:11:05,840 Speaker 2: point of views that it's not necessarily being more nervous. 200 00:11:05,880 --> 00:11:09,800 Speaker 2: I think you should be more prepared. Yeah, because the 201 00:11:09,880 --> 00:11:15,240 Speaker 2: amounts of threat is increasing based on our dependence upon data. 202 00:11:16,320 --> 00:11:20,640 Speaker 2: And that's that's where I think the attention should be placed. 203 00:11:20,679 --> 00:11:23,800 Speaker 2: Is that more and more, especially with the importance of 204 00:11:23,840 --> 00:11:27,800 Speaker 2: AI that you say, okay, then what's under all that? 205 00:11:27,840 --> 00:11:31,760 Speaker 2: And it's the data? As I said, so knowing that 206 00:11:32,640 --> 00:11:33,840 Speaker 2: you should be more concerned. 207 00:11:35,240 --> 00:11:39,240 Speaker 1: Does the advent of AI and it's rapid evolution help 208 00:11:39,360 --> 00:11:41,160 Speaker 1: defense more or offense more? 209 00:11:41,720 --> 00:11:44,600 Speaker 3: I think it's I think it's like any mega trend 210 00:11:44,600 --> 00:11:48,680 Speaker 3: that we've witnessed both. Right, So you think about AI, 211 00:11:48,800 --> 00:11:52,800 Speaker 3: it's ninety nine percent great right in terms of what 212 00:11:52,840 --> 00:11:55,920 Speaker 3: it's going to unlock for productivity for humanity, But it 213 00:11:56,000 --> 00:11:58,559 Speaker 3: also makes it a whole lot easier to build ransomware. 214 00:11:58,760 --> 00:12:01,720 Speaker 3: It's a whole lot easier to take different ways into 215 00:12:01,760 --> 00:12:04,200 Speaker 3: a system, right. But I think that's true if you 216 00:12:04,240 --> 00:12:06,040 Speaker 3: think about like the rise of the Internet, right, all 217 00:12:06,080 --> 00:12:09,600 Speaker 3: of a sudden, everyone was putting their data online and 218 00:12:09,679 --> 00:12:11,920 Speaker 3: you had to think of new ways to stay ahead 219 00:12:11,960 --> 00:12:13,599 Speaker 3: and keep that secure. And I don't think AI is 220 00:12:13,640 --> 00:12:17,320 Speaker 3: any different. You've got companies like Palta, partnerships like Powell 221 00:12:17,360 --> 00:12:22,600 Speaker 3: and IBM that are constantly scanning the landscape for not 222 00:12:22,640 --> 00:12:25,160 Speaker 3: only the current threats, but what's next, what's coming around 223 00:12:25,160 --> 00:12:28,000 Speaker 3: the corner, what's after AI? And so I think taking 224 00:12:28,040 --> 00:12:30,360 Speaker 3: it seriously and being prepared is probably the right way 225 00:12:30,360 --> 00:12:32,719 Speaker 3: of looking at it, as opposed to because if you 226 00:12:32,720 --> 00:12:35,360 Speaker 3: think about it too hard, you'll just want to crawl 227 00:12:35,400 --> 00:12:39,360 Speaker 3: into a corner and stuff everything under the mattress. 228 00:12:39,760 --> 00:12:46,959 Speaker 1: I am the CEO of a regional hospital chain, big 229 00:12:47,120 --> 00:12:52,160 Speaker 1: distribute healthcare system, so a ton of data. The consequences 230 00:12:52,160 --> 00:12:56,120 Speaker 1: of being hacked and help for ransom are life and death. 231 00:12:56,280 --> 00:13:00,640 Speaker 1: Life and death. When you come so you you come down, 232 00:13:00,720 --> 00:13:02,480 Speaker 1: You sit down with me, and you chat with me. 233 00:13:03,880 --> 00:13:06,200 Speaker 1: Walk me through the kinds of things you would tell 234 00:13:06,240 --> 00:13:09,320 Speaker 1: me about what I need to get safer. For example, 235 00:13:09,480 --> 00:13:12,360 Speaker 1: let's start with one. Is it likely that I'm spending 236 00:13:12,360 --> 00:13:14,520 Speaker 1: too little or am I spending money in the wrong place? 237 00:13:15,400 --> 00:13:19,440 Speaker 3: Great question, It depends how you've broken it out. If 238 00:13:19,480 --> 00:13:22,640 Speaker 3: you are distributing all of your dollars across a whole 239 00:13:22,640 --> 00:13:25,200 Speaker 3: bunch of different tools, it's likely you're just spending the 240 00:13:25,240 --> 00:13:27,480 Speaker 3: wrong money. And in fact, you know, putting it all 241 00:13:27,520 --> 00:13:30,280 Speaker 3: in one place is a way of potentially saving money 242 00:13:30,640 --> 00:13:34,480 Speaker 3: but keeping your security actually higher. And I'd love to 243 00:13:34,480 --> 00:13:36,320 Speaker 3: hear Jason, how you would approach it. How we would 244 00:13:36,320 --> 00:13:38,520 Speaker 3: approach it, of course, is by saying, you know what, 245 00:13:38,520 --> 00:13:41,640 Speaker 3: what does your environment look like? You know, do you 246 00:13:41,720 --> 00:13:46,160 Speaker 3: have the connected medical devices into your EMR? Are your 247 00:13:46,280 --> 00:13:49,760 Speaker 3: respirators and ventilators all online? Right? And so we would 248 00:13:49,800 --> 00:13:52,640 Speaker 3: talk about, okay, here's how you get coverage, and how 249 00:13:52,679 --> 00:13:55,200 Speaker 3: the coverage of both the firewalls as well as the 250 00:13:55,280 --> 00:13:58,679 Speaker 3: detectors all feedback into your security operation center and you 251 00:13:58,720 --> 00:14:01,319 Speaker 3: can manage it and and do your learning with AI 252 00:14:02,360 --> 00:14:03,480 Speaker 3: and keep yourself securing. 253 00:14:03,520 --> 00:14:05,800 Speaker 2: So yeah, and I would say Christy and I would 254 00:14:05,840 --> 00:14:08,280 Speaker 2: go to the same point because if you get under 255 00:14:08,559 --> 00:14:12,080 Speaker 2: what she was just asking it is your data on 256 00:14:12,160 --> 00:14:16,320 Speaker 2: prem and when it's on prem, how active is it 257 00:14:16,360 --> 00:14:20,520 Speaker 2: across the enterprise? And so that begins the basis for 258 00:14:20,560 --> 00:14:22,520 Speaker 2: the start. And then often you're going to say, well, 259 00:14:22,520 --> 00:14:25,720 Speaker 2: we actually take in data from outside and then we 260 00:14:25,760 --> 00:14:29,240 Speaker 2: also have the circumstances. There's a lot of PII and 261 00:14:29,320 --> 00:14:34,520 Speaker 2: so that personal is the personal information, right, Yeah, And 262 00:14:34,600 --> 00:14:37,840 Speaker 2: so now you're saying, okay, now, how are we securing 263 00:14:37,920 --> 00:14:40,760 Speaker 2: that and where are we securing it? And so you 264 00:14:40,840 --> 00:14:44,640 Speaker 2: have to start really thinking about the different areas within 265 00:14:44,960 --> 00:14:49,040 Speaker 2: that hospital chain. Are you sharing that amongst your hospitals? 266 00:14:49,600 --> 00:14:52,760 Speaker 2: And now you start to think of if I'm saying 267 00:14:52,800 --> 00:14:54,240 Speaker 2: no to a lot of that, it's like, well, then 268 00:14:54,280 --> 00:14:56,920 Speaker 2: are you as efficient as you want to be? So 269 00:14:57,000 --> 00:15:00,520 Speaker 2: there is that trade off of you know, am I 270 00:15:00,560 --> 00:15:04,120 Speaker 2: so tightly walled that I'm not productive? And so that's 271 00:15:04,160 --> 00:15:07,240 Speaker 2: where we would start to say, what's the outcome that 272 00:15:07,280 --> 00:15:09,400 Speaker 2: you're trying to get to? All Right, maybe you're good, 273 00:15:09,440 --> 00:15:11,800 Speaker 2: Maybe you're you're good with your five locations and you 274 00:15:11,840 --> 00:15:14,320 Speaker 2: don't need to go any further, but maybe you want 275 00:15:14,360 --> 00:15:16,400 Speaker 2: to expand to fifty and by the way, you're going 276 00:15:16,440 --> 00:15:18,160 Speaker 2: to go crossport or you're going to be in Toronto 277 00:15:18,480 --> 00:15:21,640 Speaker 2: and in New York. Okay, well then how do you 278 00:15:21,720 --> 00:15:25,160 Speaker 2: do that? And so I think that it's very easy 279 00:15:25,200 --> 00:15:30,560 Speaker 2: to start jumping into any of the typical situations. But 280 00:15:30,680 --> 00:15:33,400 Speaker 2: the first question that you have to ask you, as 281 00:15:33,480 --> 00:15:38,200 Speaker 2: the hospital CEOs, what's your objective? What are you what 282 00:15:38,240 --> 00:15:40,840 Speaker 2: are you trying to do? Because too often what we 283 00:15:40,920 --> 00:15:44,240 Speaker 2: see is that there's some bright, new, shiny thing that 284 00:15:44,320 --> 00:15:47,040 Speaker 2: everybody wants to put in play. You know, it's a 285 00:15:47,080 --> 00:15:50,520 Speaker 2: sandwich looking for lunch, and you go, but what is 286 00:15:50,560 --> 00:15:52,600 Speaker 2: it that you want to do as this Are you 287 00:15:52,640 --> 00:15:56,520 Speaker 2: doing research? Are your research hospital? Are you more consumer oriented? 288 00:15:56,800 --> 00:15:58,960 Speaker 2: So those are the questions you start to ask because 289 00:15:59,000 --> 00:16:01,800 Speaker 2: they start to then tell a story in line with 290 00:16:01,800 --> 00:16:05,480 Speaker 2: what Christy questions. And I think that that's where the again, 291 00:16:05,520 --> 00:16:08,600 Speaker 2: the complement is that instead of just saying, oh, well, 292 00:16:08,640 --> 00:16:11,280 Speaker 2: that's thanks for telling me all this, Malcolm, here's your 293 00:16:11,520 --> 00:16:16,440 Speaker 2: ten page strategy, go find somebody. We have the benefit 294 00:16:17,120 --> 00:16:19,480 Speaker 2: in IBM, and is probably why I'm still there is 295 00:16:19,560 --> 00:16:22,160 Speaker 2: you know, we're very unique. We're the only company on 296 00:16:22,200 --> 00:16:27,000 Speaker 2: the planet that has a consulting business at scale inside 297 00:16:27,000 --> 00:16:30,880 Speaker 2: of a technology company, and so we have you know, 298 00:16:31,120 --> 00:16:33,160 Speaker 2: the left brain right brain. We're able to do that, 299 00:16:33,720 --> 00:16:35,880 Speaker 2: and then we're able to say, okay, now which partners 300 00:16:36,040 --> 00:16:40,160 Speaker 2: are going to be most valuable for our clients. What's 301 00:16:40,160 --> 00:16:41,680 Speaker 2: going to work for you? Isn't going to work for 302 00:16:41,720 --> 00:16:44,000 Speaker 2: the manufacturer down the road, isn't going to work for 303 00:16:44,040 --> 00:16:49,000 Speaker 2: the consumer or CpG company across the river. Those things 304 00:16:49,040 --> 00:16:52,760 Speaker 2: are very specific. The threats and the scenes that I 305 00:16:52,800 --> 00:16:56,000 Speaker 2: was talking about are very specific. So that's where it 306 00:16:56,040 --> 00:16:59,400 Speaker 2: becomes very valuable to make sure that I'm not just 307 00:16:59,600 --> 00:17:02,400 Speaker 2: giving you some strategy that's generic. 308 00:17:02,400 --> 00:17:07,200 Speaker 1: But everything as a healthcare CEO, everything I have done, 309 00:17:07,640 --> 00:17:11,119 Speaker 1: almost everything I've done over the last ten years, hasn't 310 00:17:11,119 --> 00:17:13,640 Speaker 1: it had the effect of increasing my vulnerability. I want 311 00:17:13,680 --> 00:17:16,440 Speaker 1: to digitize data within the hospital used to be on 312 00:17:16,480 --> 00:17:20,040 Speaker 1: pieces of paper. I want doctors to go home and 313 00:17:20,160 --> 00:17:22,160 Speaker 1: to be able to seamlessly hook into stuff at work 314 00:17:22,160 --> 00:17:24,680 Speaker 1: because they got to do all their paperwork. I want 315 00:17:24,720 --> 00:17:27,399 Speaker 1: to make sure the diabetes people are speaking to the 316 00:17:27,520 --> 00:17:31,199 Speaker 1: organ transplant people. And so isn't that everything I have 317 00:17:31,280 --> 00:17:34,560 Speaker 1: done to kind of keep up with the revolution in healthcare? 318 00:17:34,960 --> 00:17:37,280 Speaker 1: Isn't that also making me more and more vulnerable to 319 00:17:37,840 --> 00:17:38,480 Speaker 1: a bad actor. 320 00:17:38,600 --> 00:17:40,960 Speaker 3: It's such a great question because think about the quality 321 00:17:41,000 --> 00:17:44,399 Speaker 3: of healthcare delivery. Right. So now doctors aren't filling out forms, 322 00:17:44,400 --> 00:17:47,159 Speaker 3: they're spending time with patients, and so the quality of 323 00:17:47,200 --> 00:17:49,800 Speaker 3: care is improving and the vulnerability is improving, right, And 324 00:17:49,800 --> 00:17:53,679 Speaker 3: so I think that's where having a strong cybersecurity strategy 325 00:17:54,320 --> 00:17:56,879 Speaker 3: actually enables all of that. One of our products is 326 00:17:56,920 --> 00:17:59,359 Speaker 3: our sas product, and we tested it with some business 327 00:17:59,400 --> 00:18:02,560 Speaker 3: application and oftentimes the wrap is, oh, security is going 328 00:18:02,600 --> 00:18:04,760 Speaker 3: to slow you down, like you have to add a firewall, 329 00:18:04,760 --> 00:18:08,639 Speaker 3: you have to checkpoints. Our product actually increases the velocity 330 00:18:08,760 --> 00:18:11,199 Speaker 3: of your ability to use that application because of the 331 00:18:11,200 --> 00:18:15,159 Speaker 3: way that it is queried through our system as opposed 332 00:18:15,160 --> 00:18:17,720 Speaker 3: to just through the regular network. So it doesn't slow 333 00:18:17,720 --> 00:18:19,959 Speaker 3: it down and in fact, it makes it run more efficiently. 334 00:18:20,720 --> 00:18:25,919 Speaker 3: That's just one minor example. But back to the healthcare question. I, 335 00:18:26,000 --> 00:18:28,359 Speaker 3: as a patient want my doctors accessing all the technology 336 00:18:28,359 --> 00:18:30,440 Speaker 3: and talking to each other and connecting the dots behind 337 00:18:30,440 --> 00:18:33,040 Speaker 3: the scenes. I also want my data to stay private, 338 00:18:33,560 --> 00:18:38,720 Speaker 3: and so having both a consulting partner who understands how 339 00:18:38,760 --> 00:18:41,159 Speaker 3: to ask questions of the environment and of the applications 340 00:18:41,200 --> 00:18:44,040 Speaker 3: you're using, and who understands the industry inside and out 341 00:18:44,480 --> 00:18:47,280 Speaker 3: and a technology partner that builds and stays ahead of 342 00:18:47,320 --> 00:18:49,959 Speaker 3: all of the different threats, come together and advise you. 343 00:18:50,000 --> 00:18:53,960 Speaker 3: I think is super important. When you bring in a 344 00:18:54,000 --> 00:18:58,040 Speaker 3: partner like IBM, with a platform like Palalta that covers 345 00:18:58,680 --> 00:19:03,240 Speaker 3: all the different parts of your environment, you're able to say, look, 346 00:19:03,320 --> 00:19:06,720 Speaker 3: where where are the vulnerabilities in the system, Where are 347 00:19:06,760 --> 00:19:09,760 Speaker 3: the different endpoints that we need to have covered, and 348 00:19:09,800 --> 00:19:11,760 Speaker 3: then just make sure you get that breadth of coverage, 349 00:19:12,600 --> 00:19:14,919 Speaker 3: and then you're better able to so, yes, you've increased 350 00:19:14,960 --> 00:19:16,520 Speaker 3: the risk, but then you've mitigated it. 351 00:19:17,240 --> 00:19:21,560 Speaker 1: So to give so before I retire my healthcare analogy, 352 00:19:21,960 --> 00:19:25,239 Speaker 1: because I was thinking about just trying to understand the 353 00:19:25,280 --> 00:19:30,080 Speaker 1: importance of this idea of having a single platform. So 354 00:19:30,400 --> 00:19:33,720 Speaker 1: if this muddle healthcare network is typical, I've acquired a 355 00:19:33,720 --> 00:19:36,200 Speaker 1: whole series of over the last ten years. I bought 356 00:19:36,200 --> 00:19:39,040 Speaker 1: a hospital over here, some I got some physicians, things 357 00:19:39,040 --> 00:19:42,160 Speaker 1: that I snapped up over here about a diagnostics company, 358 00:19:42,640 --> 00:19:46,000 Speaker 1: and so I have all of these legacy systems, and 359 00:19:46,040 --> 00:19:48,200 Speaker 1: I had, like you said, maybe I got some stuff 360 00:19:48,240 --> 00:19:50,399 Speaker 1: in the cloud with one company, some stuff with the cloud. 361 00:19:50,640 --> 00:19:53,320 Speaker 1: And what you're saying is the first step is to 362 00:19:53,400 --> 00:19:57,240 Speaker 1: kind of rationalize that put it on a single platform, 363 00:19:57,280 --> 00:20:00,520 Speaker 1: so you understand where your points of weakness are as 364 00:20:00,520 --> 00:20:02,480 Speaker 1: opposed to being blind to your points of weakness. 365 00:20:04,280 --> 00:20:09,240 Speaker 3: There's yes, although anyone who's done any kind of M 366 00:20:09,240 --> 00:20:11,840 Speaker 3: and A knows that that's a long journey, right. So 367 00:20:11,920 --> 00:20:15,560 Speaker 3: I think the first step is just understanding where everything is. 368 00:20:16,200 --> 00:20:17,399 Speaker 3: And then you get on a path and you say, 369 00:20:17,400 --> 00:20:20,240 Speaker 3: where's the biggest risk. Let's let's neutralize or mitigate that 370 00:20:20,359 --> 00:20:23,880 Speaker 3: risk one at a time. The thing about open end secure, 371 00:20:24,640 --> 00:20:27,199 Speaker 3: you know Palo Alto. We keep touting the benefits of 372 00:20:27,200 --> 00:20:29,879 Speaker 3: the platform. Everything on Palo Alto, your risk is going 373 00:20:29,920 --> 00:20:31,879 Speaker 3: to be mitigated and you're going to have the full visibility. 374 00:20:32,560 --> 00:20:35,960 Speaker 3: But you can't get there overnight. And so we've got 375 00:20:36,160 --> 00:20:39,480 Speaker 3: you know, thousands of integrations with other technology companies, including 376 00:20:39,480 --> 00:20:42,119 Speaker 3: our partners, to make sure that we can capture and 377 00:20:42,320 --> 00:20:46,520 Speaker 3: have visibility into those those endpoints in those systems as well. 378 00:20:46,720 --> 00:20:48,840 Speaker 3: And so I think step one is just figure out 379 00:20:48,840 --> 00:20:51,040 Speaker 3: where everything is. Just get the scan. So Polta has 380 00:20:51,080 --> 00:20:52,919 Speaker 3: a couple of products where you can kind of deploy 381 00:20:52,960 --> 00:20:55,720 Speaker 3: and get a view of your attack surface. I love 382 00:20:55,760 --> 00:20:58,920 Speaker 3: the analogy. Just like a digital environment is a house right, 383 00:20:58,960 --> 00:21:01,000 Speaker 3: and so like you have your front lock, of course, 384 00:21:01,200 --> 00:21:03,119 Speaker 3: because probably they're going to try the front door first. 385 00:21:03,880 --> 00:21:05,400 Speaker 3: But that's not all you're going to do, right, You're 386 00:21:05,400 --> 00:21:07,000 Speaker 3: going to make sure the whole you know, the windows 387 00:21:07,040 --> 00:21:09,160 Speaker 3: are locked and there's an alarm system and all of that. 388 00:21:10,520 --> 00:21:12,560 Speaker 3: And I think that's how you have to think about it, 389 00:21:12,680 --> 00:21:14,520 Speaker 3: is just how do we cover the whole service? 390 00:21:15,119 --> 00:21:18,520 Speaker 1: So everyone laid, people like me have been bombarded over 391 00:21:18,640 --> 00:21:22,040 Speaker 1: it seems like over the last year with one thing 392 00:21:22,080 --> 00:21:25,320 Speaker 1: another about how quickly AI is moving forward and how 393 00:21:25,400 --> 00:21:27,040 Speaker 1: big of a deal it is suddenly is going to 394 00:21:27,040 --> 00:21:31,200 Speaker 1: be in the economy. What is the impact of that 395 00:21:32,160 --> 00:21:38,119 Speaker 1: dramatic change in AI's capabilities on this cybersecurity question? So 396 00:21:38,240 --> 00:21:40,600 Speaker 1: what does it mean if you're defending somebody that you 397 00:21:40,720 --> 00:21:43,560 Speaker 1: now have these sophisticated AI tools you just suppose. 398 00:21:44,520 --> 00:21:47,800 Speaker 2: I think that AI becomes the force multiplier for cyber 399 00:21:49,520 --> 00:21:54,240 Speaker 2: To think about cyber Before it was just locking your doors, 400 00:21:54,760 --> 00:21:57,800 Speaker 2: locking the windows, and if you were really good, you 401 00:21:57,840 --> 00:22:03,800 Speaker 2: had an alarm system. You know, Now with AI, you 402 00:22:03,840 --> 00:22:06,879 Speaker 2: can say, well, I can predict what's going to happen, 403 00:22:07,000 --> 00:22:09,040 Speaker 2: I can see around the corner. I know, I can 404 00:22:09,119 --> 00:22:12,399 Speaker 2: leave my windows open upstairs and it's fine and it's okay. 405 00:22:12,640 --> 00:22:15,240 Speaker 1: I mean because why because the AI is running a 406 00:22:15,280 --> 00:22:18,760 Speaker 1: million simulations. 407 00:22:17,040 --> 00:22:21,240 Speaker 2: It can And that's exactly it. It becomes the intelligent 408 00:22:21,400 --> 00:22:25,240 Speaker 2: part of that AI. It's not artificial, it's augmented. So 409 00:22:25,280 --> 00:22:28,200 Speaker 2: you now have this new capability to see around corners 410 00:22:28,880 --> 00:22:32,120 Speaker 2: and so you're able to do the jobs of yesterday 411 00:22:32,160 --> 00:22:38,040 Speaker 2: more effectively. And the queries that you were doing and 412 00:22:38,080 --> 00:22:41,359 Speaker 2: that's all you're really doing, now you're doing them, you know, faster, 413 00:22:41,600 --> 00:22:45,320 Speaker 2: You're able to access even more data and you're able 414 00:22:45,359 --> 00:22:49,920 Speaker 2: to then make it more secure. So that's why AI 415 00:22:50,040 --> 00:22:51,359 Speaker 2: becomes a force multiplier. 416 00:22:51,640 --> 00:22:55,680 Speaker 1: Yeah, and just talk about the faster part. What does 417 00:22:55,880 --> 00:22:59,399 Speaker 1: faster mean in practical terms? If you're trying to defend 418 00:22:59,440 --> 00:23:02,160 Speaker 1: an enterprise against a cyber attack, what does speed matter 419 00:23:02,240 --> 00:23:02,920 Speaker 1: in that environment? 420 00:23:03,920 --> 00:23:07,439 Speaker 2: You're always trying to find a place through I go 421 00:23:07,480 --> 00:23:09,639 Speaker 2: back to you. We brought up the army. You always 422 00:23:09,760 --> 00:23:11,440 Speaker 2: how do you break the line? How do you find 423 00:23:11,480 --> 00:23:14,119 Speaker 2: a penetration point? And when you think about you know, 424 00:23:14,600 --> 00:23:18,560 Speaker 2: pin testing, penetration testing, where are those? So if you're 425 00:23:18,560 --> 00:23:21,359 Speaker 2: able to do that faster than the bad guys, and 426 00:23:21,400 --> 00:23:25,000 Speaker 2: not only faster, but you're picking more probable points. This 427 00:23:25,160 --> 00:23:28,080 Speaker 2: is back to the intelligence. I could waste time doing 428 00:23:28,240 --> 00:23:31,320 Speaker 2: penetration testing someplace where. That's why I mentioned leave. If 429 00:23:31,320 --> 00:23:34,160 Speaker 2: they can't get in the second story windows, why are 430 00:23:34,160 --> 00:23:37,480 Speaker 2: you spending time trying it? So that becomes more effective. 431 00:23:38,280 --> 00:23:41,040 Speaker 2: So that's when I think of speed. That's what I 432 00:23:41,080 --> 00:23:43,560 Speaker 2: think of because with not just speed, I think it's 433 00:23:43,560 --> 00:23:45,120 Speaker 2: also what's effective. 434 00:23:45,040 --> 00:23:46,639 Speaker 3: Just to put a put a fine point on it. 435 00:23:46,800 --> 00:23:48,840 Speaker 3: So I found a way in. Okay, Now what I 436 00:23:48,840 --> 00:23:50,480 Speaker 3: don't know where the jewelry is, so I have to 437 00:23:50,520 --> 00:23:53,439 Speaker 3: look around and see if there's any hidden gems and 438 00:23:53,440 --> 00:23:55,600 Speaker 3: try to find my way. That used to take a week, 439 00:23:55,640 --> 00:23:58,360 Speaker 3: two weeks or of seven to fourteen days. Now it's 440 00:23:58,520 --> 00:24:01,400 Speaker 3: hours right so there in and they can actually expltrate 441 00:24:01,480 --> 00:24:05,320 Speaker 3: data within less than a day. The metric we use 442 00:24:05,320 --> 00:24:08,080 Speaker 3: in the security operation center is meantime to detect, so 443 00:24:08,119 --> 00:24:11,119 Speaker 3: to see anyone's there, meantime to respond and remediate to 444 00:24:11,119 --> 00:24:14,639 Speaker 3: get them out. Right. That used to be also you know, 445 00:24:15,000 --> 00:24:18,679 Speaker 3: seven eight, nine, ten days. Now it needs to be 446 00:24:19,080 --> 00:24:22,720 Speaker 3: less than an hour. And with our AI based security 447 00:24:22,760 --> 00:24:26,119 Speaker 3: operations platform, it is. Now you've got one tool that 448 00:24:26,359 --> 00:24:29,200 Speaker 3: whether it's all Peloton networks or whether it's just you know, 449 00:24:29,280 --> 00:24:31,840 Speaker 3: hoofringing data from other places. Then you're able to see 450 00:24:31,840 --> 00:24:33,840 Speaker 3: it all together, so you actually get fewer alerts. So 451 00:24:33,840 --> 00:24:38,040 Speaker 3: you get from thousands of alerts down to one hundred alerts, right, 452 00:24:38,080 --> 00:24:40,120 Speaker 3: and you can investigate them, and you investigate them using 453 00:24:40,160 --> 00:24:43,960 Speaker 3: AI too. And AI is today it's today's threat, but 454 00:24:44,000 --> 00:24:46,240 Speaker 3: it's you know, you think about threat and opportunity to 455 00:24:46,280 --> 00:24:48,240 Speaker 3: think about what's next. You always have to be kind 456 00:24:48,280 --> 00:24:49,720 Speaker 3: of evolving and you have to think. 457 00:24:50,119 --> 00:24:52,160 Speaker 2: We talk about threat and risk, and you know, we 458 00:24:52,359 --> 00:24:55,760 Speaker 2: didn't tell you know, what is the cost of cyber 459 00:24:56,320 --> 00:25:00,159 Speaker 2: some type of penetration. It's typical cost is for four 460 00:25:00,200 --> 00:25:04,800 Speaker 2: and a half million dollars and that's just in labor 461 00:25:05,119 --> 00:25:09,800 Speaker 2: and remediation. If you think about reputational risk as well, 462 00:25:10,240 --> 00:25:12,640 Speaker 2: our Institute for Business Value to the study and found 463 00:25:12,640 --> 00:25:15,680 Speaker 2: it in twenty twenty three and they were thirty nine 464 00:25:15,720 --> 00:25:21,800 Speaker 2: banks that we watched that suffered a reputational risk market 465 00:25:21,880 --> 00:25:24,760 Speaker 2: value of one hundred and thirty billion dollars. And so 466 00:25:24,800 --> 00:25:29,600 Speaker 2: you start to think, wow, that's just reputational risk. So 467 00:25:29,640 --> 00:25:32,600 Speaker 2: that's what's at stake here, and it's only that is 468 00:25:32,640 --> 00:25:33,760 Speaker 2: only going to get bigger. 469 00:25:34,560 --> 00:25:37,080 Speaker 3: So one of the piece we haven't talked about about 470 00:25:37,119 --> 00:25:39,520 Speaker 3: AI that I find super interesting because we've been talking 471 00:25:39,640 --> 00:25:43,320 Speaker 3: essentially about like the terminator, the robots fighting robots, right, 472 00:25:43,400 --> 00:25:46,080 Speaker 3: Like whose robots are quicker? Like I'm designing a tax 473 00:25:46,119 --> 00:25:48,320 Speaker 3: and I'm defending against tax and I think that's that's 474 00:25:48,359 --> 00:25:52,520 Speaker 3: super important. But we recently launch and our working at 475 00:25:52,560 --> 00:25:55,359 Speaker 3: IBM on our AI security product to actually secure the 476 00:25:55,440 --> 00:25:57,560 Speaker 3: use of AILA because it also opens up another set 477 00:25:57,600 --> 00:26:01,520 Speaker 3: of threat factors. I'll give you an exampimple. I'm a 478 00:26:01,560 --> 00:26:04,280 Speaker 3: marketing executive now for your hospital. So I work for you, 479 00:26:04,960 --> 00:26:08,639 Speaker 3: and you want to announce the launch of a new center, 480 00:26:09,160 --> 00:26:11,600 Speaker 3: and so I upload all the information about all the 481 00:26:11,640 --> 00:26:13,840 Speaker 3: patients and our you know how we do things into 482 00:26:13,880 --> 00:26:15,840 Speaker 3: chat GPT to write the PR for me. Well, I've 483 00:26:15,880 --> 00:26:19,000 Speaker 3: also just uploaded to chat GPT a whole bunch of secrets, right. 484 00:26:19,560 --> 00:26:23,000 Speaker 3: So it's it's how employees are using AI, because I think, 485 00:26:23,040 --> 00:26:25,199 Speaker 3: you know, some companies are sort of building their own 486 00:26:25,240 --> 00:26:27,440 Speaker 3: language models and their own AI applications that they want 487 00:26:27,440 --> 00:26:30,320 Speaker 3: to keep secure. Others are just curious about how their 488 00:26:30,320 --> 00:26:33,800 Speaker 3: employees are using AI applications on the shelf. And so 489 00:26:33,880 --> 00:26:36,199 Speaker 3: we announced in May a product where you can actually 490 00:26:36,960 --> 00:26:39,640 Speaker 3: scan and see how AI is being used in your 491 00:26:39,880 --> 00:26:42,919 Speaker 3: enterprise and within We made the announcement with the GA 492 00:26:42,960 --> 00:26:44,680 Speaker 3: was last month, but we made the announcement in May, 493 00:26:44,720 --> 00:26:47,800 Speaker 3: and we had immediately thousands of CIOs signing up because 494 00:26:47,840 --> 00:26:51,200 Speaker 3: just understanding you know who's using what it's another open 495 00:26:51,280 --> 00:26:54,840 Speaker 3: question because you know, we talk about AI enhancing productivity 496 00:26:54,880 --> 00:26:56,399 Speaker 3: and all the benefits it's going to bring, but it 497 00:26:56,440 --> 00:26:59,200 Speaker 3: brings it brings risks, not just in how it's being 498 00:26:59,280 --> 00:27:02,000 Speaker 3: used by the threat actors, but also you know what 499 00:27:02,119 --> 00:27:03,199 Speaker 3: other vulnerabilities that. 500 00:27:03,160 --> 00:27:07,160 Speaker 1: Excuse is the eye that you does that system tell 501 00:27:07,240 --> 00:27:08,720 Speaker 1: you what's a problematic use? 502 00:27:09,480 --> 00:27:11,879 Speaker 3: It does, so what what it does, and you've got 503 00:27:11,920 --> 00:27:13,640 Speaker 3: to train it right. But what it does is say 504 00:27:13,680 --> 00:27:16,720 Speaker 3: this is this is outside of your policy. So CIOs 505 00:27:16,720 --> 00:27:19,240 Speaker 3: will set policies on here's what is acceptable and not 506 00:27:19,280 --> 00:27:21,160 Speaker 3: acceptable use. So we'll be able to scan and say 507 00:27:21,200 --> 00:27:23,880 Speaker 3: these these falling uses are outside of policy, and then 508 00:27:23,920 --> 00:27:25,920 Speaker 3: it'll punt and say I think this is too restrictive, 509 00:27:25,920 --> 00:27:27,520 Speaker 3: I think this is too permissive, and then you can 510 00:27:27,560 --> 00:27:31,200 Speaker 3: sort of update your policies from there. That's just sort 511 00:27:31,200 --> 00:27:33,680 Speaker 3: of the visibility piece, and then there's the run time piece, 512 00:27:33,720 --> 00:27:35,639 Speaker 3: which will actually stop you from using it. So you 513 00:27:35,680 --> 00:27:38,359 Speaker 3: go and say, Okay, here's all my patient's social security numbers. 514 00:27:38,359 --> 00:27:40,960 Speaker 3: I'm going to upload them to chat GPT to you know, 515 00:27:41,560 --> 00:27:43,840 Speaker 3: get an understanding of like where they all live. I 516 00:27:43,840 --> 00:27:46,040 Speaker 3: don't know what why you would possibly do that, but 517 00:27:46,119 --> 00:27:48,440 Speaker 3: let's say you were, and then you know, it'll note 518 00:27:48,520 --> 00:27:50,359 Speaker 3: that looks like a social security number. You can't upload 519 00:27:50,400 --> 00:27:51,639 Speaker 3: that into your prompt, so it. 520 00:27:51,640 --> 00:27:57,360 Speaker 1: Will stop before you Yeah, thoughtful voice over your shoulder, 521 00:27:57,960 --> 00:28:00,920 Speaker 1: just to remind you not to do something silly exactly. 522 00:28:01,280 --> 00:28:03,800 Speaker 1: But this is just talk a little bit more about 523 00:28:04,000 --> 00:28:07,840 Speaker 1: adding AI into this mix. You say it's a force multiplier. 524 00:28:07,920 --> 00:28:11,760 Speaker 1: It's a really interesting dig into that. What other instances 525 00:28:11,800 --> 00:28:12,960 Speaker 1: of what that means? 526 00:28:13,359 --> 00:28:13,520 Speaker 3: Well? 527 00:28:13,520 --> 00:28:20,040 Speaker 1: How does the balance between AI and human expertise work 528 00:28:20,280 --> 00:28:22,480 Speaker 1: in the kind of next generation of cybersecurity? 529 00:28:23,320 --> 00:28:27,520 Speaker 2: I think the common way to look at as it 530 00:28:27,760 --> 00:28:31,280 Speaker 2: back to the force multipliers, It's not going to be 531 00:28:31,480 --> 00:28:33,720 Speaker 2: is your AI better? But can you use it better? 532 00:28:34,119 --> 00:28:37,840 Speaker 2: Can you ask your AI the right questions? Are you 533 00:28:37,880 --> 00:28:41,360 Speaker 2: well trained? So the competition really becomes your use of AI? 534 00:28:42,160 --> 00:28:45,520 Speaker 2: And are you pointed it in the right direction? You 535 00:28:45,560 --> 00:28:48,600 Speaker 2: have fifty people can they do the work of two 536 00:28:48,720 --> 00:28:51,680 Speaker 2: hundred and fifty and can they do it in a 537 00:28:51,720 --> 00:28:54,800 Speaker 2: safe and secure manner, So you're not opening up more 538 00:28:54,880 --> 00:28:58,160 Speaker 2: risk based on or too much risk is your risk 539 00:28:58,200 --> 00:29:01,000 Speaker 2: tolerance in order to get the outcome. So that's why 540 00:29:01,120 --> 00:29:04,200 Speaker 2: I think there's the opportunity. And so you see this 541 00:29:04,800 --> 00:29:07,480 Speaker 2: truly as a force multiplier because the first thing people go, oh, 542 00:29:07,520 --> 00:29:10,360 Speaker 2: you're going to get rid of people, Oh, the people 543 00:29:10,440 --> 00:29:14,400 Speaker 2: portion is still still going to be just as important 544 00:29:14,400 --> 00:29:16,240 Speaker 2: because they're doing that other piece of work. 545 00:29:16,320 --> 00:29:18,480 Speaker 3: One of my favorite statistics is that there are now 546 00:29:18,600 --> 00:29:20,720 Speaker 3: more bank tellers in the US than there were in 547 00:29:20,800 --> 00:29:23,960 Speaker 3: nineteen sixty before the ATM was invented. Right, So, but 548 00:29:23,960 --> 00:29:25,640 Speaker 3: it used to be you would go to your bank 549 00:29:26,160 --> 00:29:28,480 Speaker 3: because you had to. I remember doing this. You go, 550 00:29:28,600 --> 00:29:30,400 Speaker 3: you fill out your deposit slip, you hand it to 551 00:29:30,440 --> 00:29:33,000 Speaker 3: the teller, and they give you your cash. And then 552 00:29:33,040 --> 00:29:34,840 Speaker 3: ATMs are invented, and it's like, oh, no, what's going 553 00:29:34,880 --> 00:29:36,880 Speaker 3: to happen all these jobs and now there's more, Right, 554 00:29:36,760 --> 00:29:40,480 Speaker 3: but you're not withdrawing money from a bank teller. You're 555 00:29:40,480 --> 00:29:43,920 Speaker 3: now doing more sophisticated transactions. And so I think it's 556 00:29:43,920 --> 00:29:46,520 Speaker 3: similar with AI, right, Like you want people doing things 557 00:29:46,520 --> 00:29:47,440 Speaker 3: that only people can do. 558 00:29:47,920 --> 00:29:50,720 Speaker 1: The human element remains absolutely central in all of this. 559 00:29:52,280 --> 00:29:56,880 Speaker 1: How do you make sure that your cybersecurity folks are 560 00:29:57,080 --> 00:29:59,920 Speaker 1: equipped to handle high value tasks, are ready for the 561 00:30:00,040 --> 00:30:01,400 Speaker 1: this increasing responsibility. 562 00:30:02,600 --> 00:30:04,480 Speaker 3: There's a couple of ways to answer this, but I 563 00:30:04,520 --> 00:30:08,960 Speaker 3: think the more you're able to automate the routine and 564 00:30:09,000 --> 00:30:13,880 Speaker 3: the mundane tasks. For example, the bulk of cybersecurity happens 565 00:30:13,920 --> 00:30:16,840 Speaker 3: in the security operations center. There's analysts who are sitting 566 00:30:16,840 --> 00:30:19,960 Speaker 3: in that center. If they're spending all day either configuring 567 00:30:21,000 --> 00:30:23,640 Speaker 3: alerts or responding to alerts, they're not able to do 568 00:30:23,680 --> 00:30:26,440 Speaker 3: the advanced sort of threat hunting and analysis work. And 569 00:30:26,440 --> 00:30:28,160 Speaker 3: so I think a big chunk of it is just 570 00:30:28,200 --> 00:30:30,920 Speaker 3: freeing up their time to be able to do the 571 00:30:30,920 --> 00:30:33,880 Speaker 3: more advanced strategic work. And a lot of the automation 572 00:30:33,960 --> 00:30:37,920 Speaker 3: tools based on AI, like our cortex XIM product, is 573 00:30:38,640 --> 00:30:40,800 Speaker 3: it's designed to free up their time in order to 574 00:30:40,840 --> 00:30:41,240 Speaker 3: be able. 575 00:30:41,120 --> 00:30:44,880 Speaker 2: To do that. And from our perspective, is making sure 576 00:30:45,200 --> 00:30:48,240 Speaker 2: that it's a requirement to make sure that you have 577 00:30:48,480 --> 00:30:51,440 Speaker 2: the qualifications because people can easily get used to doing 578 00:30:51,440 --> 00:30:54,640 Speaker 2: what they've always done. I know this, and that's what 579 00:30:54,680 --> 00:30:58,360 Speaker 2: I do you say, Well, now, all the threat actors 580 00:30:58,640 --> 00:31:01,560 Speaker 2: are learning on the fly. They're trying to always outsmart you. 581 00:31:01,680 --> 00:31:04,800 Speaker 2: So it's in your best interest, our best interest, our 582 00:31:04,840 --> 00:31:07,880 Speaker 2: client's best and partners best that you are on the 583 00:31:07,880 --> 00:31:11,120 Speaker 2: front leaning edge of that learning capability. 584 00:31:11,520 --> 00:31:13,479 Speaker 1: If you're talking to a client he wants to develop 585 00:31:13,560 --> 00:31:18,960 Speaker 1: a kind of unified cybersecurity strategy, what's the best single 586 00:31:19,000 --> 00:31:22,720 Speaker 1: piece of advice you can give them? 587 00:31:22,760 --> 00:31:26,840 Speaker 3: You should have a single platform. It's hard not to 588 00:31:26,920 --> 00:31:29,480 Speaker 3: answer that, but it is true. I mean all joking 589 00:31:29,520 --> 00:31:33,200 Speaker 3: aside having you know the best of breed solutions that 590 00:31:33,240 --> 00:31:35,160 Speaker 3: are all talking to each other and able to stitch 591 00:31:35,200 --> 00:31:38,479 Speaker 3: together and identify threats before human might be able to. 592 00:31:39,400 --> 00:31:41,800 Speaker 3: That's number one, and number two is making sure you 593 00:31:41,840 --> 00:31:45,400 Speaker 3: have visibility on all elements so you're able to cover 594 00:31:45,440 --> 00:31:48,080 Speaker 3: your whole environment and understand how people are accessing it. 595 00:31:48,320 --> 00:31:52,360 Speaker 2: I'd say, think like a bad actor. Yeah, always think 596 00:31:52,400 --> 00:31:55,680 Speaker 2: outside in because you get comfortable the other way around. 597 00:31:57,400 --> 00:32:00,760 Speaker 1: You guys work together with a Push and five company, 598 00:32:01,520 --> 00:32:03,280 Speaker 1: and I'd love for you to talk a little bit 599 00:32:03,320 --> 00:32:05,240 Speaker 1: about use that as a kind of case study for 600 00:32:05,680 --> 00:32:10,160 Speaker 1: what this collaboration between the two your two companies looks 601 00:32:10,280 --> 00:32:12,320 Speaker 1: like when you work with a cloud. 602 00:32:12,480 --> 00:32:15,440 Speaker 3: It really was you know, IBM leading on a digital 603 00:32:15,480 --> 00:32:18,800 Speaker 3: transformation for this client that wanted to move their applications 604 00:32:18,800 --> 00:32:20,800 Speaker 3: into the cloud. And so you're asking a lot of 605 00:32:20,840 --> 00:32:23,560 Speaker 3: questions about how does AI increase the risk and the 606 00:32:23,560 --> 00:32:25,960 Speaker 3: surface area. Those same questions ten years ago were asked 607 00:32:25,960 --> 00:32:28,320 Speaker 3: about the cloud, and we're still on the journey where 608 00:32:28,440 --> 00:32:31,479 Speaker 3: where companies are migrating into the cloud. We're not anywhere 609 00:32:31,520 --> 00:32:33,720 Speaker 3: near finished that yet. And so there's two pieces to 610 00:32:33,720 --> 00:32:35,920 Speaker 3: a cloud migration. One is just refactoring for the cloud 611 00:32:35,920 --> 00:32:38,240 Speaker 3: to make sure the application works effectively in the cloud. 612 00:32:38,560 --> 00:32:40,480 Speaker 3: And the second is security. And then you built in 613 00:32:40,520 --> 00:32:44,080 Speaker 3: security by design using poll does prison of cloud products 614 00:32:44,120 --> 00:32:46,040 Speaker 3: to make sure that not only did you have the 615 00:32:46,160 --> 00:32:48,720 Speaker 3: visibility so our cloud product you can scan and see 616 00:32:48,720 --> 00:32:51,640 Speaker 3: where the vulnerabilities are. And then there's also you know 617 00:32:51,720 --> 00:32:56,120 Speaker 3: cloud firewalls essentially that will keep bad actors out and 618 00:32:56,320 --> 00:32:58,080 Speaker 3: keep the cloud instance secure. 619 00:32:59,120 --> 00:33:01,680 Speaker 1: If we sit down, I don't have this conversation five 620 00:33:01,760 --> 00:33:04,560 Speaker 1: years from now, which I actually hope we do. Be fun. 621 00:33:07,000 --> 00:33:10,320 Speaker 1: This pretend is twenty twenty nine. Tell me what are 622 00:33:10,320 --> 00:33:11,880 Speaker 1: you happy about in twenty twenty nine. 623 00:33:12,160 --> 00:33:17,760 Speaker 2: I think twenty twenty nine, quantum computing is mainstream. I 624 00:33:17,800 --> 00:33:23,560 Speaker 2: think quantum computing is now quantum safe, where we're using 625 00:33:24,360 --> 00:33:29,160 Speaker 2: quantum computing to make sure that those bad actors aren't 626 00:33:29,200 --> 00:33:31,480 Speaker 2: as bad as they used to be back in twenty 627 00:33:31,520 --> 00:33:35,880 Speaker 2: twenty four, and we're seeing around the corners and that 628 00:33:35,920 --> 00:33:41,200 Speaker 2: we're empowering our palo alto relationship. That in twenty twenty 629 00:33:41,280 --> 00:33:45,880 Speaker 2: nine is the premiere type of capability that people are 630 00:33:45,880 --> 00:33:48,880 Speaker 2: looking at when they think of what used to be 631 00:33:48,960 --> 00:33:51,760 Speaker 2: AI and now is quantum capability. 632 00:33:51,960 --> 00:33:52,240 Speaker 1: Yeah. 633 00:33:53,000 --> 00:33:56,920 Speaker 3: Yeah, I think for AI, everyone's just using it as 634 00:33:56,960 --> 00:34:00,000 Speaker 3: part of their job. The way email was an innovation 635 00:34:00,080 --> 00:34:02,400 Speaker 3: in the nineties, the way you know cloud was an 636 00:34:02,440 --> 00:34:05,280 Speaker 3: innovation in the twenty tens, and we thought, how are 637 00:34:05,320 --> 00:34:06,880 Speaker 3: we going to use this? What impact is it going 638 00:34:06,960 --> 00:34:09,520 Speaker 3: to have on productivity? All these people who are spending 639 00:34:09,520 --> 00:34:11,600 Speaker 3: their days typing up memos, like what are they going 640 00:34:11,640 --> 00:34:14,160 Speaker 3: to do? We're going to be past that fear and 641 00:34:14,200 --> 00:34:17,080 Speaker 3: we're all going to understand that it is this like 642 00:34:17,280 --> 00:34:20,960 Speaker 3: truly positive force multiplier. For you know, every employee is 643 00:34:21,000 --> 00:34:25,000 Speaker 3: able to do their best work and spend their time 644 00:34:25,040 --> 00:34:26,920 Speaker 3: on the things that only they can do, and then 645 00:34:26,960 --> 00:34:29,160 Speaker 3: the AI is doing the rest of that for them. 646 00:34:29,000 --> 00:34:33,640 Speaker 2: Right, AI, it's fun to enable many things to work together, 647 00:34:33,760 --> 00:34:37,040 Speaker 2: and it won't be just one language model. We won't 648 00:34:37,080 --> 00:34:40,480 Speaker 2: even think about. It will be the difference between you know, 649 00:34:41,400 --> 00:34:45,840 Speaker 2: Malcolm having a fax machine, stereo and a telephone and 650 00:34:46,280 --> 00:34:49,120 Speaker 2: a memo board. Now it's in your pocket and it's 651 00:34:49,160 --> 00:34:51,360 Speaker 2: all one thing and you don't even call that, you know. 652 00:34:51,400 --> 00:34:53,120 Speaker 2: I said, walk them into my kids the other day 653 00:34:53,160 --> 00:34:56,799 Speaker 2: and they're like, what's a walk man? So I do 654 00:34:56,840 --> 00:34:58,799 Speaker 2: think it will. It'll be part of the past, and 655 00:34:58,800 --> 00:35:02,440 Speaker 2: it will be the stock of the seamless connection. That is, 656 00:35:02,840 --> 00:35:09,160 Speaker 2: secure seamless connection, of HR, of finance, of distribution, logistics, 657 00:35:09,160 --> 00:35:12,799 Speaker 2: of billing, all of those will have a capability to 658 00:35:12,840 --> 00:35:13,840 Speaker 2: work together. 659 00:35:14,200 --> 00:35:14,399 Speaker 3: Yeah. 660 00:35:15,560 --> 00:35:18,200 Speaker 1: I have to do some social quick fire questions you 661 00:35:18,200 --> 00:35:21,920 Speaker 1: guys ready, all right? What's the number one thing that 662 00:35:21,960 --> 00:35:23,479 Speaker 1: people misunderstand about AI? 663 00:35:24,920 --> 00:35:28,120 Speaker 2: The reliance on data? What do you mean by that? 664 00:35:28,960 --> 00:35:31,480 Speaker 2: I think that it's just assumed that it's happening and 665 00:35:31,680 --> 00:35:34,160 Speaker 2: it can just go out and grab data anywhere. 666 00:35:35,160 --> 00:35:36,799 Speaker 1: Yeah, you have. 667 00:35:36,760 --> 00:35:39,919 Speaker 2: To have good data, reliable data, and access to the data. 668 00:35:40,680 --> 00:35:42,480 Speaker 3: I think people are too afraid of it. 669 00:35:42,880 --> 00:35:45,960 Speaker 1: Checkbox and image generators are the biggest things in consumer 670 00:35:46,000 --> 00:35:47,920 Speaker 1: AI right now? What do you think is that next 671 00:35:47,920 --> 00:35:50,160 Speaker 1: big business application, Jason. 672 00:35:50,239 --> 00:35:53,480 Speaker 2: I think it's the tying together of multiple capabilities. I'm 673 00:35:53,560 --> 00:35:56,760 Speaker 2: hinting towards this earlier is. I think tying together the 674 00:35:56,960 --> 00:35:59,600 Speaker 2: disparate systems that sit in different parts of the organization, 675 00:35:59,640 --> 00:36:02,000 Speaker 2: front all this back office, making it one office and 676 00:36:02,080 --> 00:36:04,040 Speaker 2: tying together those different functions. That's it. 677 00:36:05,120 --> 00:36:07,560 Speaker 3: I mean, it's workflow automation. I think back to your 678 00:36:07,560 --> 00:36:10,759 Speaker 3: point on the reliance on data seems easy. It's a 679 00:36:10,760 --> 00:36:12,239 Speaker 3: lot harder than you think because you have to have 680 00:36:12,239 --> 00:36:14,240 Speaker 3: everything set up and exactly the right way to get 681 00:36:14,440 --> 00:36:17,120 Speaker 3: all of your systems automated and the sort of the 682 00:36:17,160 --> 00:36:19,319 Speaker 3: more boring jobs taken care of so that humans could 683 00:36:19,320 --> 00:36:20,360 Speaker 3: do the strategic ones. 684 00:36:22,040 --> 00:36:24,520 Speaker 1: How are you already using AI in your day to 685 00:36:24,600 --> 00:36:25,080 Speaker 1: day life? 686 00:36:27,280 --> 00:36:30,200 Speaker 3: I mean I use it at work all the time, 687 00:36:30,840 --> 00:36:32,839 Speaker 3: and then I've found right now I go to chat 688 00:36:32,880 --> 00:36:36,000 Speaker 3: GPT instead of Google to look things up. I like 689 00:36:36,040 --> 00:36:36,880 Speaker 3: having a conversation. 690 00:36:38,320 --> 00:36:42,400 Speaker 2: We have a wonderful capability in our consulting business called 691 00:36:43,000 --> 00:36:47,200 Speaker 2: our consulting assistant and consulting advantage is the proper name 692 00:36:47,200 --> 00:36:49,240 Speaker 2: for it. But I look at it as that assistant 693 00:36:49,360 --> 00:36:51,840 Speaker 2: and it's a force multiplier for me. So if I 694 00:36:52,000 --> 00:36:56,840 Speaker 2: need to pull together content proposals with the teams, we 695 00:36:56,920 --> 00:36:57,600 Speaker 2: go straight to that. 696 00:36:58,440 --> 00:37:00,880 Speaker 1: We are one more we Here's so many definitions of 697 00:37:01,000 --> 00:37:04,279 Speaker 1: open related to technology. How do you define it and 698 00:37:04,320 --> 00:37:06,440 Speaker 1: how does the concept help you innovate? 699 00:37:07,600 --> 00:37:11,040 Speaker 3: By definition? In cybersecurity, you don't want to be too open, right, 700 00:37:11,520 --> 00:37:15,480 Speaker 3: So I think we enable openness with this concept of 701 00:37:15,560 --> 00:37:17,839 Speaker 3: zero trust and saying like everyone's invited in as long 702 00:37:17,880 --> 00:37:20,600 Speaker 3: as you have the right credentials, right. So that's that's 703 00:37:20,640 --> 00:37:22,479 Speaker 3: one way, and then the other way is just making 704 00:37:22,520 --> 00:37:25,960 Speaker 3: sure you're connected to all the different systems in order 705 00:37:26,040 --> 00:37:28,320 Speaker 3: to be able to have that visibility and see what's happening, 706 00:37:28,360 --> 00:37:32,160 Speaker 3: because if you are blind, that's the minute you have 707 00:37:32,200 --> 00:37:33,240 Speaker 3: that vulnerability. 708 00:37:33,880 --> 00:37:39,600 Speaker 2: And I'd say it's moving quickly with security, it sounds 709 00:37:39,600 --> 00:37:42,879 Speaker 2: contradictory open. Oh, then it means you're not safe. No, 710 00:37:43,000 --> 00:37:44,440 Speaker 2: you are safe and you can move faster. 711 00:37:44,719 --> 00:37:46,399 Speaker 1: Yeah, thank you so much. It's fun. 712 00:37:46,560 --> 00:37:48,160 Speaker 3: Thanks a lot, Thank you great. We'll see you in 713 00:37:48,200 --> 00:37:48,680 Speaker 3: five years. 714 00:37:49,600 --> 00:37:55,680 Speaker 1: Five years, man, I'll be old in five years. Thank 715 00:37:55,719 --> 00:37:59,160 Speaker 1: you to Jason Kelly at IBM and Christy Frederick's at 716 00:37:59,239 --> 00:38:03,920 Speaker 1: POLO Alto Networks for that fascinating conversation about the threats 717 00:38:03,920 --> 00:38:09,760 Speaker 1: and opportunities in cybersecurity today. As Jason and Christie stressed, 718 00:38:10,120 --> 00:38:14,600 Speaker 1: AI can be a force multiplier for enterprise across industries. 719 00:38:15,600 --> 00:38:18,040 Speaker 1: When you're working with multiple products and have your data 720 00:38:18,080 --> 00:38:22,400 Speaker 1: in distributed environments, you need technology that will work across 721 00:38:22,440 --> 00:38:27,040 Speaker 1: your organization, and with Palo Alto Networks platform, you can 722 00:38:27,160 --> 00:38:33,080 Speaker 1: enhance cyber resiliency and simplify your operations. Through their collaboration, 723 00:38:33,440 --> 00:38:37,000 Speaker 1: IBM and Palo Alto Networks are charting the future a 724 00:38:37,120 --> 00:38:44,360 Speaker 1: fully integrated open end to end security solutions. Smart Talks 725 00:38:44,360 --> 00:38:48,080 Speaker 1: with IBM is produced by Matt Romano, Joey Fishground, Amy 726 00:38:48,120 --> 00:38:53,240 Speaker 1: Gaines McQuaid, and Jacob Goldstein. We're edited by Lydia Jane Kott. 727 00:38:53,440 --> 00:38:57,279 Speaker 1: Our engineers are Sarah Bugaier and Ben Tolliday. Theme song 728 00:38:57,320 --> 00:39:01,080 Speaker 1: by Gramoscope Special thanks to the eight Bar and IBM teams, 729 00:39:01,480 --> 00:39:04,799 Speaker 1: as well as the Pushkin Marketing team. Smart Talks with 730 00:39:04,840 --> 00:39:08,080 Speaker 1: IBM is a production of Pushkin Industries and Ruby Studio 731 00:39:08,200 --> 00:39:12,239 Speaker 1: at iHeartMedia. To find more Pushkin podcasts, listen on the 732 00:39:12,280 --> 00:39:16,719 Speaker 1: iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts. 733 00:39:17,560 --> 00:39:22,560 Speaker 1: I'm Malcolm Glaphom. This is a paid advertisement from IBM. 734 00:39:22,800 --> 00:39:29,080 Speaker 1: The conversations on this podcast don't necessarily represent IBM's positions, strategies, 735 00:39:29,760 --> 00:39:40,760 Speaker 1: or opinions.