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Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. This season

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Speaker 1: on smart Talks with IBM, Malcolm Gladwell and team are

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Speaker 1: diving into the transformative world of artificial intelligence with a

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Speaker 1: fresh perspective on the concept of open What does open

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Speaker 1: really mean in the context of AI. It can mean

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Speaker 1: open source code or open data, but it also encompasses

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Speaker 1: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,

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Speaker 1: and enabling new levels of transparency. Join hosts from your

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Speaker 1: favorite Pushkin podcasts as they explore how openness and AI

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Speaker 1: is reshaping industries, driving innovation, and redefining what's possible. You'll

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Speaker 1: hear from industry experts and leaders about the implications and

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Speaker 1: possibilities of open AI, and of course, Malcolm Gladwell will

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Speaker 1: be there to guide you through the season with his

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Speaker 1: unique insights. Look out for new episodes of Smart Talks

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Speaker 1: every other week on the iHeartRadio app, Apple Podcasts, or

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Speaker 1: wherever you get your podcasts, and learn more at IBM

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Speaker 1: dot com slash smart Talks.

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Speaker 2: Hello, Hello, Welcome to Smart Talks with IBM, a podcast

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Speaker 2: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Glappwell. This season,

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Speaker 2: we're diving back into the world of artificial intelligence, but

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Speaker 2: with a focus on the powerful concept of open its possibilities, implications,

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Speaker 2: and misconceptions. We'll look at openness from a variety of

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Speaker 2: angles and explore how the concept is already reshaping industries,

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Speaker 2: ways of doing business and our very notion of what's possible.

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Speaker 2: On today's episode, I'm joined by Jason Kelly, the global

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Speaker 2: Managing Partner for IBM Strategic Partners and Ecosystems, and by

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Speaker 2: Christy Fredericks, the Senior Vice president and Chief Partnership Officer

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Speaker 2: at Polowa Networks. We discussed how their partnership in the

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Speaker 2: cybersecurity space helps strengthen enterprises by focusing on seamless cybersecurity

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Speaker 2: solutions tailored to meet the evolving threat landscape. By leveraging

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Speaker 2: AI and automation, this collaboration aims to modernize security programs,

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Speaker 2: improve response times, and produce risks. Jason and Christy both

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Speaker 2: bring a tremendous amount of experience and expertise to the subject.

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Speaker 2: I think you're really going to enjoy this one, Jason, Christy,

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Speaker 2: Welcome to Smart Talks with IBM. Thank you for joining me.

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Speaker 3: Thank you.

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Speaker 4: It's great to be here.

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Speaker 2: We are here to discuss cybersecurity and the partnership between

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Speaker 2: IBM and Palo Alto Networks. But before we get there,

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Speaker 2: I wanted you guys to tell me a little bit

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Speaker 2: out yourself. Jason, let's start with you. I see on

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Speaker 2: your resume west Point, which makes we think there's some

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Speaker 2: interesting things going on there. How did you get to

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Speaker 2: west Point?

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Speaker 3: West Point? West Point was the decision. First, it was

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Speaker 3: it was affordable back in the day. But I had

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Speaker 3: a sense of service. My father was a World War

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Speaker 3: Two vet, so I grew up on the weekends watching

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Speaker 3: World War two video. You know, he's army as well. Yeah,

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Speaker 3: and so I thought, oh, that'd be exciting, and I

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Speaker 3: thought I do some type of service. Went there and

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Speaker 3: now I have the biggest family, extended family I could

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Speaker 3: ever have. So it was very exciting. Played football lucked out,

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Speaker 3: meaning I wasn't recruited. I walked on and that kept

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Speaker 3: me there because it gave me something and outlet with

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Speaker 3: all the other pressures. Defensive back I was. I was

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Speaker 3: great at knocking the ball down, not the best at

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Speaker 3: catching it.

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Speaker 2: Yeah, and then you were a ranger I was.

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Speaker 3: I was privileged to be a US Army airborne ranger station,

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Speaker 3: but did most of my time in northern Italy. We're

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Speaker 3: part of the eighty second Airbarnship Post. Oh yeah, that's

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Speaker 3: what people say, seriously, like, you know, you were you

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Speaker 3: were northern, you were drinking wine and having bred, you know.

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Speaker 3: But it was a part of a NATO force there

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Speaker 3: at the time. Yeah, so exciting.

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Speaker 2: How did you get from there to IBM?

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Speaker 3: A long path. As I came out of the military,

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Speaker 3: I started manufacturing retail housing and did a quick stint,

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Speaker 3: took a leave of absence from industry, and did a

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Speaker 3: stint of yet again public service in the state of

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Speaker 3: Tennessee with economic development and got a whiff of how

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Speaker 3: fun it could be to do things around data and media.

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Speaker 3: Started a small media firm what we would now call

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Speaker 3: a digital firm, sold it and said I wanted to

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Speaker 3: go do it again somewhere, but I want to go

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Speaker 3: to a big company. And the family at IBM brought

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Speaker 3: me in and yet to let me go.

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Speaker 2: That was how many years ago?

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Speaker 3: Two decades? Oh wow, So I know I look amazingly young, but.

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Speaker 2: Yes, because you must have.

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Speaker 3: And IBM was my fifth career and and and I've

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Speaker 3: I've enjoyed it since and that's what I what I

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Speaker 3: do there, build teams, grow new parts of the company,

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Speaker 3: and get to work with some of the most brilliant

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Speaker 3: people on the face of the planet, as well as

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Speaker 3: partners like like Christy that just keep it exciting.

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Speaker 2: Christy, you're I was delighted to learn that you are Canadian?

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Speaker 2: Yes here, yeah? But you so you were a consultant

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Speaker 2: for a long time a Bane Yes.

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Speaker 4: Yeah. I joined Baine Consulting intending to spend a couple

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Speaker 4: of years there learn the ropes and then go get

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Speaker 4: my first real job. But the value personally to my

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Speaker 4: growth and development, and then that we were able to

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Speaker 4: bring our clients. I ended up there for sixteen years

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Speaker 4: and then post Bane went onto another my first product company,

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Speaker 4: a new relic, and then it's come full circle at

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Speaker 4: Polota Networks. But at Bain it was all about bringing

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Speaker 4: expertise across different industries to help our clients improve whatever

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Speaker 4: they needed to improve and bringing that expertise to bear.

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Speaker 4: And then you have the product lens and you think, Okay,

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Speaker 4: we're going to build the absolute best product to help

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Speaker 4: our customers do what they need to get done, and

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Speaker 4: then I joined pal Alto about six seven months ago

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Speaker 4: in a partnerships role and I'm delighted to be able

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Speaker 4: to work with amazing consulting companies like IBM where we

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Speaker 4: bring both to bear.

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Speaker 2: How long have IBM and Palo Alto Network's been partners?

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Speaker 3: So we've been We've been working together for quite quite

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Speaker 3: a long time, but we made it official, meaning we

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Speaker 3: got married as strategic partners last year.

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Speaker 2: Oh I see. So what is it that each of

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Speaker 2: you bring to the table? What's each side special?

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Speaker 3: So it's great that you asked that because about a

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Speaker 3: decade ago, are now CEO Arvin Christ says, you know,

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Speaker 3: it wouldn't be great if we has had this one

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Speaker 3: focus with what does IBM do and you have this

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Speaker 3: whole list, and he says, let's make it simple. We

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Speaker 3: are a multi cloud, hybrid cloud AI company. And so

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Speaker 3: when you say that, it sounds very simple. But then people,

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Speaker 3: what the hell is that? What your hybrid cloud? Well,

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Speaker 3: both of those two things have a lot of data involved,

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Speaker 3: and a lot of those mean that that data is

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Speaker 3: going to sit in multiple places and distributed environments. Well,

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Speaker 3: if you're able to tie those things together with multiple partners.

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Speaker 3: You also have to make sure that it's secure because

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Speaker 3: in the direction that we're going, where data is now

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Speaker 3: being consumed in many different places and it is the

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Speaker 3: fuel behind AI as we know. Then you say, ah, well,

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Speaker 3: who does that well and who does it in a

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Speaker 3: way that's getting rid of seams, the seams that could

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Speaker 3: be across multiple products, multiple product SATs even And that's

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Speaker 3: where Powell comes in.

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Speaker 4: I think the canal wisdom in cybersecurity was always you

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Speaker 4: need all the new tools, right, you need it every threat.

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Speaker 4: It's like, whack them all. Every threat that pops up,

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Speaker 4: you get the tool that's purpose built for that specific thing. Well,

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Speaker 4: fast forward to you know, the RSA conference this year.

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Speaker 4: There were four thousand vendors on the floor. You look

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Speaker 4: at an average company, there's hundreds of cybersecurity tools. It

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Speaker 4: introduces a level of complexity that is really hard to manage.

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Speaker 4: You as a user, query and application right, that query

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Speaker 4: can go through a bunch of different pings from one

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Speaker 4: cloud to the next. It goes into and out of

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Speaker 4: as SaaS application. It may be running along a network,

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Speaker 4: you may be accessing it from your phone, which is

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Speaker 4: an unmanaged device. It's got to go in and out.

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Speaker 4: And if you say, okay, I've got to secure that phone,

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Speaker 4: I've got to secure the network, I've got it. Then

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Speaker 4: all of a sudden you've got sort of firewalls, software

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Speaker 4: and hardwel flows popping up everywhere. You've got cloud security,

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Speaker 4: and it's you've probably heard of this concept of zero trust,

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Speaker 4: which is every time you have to check and say

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Speaker 4: are you allowed in here? Are you allowed in here?

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Speaker 4: The number of places that can fall down it just

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Speaker 4: becomes overwhelming. So you end up with either alerts firing,

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Speaker 4: you know, every two seconds that you have to then

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Speaker 4: go investigate in most of which are false positives or

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Speaker 4: you miss something right. And so that was the conventionalism

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Speaker 4: was we've got to buy all these tools, and now

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Speaker 4: you've got overwhelmed CIOs and CSOs with hundreds of tools,

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Speaker 4: and Palo Alto strategy has been, look, we're going to

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Speaker 4: create a platform where everything can be stitched together, everything

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Speaker 4: can speak the same language, and we can sort of

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Speaker 4: manage throughout the architecture and watch, you know, this call

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Speaker 4: as as it's passing through all these different checkpoints, and

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Speaker 4: we can do it in a way that you still

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Speaker 4: have the confidence that it's best to breed right, so

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Speaker 4: you're not making any trade offs. But it's not so

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Speaker 4: simple just to get from the spaghetti to the seamless architecture.

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Speaker 4: You need, oftentimes to re engineer your business processes. You

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Speaker 4: have to re architect your digital environment. And so that's

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Speaker 4: where we partner with a company like IBM to bring

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Speaker 4: that expertise and say, we're going to help you not

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Speaker 4: just deploy the best cybersecurity architecture, but really get your

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Speaker 4: environment ready to have this zero customers as well as.

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Speaker 3: All of those players that cross that spaghetti, because when

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Speaker 3: you start thinking about all the other partners that you

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Speaker 3: work with, if you're you think of an industry perspective,

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Speaker 3: you're going to have an ERP. It could be an Oracle,

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Speaker 3: it could be an SAP. You're not going to have

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Speaker 3: one cloud, as I mentioned, it's going to be possibly

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Speaker 3: multiple clouds. You'll have some AWS maybe Microsoft asure and

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Speaker 3: then even even some Google in there, and then your

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Speaker 3: own that you've built in your private over there, uh,

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Speaker 3: some an IBM cloud. You'll have those multiple clouds, and

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Speaker 3: then you also will have you know, fit for purpose, Oh,

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Speaker 3: I need a I need a salesforce in there for

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Speaker 3: my customer focusing I need. I'm doing some graphics, so

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Speaker 3: I have Adobe, so I just as I can name

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Speaker 3: name name, all of those then have to be re engineered. Seriously.

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Speaker 3: I mean, come on, Malcolm, you're gonna sit there you

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Speaker 3: think how long that would take. So if you haven't

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Speaker 3: done that before, you're going to have to go to

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Speaker 3: each one of those individually, or you can work with

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Speaker 3: a company that can tie those things together, because we

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Speaker 3: are also strategic partners with them. So that's where you

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Speaker 3: start to say, Okay, I see how this comes together.

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Speaker 3: You have to make sure that your ecosystem is going

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Speaker 3: to be stronger than your competitor's ecosystem, and you have

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Speaker 3: to be secure in what you're doing because as you

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Speaker 3: add more players or products, you create seams, and you

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Speaker 3: want to make sure there's fewer seams and that there's

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Speaker 3: zero trust across that capability you're building. And that's why

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Speaker 3: the compliment between the two companies.

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Speaker 2: Well, take a step back from a moment before we

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Speaker 2: sort of launch, once get into the specifics of what

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Speaker 2: you guys are doing. I'm curious at this moment in

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Speaker 2: twenty twenty four, how nervous should we be about cybersecurity?

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Speaker 2: So compared it to five years ago or ten years ago.

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Speaker 2: Are we are less nervous than you were five years ago?

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Speaker 2: Were more nervous? Or all of changes going on right

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Speaker 2: now increasing vulnerability or decreasing it?

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Speaker 3: I would say Christie, also, I think we share the

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Speaker 3: point of view is that it's not necessarily being more nervous.

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Speaker 3: I think you should be more prepared because the amounts

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Speaker 3: of threat is increasing based on our dependence upon data.

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Speaker 3: And that's that's where I think the attention should be placed.

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Speaker 3: Is that more and more, especially with the importance of AI,

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Speaker 3: that you say, okay, then what's under all that? And

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Speaker 3: it's the data, as I said, So knowing that you

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Speaker 3: should be more concerned.

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Speaker 2: Does the advent of AI and its rapid evolution help

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Speaker 2: defense more or offense more?

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Speaker 4: I think it's I think it's like any mega trend

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Speaker 4: that we've witnessed both. Right, So you think about AI.

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Speaker 4: It's great, right in terms of what it's going to

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Speaker 4: unlock for productivity, for humanity, but it also makes it

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Speaker 4: a whole lot easier to build ransomware. It's a whole

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Speaker 4: lot easier to test different ways into a system. Right.

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Speaker 4: But I think that's true if you think about like

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Speaker 4: the rise of the Internet, right, all of a sudden,

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Speaker 4: everyone was putting their data online and you had to

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Speaker 4: think of new ways to stay ahead and keep that secure.

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Speaker 4: And I don't think AI is any different. You've got

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Speaker 4: companies like Palta, partnerships like Powell and IBM that are

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Speaker 4: constantly scanning the landscape for not only the current threats,

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Speaker 4: but what's next, what's coming around the corner, what's after AI?

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Speaker 4: And so I think taking it seriously and being prepared

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Speaker 4: is probably the right way of looking at it, as

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Speaker 4: opposed to because if you think about it too hard,

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Speaker 4: you'll just want to crawl into a corner and stuff

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Speaker 4: everything under the mattress.

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Speaker 2: I am the CEO of a regional hospital chain, big

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Speaker 2: distribute healthcare system, so a ton of data. The consequences

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Speaker 2: of being hacked and help for ransom are life and death.

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Speaker 2: Life and death. Right littally, when you come so, you

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Speaker 2: come down, you sit down with me, and you chat

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Speaker 2: with me. Walk me through the kinds of things you

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Speaker 2: would tell me about what I need to get safer.

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Speaker 2: For example, let's start with one. Is it likely that

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Speaker 2: I'm spending too little? Or am I spending money in

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Speaker 2: the wrong place.

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Speaker 4: Great question. It depends how you've broken it out. If

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Speaker 4: you are distributing all of your dollars across a whole

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Speaker 4: bunch of different tools, it's likely you're just spending the

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Speaker 4: wrong money. And in fact, you know, putting it all

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Speaker 4: in one place is a way of potentially saving money

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Speaker 4: but keeping your security actually higher. And I'd love to

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Speaker 4: hear Jason, how you would approach it. How we would

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Speaker 4: approach it, of course, is by saying, you know, what,

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Speaker 4: what does your environment look like? You know, do you

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Speaker 4: have the connected medical devices into your EMR? Are your

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Speaker 4: respirators and ventilators all online? Right? And so we would

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Speaker 4: talk about, okay, here's how you get coverage and how

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Speaker 4: the coverage of both the firewalls as well as the

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Speaker 4: detectors all feedback into your security operation center and you

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Speaker 4: can manage it and do your learning with AI and

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Speaker 4: keep yourself securing.

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Speaker 3: So yeah, and I would say Christy and I would

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Speaker 3: go to the same point because if you get under

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Speaker 3: what she was just asking, it is your data on

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Speaker 3: prem and when it's on prem, how active is it

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Speaker 3: across the enterprise? And so that begins the basis for

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Speaker 3: the start and then often you're going to say, well,

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Speaker 3: we actually take in data from outside, and then we

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Speaker 3: also have the circumstances. There's a lot of PII and

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Speaker 3: so that personal is the personal information, right, And so

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Speaker 3: now you're saying, okay, now, how are we securing that

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Speaker 3: and where are we securing it? And so you have

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Speaker 3: to start really thinking about the different areas within that

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Speaker 3: hospital chain. Are you sharing that amongst your hospitals? And

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Speaker 3: now you start to think of if I'm saying no

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Speaker 3: to a lot of that, it's like, well, then are

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Speaker 3: you as efficient as you want to be? So there

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Speaker 3: is that trade off of you know, am I so

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Speaker 3: tightly walled that I'm not productive? And so that's where

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Speaker 3: we would start to say, what's the outcome that you're

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Speaker 3: trying to get to? All Right, maybe you're good, Maybe

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Speaker 3: you're you're good with your five locations and you don't

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Speaker 3: need to go any further, but maybe you want to

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Speaker 3: expand to fifty and by the way, you're going to

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Speaker 3: go crossport or you're going to be in Toronto and

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Speaker 3: in New York. Okay, well then how do you do that?

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Speaker 3: And so I think that it's very easy to start

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Speaker 3: jumping into any of the typical situations. But the first

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Speaker 3: question that you have to ask you as the hospital CEOs,

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Speaker 3: what's your objective? What are you what are you trying

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Speaker 3: to do? Because too often what we see is that

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Speaker 3: there's some bright, new, shiny thing that everybody wants to

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Speaker 3: put in play. You know, it's a sandwich looking for

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Speaker 3: lunch and you go, but what is it that you

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Speaker 3: want to do as this? Are you doing research? Are

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Speaker 3: your research hospital? Are you more consumer oriented? So those

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Speaker 3: are the questions you start to ask because they start

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Speaker 3: to then tell a story in line with what Christy questions.

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Speaker 3: And I think that that's where the again, the complement

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Speaker 3: is that instead of just saying, oh, well, that's thanks

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Speaker 3: for telling me all this, Malcolm, here's your ten page strategy,

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Speaker 3: go find somebody. We have the benefit in IBM. And

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Speaker 3: it's probably why I'm still there is you know, we're

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Speaker 3: very unique. We're the only company on the planet that

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Speaker 3: has a consulting business at scale inside of a technology company,

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Speaker 3: and so we have, you know, the left brain, right brain.

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Speaker 3: We're able to do that and then we're able to say, okay,

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Speaker 3: now which partners are going to be most valuable for

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Speaker 3: our clients? What's going to work for you, isn't going

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Speaker 3: to work for the manufacturer down the road, isn't going

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Speaker 3: to work for the consumer or CpG company across the river.

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Speaker 3: Those things are bare very specific. The threats and the

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Speaker 3: scenes that I was talking about are very specific. So

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Speaker 3: that's where it becomes very valuable to make sure that

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Speaker 3: I'm not just giving you some strategy that's generic.

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Speaker 2: But everything as a healthcare CEO, everything I have done,

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Speaker 2: almost everything I've done over the last ten years, has

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Speaker 2: it had the effect of increasing my vulnerability. I want

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Speaker 2: to digitize data within the hospital used to be on

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Speaker 2: pieces of paper. I want doctors to go home and

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Speaker 2: to be able to seamlessly hook into stuff at work

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Speaker 2: because they got to do all their paperwork. I want

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Speaker 2: to make sure the diabetes people are speaking to the

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Speaker 2: organ transplant people. And so isn't that everything I have

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Speaker 2: done to kind of keep up with the revolution in healthcare?

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Speaker 2: Isn't that also making me more and more vulnerable to

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Speaker 2: a bad actor.

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Speaker 4: It's such a great question because think about the quality

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Speaker 4: of healthcare delivery. Right, So now doctors aren't filling out forms,

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Speaker 4: They're spending time with patients, and so the quality of

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Speaker 4: care is improving and the vulnerability is improving, and so

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Speaker 4: I think that's where having a strong cybersecurity strategy actually

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Speaker 4: enables all of that. One of our products is our

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Speaker 4: sas product, and we tested it with some business applications,

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Speaker 4: and oftentimes the wrap is, oh, security is going to

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Speaker 4: slow you down, right, like you have to add a firewall,

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Speaker 4: you have to checkpoints. Our product actually increases the velocity

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Speaker 4: of your ability to use that application because of the

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Speaker 4: way that it is queried through our system as opposed

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Speaker 4: to just through the regular network. So it doesn't slow

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Speaker 4: it down and in fact, it makes it run more efficiently.

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Speaker 4: That's just one minor example. But back to the healthcare question. I,

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Speaker 4: as a patient want my doctors accessing all the technology

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Speaker 4: and talking to each other and connecting the dots behind

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Speaker 4: the scenes. I also want my data to stay private,

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Speaker 4: and so having both a consulting partner who understands how

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Speaker 4: to ask questions of the environment and of the applications

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Speaker 4: you're using, and who understands the industry inside and out,

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Speaker 4: and a technology partner that builds and stays ahead of

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Speaker 4: all of the different threats come together advise you. I

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Speaker 4: think is super important. When you bring in a partner

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Speaker 4: like IBM, with a platform like pal Alta that covers

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Speaker 4: you know, all the different parts of your environment, you're

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Speaker 4: able to say, look, where where are the vulnerabilities in

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Speaker 4: the system, Where are the different endpoints that we need

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Speaker 4: to have covered, And then just make sure you get

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Speaker 4: that breadth of coverage and then you're better able to so, yes,

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Speaker 4: you've increased the risk, but then you've mitigated it.

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Speaker 2: So to give so before I retire my healthcare analogy,

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Speaker 2: because I was thinking about just trying to understand the

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Speaker 2: importance of this idea of having a single platform. So

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Speaker 2: if this mudtle healthcare network is typical, I've acquired a

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Speaker 2: whole series of over the last ten years. I bought

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Speaker 2: a hospital over here, some I got some physicians things

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Speaker 2: that I snapped up over here. I bought a diagnostics company,

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Speaker 2: and so I have all of these legacy systems and

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Speaker 2: I had, like you said, maybe I got some stuff

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Speaker 2: in the cloud with one company, some stuff with the cloud.

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Speaker 2: And what you're saying is the first step is to

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Speaker 2: kind of rationalize that put it on a single platform,

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Speaker 2: so you understand where your points of weakness are as

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Speaker 2: opposed to being blind to your points of weakness.

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Speaker 4: There's yes, although anyone who's done any kind of M

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Speaker 4: and A knows that that's a long journey, right. So

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Speaker 4: I think the first step is just understanding where everything is,

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Speaker 4: and then you get on a path and you say,

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Speaker 4: where's the biggest risk. Let's neutralize or mitigate that risk

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Speaker 4: one at a time. The thing about open end Secure,

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Speaker 4: you know, Palo Alto. We keep touting the benefits of

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Speaker 4: the platform. Everything on Palo Alto, your risk is going

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Speaker 4: to be mitigated and you're going to have the full visibility.

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Speaker 4: But you can't get there overnight. And so we've got

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Speaker 4: you know, thousands of integrations with other technology companies, including

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Speaker 4: our partners, to make sure that we can capture and

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Speaker 4: have visibility into those those endpoints in those systems as well.

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Speaker 4: And so I think step one is just figure out

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Speaker 4: where everything is, get the scan, so politize a couple

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Speaker 4: of products where you can kind of deploy and get

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Speaker 4: a view of your attack surface. I love the analogy.

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Speaker 4: Just like a digital environment is a house, right, and

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Speaker 4: so like you have your front door log of course,

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Speaker 4: because probably they're going to try the front door first,

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Speaker 4: but that's not all you're going to do, right, you're

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Speaker 4: going to make sure the whole you know, the windows

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Speaker 4: are locked and there's an alarm system and all of that.

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Speaker 4: And I think that's how you have to think about it,

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Speaker 4: is just how do we cover the whole service.

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Speaker 2: So everyone lay. People like me have been bombarded over

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Speaker 2: it seems like over the last year with one thing

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Speaker 2: another about how quickly AI is moving forward and how

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Speaker 2: big of a deal it is suddenly is going to

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Speaker 2: be in the economy. What is the impact of that

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Speaker 2: dramatic change in AI's capabilities on this cybersecurity question? So

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Speaker 2: what does it mean if you're defending somebody that you

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Speaker 2: now have these sophisticated AI tools, you suppose.

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Speaker 3: I think that AI becomes the force multiplier for cyber

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Speaker 3: To think about cyber Before it was just locking your doors,

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Speaker 3: locking the windows, and if you were really good, you

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Speaker 3: had an alarm system. You know. Now with AI, you

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Speaker 3: can said, well, I can predict what's going to happen.

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Speaker 3: I can see around the corner. I know, I can

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Speaker 3: leave my windows open upstairs and it's fine, and it's okay.

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Speaker 2: I mean because why because the AI is running a

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Speaker 2: million simulations.

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Speaker 3: It can And that's exactly it. It becomes the intelligent

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Speaker 3: part of that AI. It's not artificial, it's augmented. So

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Speaker 3: you now have this new capability to see around corners,

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Speaker 3: and so you're able to do the jobs of yesterday

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Speaker 3: more effectively. And the queries that you were doing, and

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Speaker 3: that's all you're really doing, now you're doing them, you know, faster,

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Speaker 3: you're able to access even more data and you're able

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Speaker 3: to then make it more secure. So that's why AI

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Speaker 3: becomes a force multiplier.

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Speaker 2: Yeah, and just talk about the faster part. What does

433
00:24:06,400 --> 00:24:09,960
Speaker 2: faster mean in practical terms? If you're trying to defend

434
00:24:09,960 --> 00:24:12,720
Speaker 2: an enterprise against a cyber attack, what does speed matter

435
00:24:12,760 --> 00:24:13,440
Speaker 2: in that environment?

436
00:24:14,440 --> 00:24:17,960
Speaker 3: You're always trying to find a place through I go

437
00:24:18,040 --> 00:24:20,200
Speaker 3: back to you. We brought up the army. You always

438
00:24:20,280 --> 00:24:22,000
Speaker 3: how do you break the line? How do you find

439
00:24:22,000 --> 00:24:24,679
Speaker 3: a penetration point? And when you think about you know,

440
00:24:25,119 --> 00:24:29,080
Speaker 3: pin testing, penetration testing, where are those? So if you're

441
00:24:29,119 --> 00:24:31,919
Speaker 3: able to do that faster than the bad guys, and

442
00:24:31,960 --> 00:24:35,560
Speaker 3: not only faster, but you're picking more probable points. This

443
00:24:35,720 --> 00:24:38,600
Speaker 3: is back to the intelligence. I could waste time doing

444
00:24:38,760 --> 00:24:41,840
Speaker 3: penetration testing someplace where That's why I mentioned leave if

445
00:24:41,840 --> 00:24:44,680
Speaker 3: they can't get in the second story windows. Why are

446
00:24:44,720 --> 00:24:48,000
Speaker 3: you spending time trying it so that becomes more effective?

447
00:24:48,800 --> 00:24:51,600
Speaker 3: So that's when I think of speed. That's what I

448
00:24:51,640 --> 00:24:54,080
Speaker 3: think of because with not just speed, I think it's

449
00:24:54,080 --> 00:24:55,720
Speaker 3: also what's effective.

450
00:24:55,560 --> 00:24:57,200
Speaker 4: Just to put a put a fine point on it.

451
00:24:57,320 --> 00:24:59,359
Speaker 4: So I found a way in Okay, Now what I

452
00:24:59,359 --> 00:25:01,000
Speaker 4: don't know where the jewelry is, so I have to

453
00:25:01,040 --> 00:25:03,960
Speaker 4: look around and see if there's any hidden gems and

454
00:25:03,960 --> 00:25:06,120
Speaker 4: try to find my way. That used to take a week,

455
00:25:06,160 --> 00:25:10,080
Speaker 4: two weeks, sort of seven to fourteen days. Now it's hours, right,

456
00:25:10,119 --> 00:25:12,560
Speaker 4: So they're in and they can actually expeltrate data within

457
00:25:12,880 --> 00:25:16,040
Speaker 4: less than a day. The metric we use in the

458
00:25:16,080 --> 00:25:18,960
Speaker 4: security operation Center is meantime to detect, so to see

459
00:25:18,960 --> 00:25:21,959
Speaker 4: anyone's there, meantime to respond and remediate to get them

460
00:25:21,960 --> 00:25:26,200
Speaker 4: out right. That used to be also you know, seven eight, nine,

461
00:25:26,240 --> 00:25:30,320
Speaker 4: ten days. Now it needs to be less than an hour.

462
00:25:31,080 --> 00:25:35,040
Speaker 4: And with our AI based security operations platform, it is.

463
00:25:35,480 --> 00:25:38,360
Speaker 4: Now you've got one tool that whether it's all peloton

464
00:25:38,440 --> 00:25:40,720
Speaker 4: networks or whether it's just you know, hoofringing data from

465
00:25:40,760 --> 00:25:42,920
Speaker 4: other places, then you're able to see it all together.

466
00:25:42,960 --> 00:25:44,960
Speaker 4: So you actually get fewer alerts, so you get from

467
00:25:45,400 --> 00:25:48,679
Speaker 4: thousands of alerts down to one hundred alerts right, and

468
00:25:48,720 --> 00:25:51,240
Speaker 4: you can investigate them and you investigate them using AI too.

469
00:25:51,560 --> 00:25:55,040
Speaker 4: And AI is today, it's today's threat, but it's you know,

470
00:25:55,080 --> 00:25:57,760
Speaker 4: you think about threat and opportunity to think about what's next.

471
00:25:57,760 --> 00:25:59,760
Speaker 4: You always have to be kind of evolving.

472
00:25:59,520 --> 00:26:01,760
Speaker 3: And you have to. I think we talk about threat

473
00:26:01,760 --> 00:26:03,639
Speaker 3: and risk. You know, we didn't tell you know, what

474
00:26:03,720 --> 00:26:08,360
Speaker 3: is the cost of cyber some type of penetration. It's

475
00:26:08,760 --> 00:26:11,800
Speaker 3: typical costs is about four and a half million dollars.

476
00:26:12,920 --> 00:26:17,760
Speaker 3: And that's just in labor and remediation. If you think

477
00:26:17,760 --> 00:26:22,240
Speaker 3: about reputational risk as well, our Institute for Business Value

478
00:26:22,320 --> 00:26:25,320
Speaker 3: to the study and found it in twenty twenty three

479
00:26:25,359 --> 00:26:28,199
Speaker 3: and they were thirty nine banks that we watched that

480
00:26:29,240 --> 00:26:33,240
Speaker 3: suffered a reputational risk market value of one hundred and

481
00:26:33,320 --> 00:26:36,440
Speaker 3: thirty billion dollars. And so you start to think, wow,

482
00:26:36,520 --> 00:26:41,520
Speaker 3: that's just reputational risk. So that's what's at stake here,

483
00:26:42,040 --> 00:26:44,280
Speaker 3: and it's only that is only going to get bigger.

484
00:26:45,080 --> 00:26:47,600
Speaker 4: So one of the piece we haven't talked about about

485
00:26:47,640 --> 00:26:50,040
Speaker 4: AI that I find super interesting because we've been talking

486
00:26:50,160 --> 00:26:53,920
Speaker 4: essentially about like the terminator, the robots fighting robots, right,

487
00:26:53,920 --> 00:26:56,600
Speaker 4: like whose robots are quicker? Like I'm designing a tax

488
00:26:56,640 --> 00:26:58,840
Speaker 4: and I'm defending against tax and I think that's that's

489
00:26:58,880 --> 00:27:03,080
Speaker 4: super important. But we recently launch and our working at

490
00:27:03,080 --> 00:27:05,919
Speaker 4: IBM on our AI security product to actually secure the

491
00:27:05,960 --> 00:27:08,080
Speaker 4: use of ALI because it also opens up another set

492
00:27:08,119 --> 00:27:12,080
Speaker 4: of threat factors. I'll give you an example. I'm a

493
00:27:12,080 --> 00:27:14,800
Speaker 4: marketing executive now for your hospital. So I work for you,

494
00:27:15,480 --> 00:27:19,199
Speaker 4: and you want to announce the launch of a new center,

495
00:27:19,680 --> 00:27:22,119
Speaker 4: and so I upload all the information about all the

496
00:27:22,200 --> 00:27:24,360
Speaker 4: patients and our you know how we do things into

497
00:27:24,400 --> 00:27:26,399
Speaker 4: chat GPT to write the PR for me. Well, I've

498
00:27:26,400 --> 00:27:29,520
Speaker 4: also just uploaded to chat GPT a whole bunch of secrets, right.

499
00:27:30,119 --> 00:27:33,520
Speaker 4: So it's it's how employees are using AI, because I think,

500
00:27:33,560 --> 00:27:35,760
Speaker 4: you know, some companies are sort of building their own

501
00:27:35,800 --> 00:27:37,960
Speaker 4: language models and their own AI applications that they want

502
00:27:37,960 --> 00:27:40,840
Speaker 4: to keep secure. Others are just curious about how their

503
00:27:40,840 --> 00:27:44,359
Speaker 4: employees are using AI applications on the shelf, and so

504
00:27:44,400 --> 00:27:46,719
Speaker 4: we announced in May a product where you can actually

505
00:27:47,480 --> 00:27:50,200
Speaker 4: scan and see how AI is being used in your

506
00:27:50,400 --> 00:27:53,480
Speaker 4: enterprise and within We made the announcement with the GA

507
00:27:53,480 --> 00:27:55,199
Speaker 4: was last month, but we made the announcement in May,

508
00:27:55,240 --> 00:27:58,320
Speaker 4: and we had immediately thousands of CIOs signing up because

509
00:27:58,400 --> 00:28:01,720
Speaker 4: just understanding you know who's using what it's another open

510
00:28:01,800 --> 00:28:05,399
Speaker 4: question because you know, we talk about AI enhancing productivity

511
00:28:05,400 --> 00:28:06,919
Speaker 4: and all the benefits it's going to bring, but it

512
00:28:06,960 --> 00:28:09,760
Speaker 4: brings it brings risks, not just in how it's being

513
00:28:09,840 --> 00:28:12,600
Speaker 4: used by the thread actors, but also you know what

514
00:28:12,640 --> 00:28:13,720
Speaker 4: other vulnerabilities that.

515
00:28:13,720 --> 00:28:17,720
Speaker 2: Exc It's the eye that you does that system tell

516
00:28:17,760 --> 00:28:19,240
Speaker 2: you what's a problematic use.

517
00:28:20,000 --> 00:28:22,440
Speaker 4: It does, so what what it does, and you've got

518
00:28:22,480 --> 00:28:24,200
Speaker 4: to train it right. But what it does is say

519
00:28:24,200 --> 00:28:27,240
Speaker 4: this is this is outside of your policy. So CIOs

520
00:28:27,240 --> 00:28:29,720
Speaker 4: will set policies on here's what is acceptable and not

521
00:28:29,840 --> 00:28:31,680
Speaker 4: acceptable use. So we'll be able to scan and say

522
00:28:31,720 --> 00:28:34,359
Speaker 4: these these falling uses are outside of policy, and then

523
00:28:34,440 --> 00:28:36,440
Speaker 4: it'll punt and say I think this is too restrictive,

524
00:28:36,480 --> 00:28:38,040
Speaker 4: I think this is too permissive, and then you can

525
00:28:38,080 --> 00:28:41,720
Speaker 4: sort of update your policies from there. That's just sort

526
00:28:41,760 --> 00:28:44,240
Speaker 4: of the visibility piece, and then there's the run time piece,

527
00:28:44,240 --> 00:28:46,160
Speaker 4: which will actually stop you from using it. So you

528
00:28:46,200 --> 00:28:48,880
Speaker 4: go and say, okay, here's all my patient's social security numbers.

529
00:28:48,880 --> 00:28:51,480
Speaker 4: I'm going to upload them to chat GPT to you know,

530
00:28:52,080 --> 00:28:54,360
Speaker 4: get an understanding of like where they all live. I

531
00:28:54,400 --> 00:28:56,600
Speaker 4: don't know what why you would possibly do that, but

532
00:28:56,640 --> 00:28:59,000
Speaker 4: let's say you are and then you know, it'll note

533
00:28:59,040 --> 00:29:00,920
Speaker 4: that looks like a social security number. You can't upload

534
00:29:00,920 --> 00:29:02,040
Speaker 4: that into your prompt.

535
00:29:01,920 --> 00:29:07,080
Speaker 2: So it will stop you before you Yeah, thoughtful voice

536
00:29:07,080 --> 00:29:09,719
Speaker 2: over your shoulder, just to remind you not to do

537
00:29:09,760 --> 00:29:13,600
Speaker 2: something silly exactly. But this is just talk a little

538
00:29:13,640 --> 00:29:17,120
Speaker 2: bit more about adding AI into this mix. You say

539
00:29:17,120 --> 00:29:20,680
Speaker 2: it's a force multiplier. It's a really interesting dig into that.

540
00:29:20,720 --> 00:29:24,520
Speaker 2: What other instances of what that means? How does the

541
00:29:25,040 --> 00:29:31,280
Speaker 2: balance between AI and human expertise work in the kind

542
00:29:31,280 --> 00:29:33,000
Speaker 2: of next generation of cybersecurity?

543
00:29:33,840 --> 00:29:38,120
Speaker 3: I think the common way to look at as it

544
00:29:38,280 --> 00:29:41,800
Speaker 3: back to the force multipliers, It's not going to be

545
00:29:42,000 --> 00:29:44,240
Speaker 3: is your AI better? But can you use it better?

546
00:29:44,640 --> 00:29:48,360
Speaker 3: Can you ask your AI the right questions? Are you

547
00:29:48,440 --> 00:29:51,880
Speaker 3: well trained? So the competition really becomes your use of AI?

548
00:29:52,680 --> 00:29:56,080
Speaker 3: And are you pointed it in the right direction. You

549
00:29:56,080 --> 00:29:59,120
Speaker 3: have fifty people, can they do the work of two

550
00:29:59,240 --> 00:30:02,200
Speaker 3: hundred and fifty? And can they do it in a

551
00:30:02,240 --> 00:30:05,320
Speaker 3: safe and secure manner? So you're not opening up more

552
00:30:05,440 --> 00:30:08,680
Speaker 3: risk based on or too much risk is your risk

553
00:30:08,760 --> 00:30:11,520
Speaker 3: tolerance in order to get the outcome. So that's why

554
00:30:11,640 --> 00:30:14,720
Speaker 3: I think there's the opportunity. And so you see this

555
00:30:15,320 --> 00:30:18,040
Speaker 3: truly as a force multiplier because the first thing people go, oh,

556
00:30:18,080 --> 00:30:20,880
Speaker 3: you're going to get rid of people. Oh, the people

557
00:30:20,960 --> 00:30:24,920
Speaker 3: portion is still still going to be just as important

558
00:30:24,920 --> 00:30:26,760
Speaker 3: because they're doing that other piece of work.

559
00:30:26,840 --> 00:30:29,000
Speaker 4: One of my favorite statistics is that there are now

560
00:30:29,120 --> 00:30:31,240
Speaker 4: more bank tellers in the US than there were in

561
00:30:31,320 --> 00:30:34,480
Speaker 4: nineteen sixty before the ATM was invented. Right, So, but

562
00:30:34,480 --> 00:30:36,200
Speaker 4: it used to be you would go to your bank

563
00:30:36,680 --> 00:30:39,000
Speaker 4: because you had to. I remember doing this. You go,

564
00:30:39,160 --> 00:30:40,880
Speaker 4: you fill out your deposit slip, you hand it to

565
00:30:40,960 --> 00:30:43,960
Speaker 4: the teller and they give you your cash. And then ATMs

566
00:30:43,960 --> 00:30:45,680
Speaker 4: are invented. It's like, oh no, what's going to happen?

567
00:30:45,720 --> 00:30:48,480
Speaker 4: All these jobs and now there's more, Right, but you're

568
00:30:48,520 --> 00:30:51,360
Speaker 4: not withdrawing money from a bank teller. You're now doing

569
00:30:51,360 --> 00:30:55,480
Speaker 4: more sophisticated transactions. And so I think it's similar with AI, right,

570
00:30:55,600 --> 00:30:57,960
Speaker 4: like you want people doing things that only people can do.

571
00:30:58,440 --> 00:31:01,280
Speaker 2: The human element remains absolutely central in all of this.

572
00:31:02,840 --> 00:31:07,440
Speaker 2: How do you make sure that your cybersecurity folks are

573
00:31:07,640 --> 00:31:10,600
Speaker 2: equipped to handle high value tasks are ready for this

574
00:31:10,680 --> 00:31:12,520
Speaker 2: increasing responsibility.

575
00:31:13,120 --> 00:31:15,040
Speaker 4: There's a couple of ways to answer this, but I

576
00:31:15,080 --> 00:31:19,480
Speaker 4: think the more you're able to automate the routine and

577
00:31:19,520 --> 00:31:24,440
Speaker 4: the mundane tasks. For example, the bulk of cybersecurity happens

578
00:31:24,440 --> 00:31:27,360
Speaker 4: in the security operations center. There's analysts who are sitting

579
00:31:27,360 --> 00:31:30,480
Speaker 4: in that center. If they're spending all day either configuring

580
00:31:31,560 --> 00:31:34,160
Speaker 4: alerts or responding to alerts, they're not able to do

581
00:31:34,200 --> 00:31:36,960
Speaker 4: the advanced sort of threat hunting and analysis work. And

582
00:31:37,000 --> 00:31:38,680
Speaker 4: so I think a big chunk of it is just

583
00:31:38,720 --> 00:31:41,440
Speaker 4: freeing up their time to be able to do the

584
00:31:41,480 --> 00:31:44,400
Speaker 4: more advanced strategic work. And a lot of the automation

585
00:31:44,480 --> 00:31:48,440
Speaker 4: tools based on AI, like our cortex XIM product, is

586
00:31:49,160 --> 00:31:51,360
Speaker 4: it's designed to free up their time in order to

587
00:31:51,360 --> 00:31:52,040
Speaker 4: be able to do that.

588
00:31:52,640 --> 00:31:56,120
Speaker 3: And from our perspective, is making sure that it's a

589
00:31:56,160 --> 00:32:00,280
Speaker 3: requirement to make sure that you have the qualifications because

590
00:32:00,280 --> 00:32:02,960
Speaker 3: people can easily get used to doing what they've always done.

591
00:32:03,400 --> 00:32:06,360
Speaker 3: I know this, and that's that's what I do. You say, well, no,

592
00:32:07,880 --> 00:32:10,760
Speaker 3: all the threat actors are learning on the fly. They're

593
00:32:10,800 --> 00:32:14,320
Speaker 3: trying to always outsmart you. So it's in your best interest,

594
00:32:14,400 --> 00:32:17,000
Speaker 3: our best interest, our client's best and partners best, and

595
00:32:17,320 --> 00:32:20,200
Speaker 3: that you are on the front leaning edge of that

596
00:32:20,560 --> 00:32:21,680
Speaker 3: learning capability.

597
00:32:22,040 --> 00:32:24,000
Speaker 2: If you're talking to a client he wants to develop

598
00:32:24,080 --> 00:32:29,480
Speaker 2: a kind of unified cybersecurity strategy, what's the best single

599
00:32:29,520 --> 00:32:33,280
Speaker 2: piece of advice you can give them?

600
00:32:33,320 --> 00:32:37,400
Speaker 4: You should have a single platform. It's hard not to

601
00:32:37,440 --> 00:32:40,000
Speaker 4: answer that, but it is true. I mean all joking

602
00:32:40,040 --> 00:32:43,720
Speaker 4: aside having you know the best of breed solutions that

603
00:32:43,760 --> 00:32:45,680
Speaker 4: are all talking to each other and able to stitch

604
00:32:45,720 --> 00:32:49,000
Speaker 4: together and identify threats before human might be able to.

605
00:32:49,920 --> 00:32:52,360
Speaker 4: That's number one, and number two is making sure you

606
00:32:52,360 --> 00:32:55,920
Speaker 4: have visibility on all elements so you're able to cover

607
00:32:55,960 --> 00:32:58,600
Speaker 4: your whole environment and understand how people are accessing it.

608
00:32:58,880 --> 00:33:03,360
Speaker 3: I'd say think like a actor. Yeah, always think outside

609
00:33:03,440 --> 00:33:06,280
Speaker 3: in because you get comfortable the other way around.

610
00:33:07,920 --> 00:33:12,160
Speaker 2: You guys work together with a fortunate enviioutrial company, and

611
00:33:12,480 --> 00:33:14,120
Speaker 2: I'd love for you to talk a little bit about

612
00:33:14,280 --> 00:33:16,360
Speaker 2: use that as a kind of case study for what

613
00:33:16,440 --> 00:33:20,920
Speaker 2: this collaboration between the two your two companies looks like.

614
00:33:21,280 --> 00:33:22,840
Speaker 2: When you work with a cloud.

615
00:33:23,040 --> 00:33:26,000
Speaker 4: It really was, you know, IBM leading on a digital

616
00:33:26,000 --> 00:33:29,320
Speaker 4: transformation for this client that wanted to move their applications

617
00:33:29,360 --> 00:33:31,320
Speaker 4: into the cloud, and so you're asking a lot of

618
00:33:31,400 --> 00:33:34,080
Speaker 4: questions about how does AI increase the risk and the

619
00:33:34,120 --> 00:33:36,480
Speaker 4: surface area. Those same questions ten years ago were asked

620
00:33:36,480 --> 00:33:38,880
Speaker 4: about the cloud. And we're still on the journey where

621
00:33:38,960 --> 00:33:42,040
Speaker 4: where companies are migrating to the cloud. We're not anywhere

622
00:33:42,080 --> 00:33:44,240
Speaker 4: near finished that yet. And so there's two pieces to

623
00:33:44,280 --> 00:33:46,440
Speaker 4: a cloud migration. One is just refactoring for the cloud

624
00:33:46,480 --> 00:33:48,760
Speaker 4: to make sure the application works effectively in the cloud.

625
00:33:49,080 --> 00:33:51,040
Speaker 4: And the second is security. And then you built in

626
00:33:51,040 --> 00:33:54,640
Speaker 4: security by design using POW does prison of cloud products

627
00:33:54,640 --> 00:33:56,600
Speaker 4: to make sure that not only did you have the

628
00:33:56,680 --> 00:33:59,200
Speaker 4: visibility so our cloud product you can scan and see

629
00:33:59,200 --> 00:34:02,640
Speaker 4: where the vulnerability. And then there's also you know, cloud

630
00:34:03,120 --> 00:34:07,080
Speaker 4: firewalls essentially that will keep bad actors out and keep

631
00:34:07,120 --> 00:34:08,600
Speaker 4: the cloud instant secure.

632
00:34:09,640 --> 00:34:12,520
Speaker 2: If we sit down and have this conversation five years

633
00:34:12,560 --> 00:34:15,120
Speaker 2: from now, which I actually hope we do, be fun,

634
00:34:16,320 --> 00:34:20,840
Speaker 2: this pretend is twenty twenty nine. Tell me what are

635
00:34:20,880 --> 00:34:22,480
Speaker 2: you happy about in twenty twenty nine.

636
00:34:22,719 --> 00:34:28,360
Speaker 3: I think twenty twenty nine quantum computing is mainstream. I

637
00:34:28,360 --> 00:34:34,080
Speaker 3: think quantum computing is now quantum safe, where we're using

638
00:34:34,880 --> 00:34:39,720
Speaker 3: quantum computing to make sure that those bad actors aren't

639
00:34:39,719 --> 00:34:42,040
Speaker 3: as bad as they used to be back in twenty

640
00:34:42,040 --> 00:34:46,160
Speaker 3: twenty four, and that we're seeing around the corners and

641
00:34:46,280 --> 00:34:51,400
Speaker 3: that we're empowering our Palo Alto relationship. That in twenty

642
00:34:51,480 --> 00:34:56,279
Speaker 3: twenty nine is the premiere type of capability that people

643
00:34:56,280 --> 00:34:59,319
Speaker 3: are looking at when they think of what used to

644
00:34:59,360 --> 00:35:02,279
Speaker 3: be AI. Now is quantum capability.

645
00:35:02,520 --> 00:35:07,319
Speaker 4: Yeah, yeah, I think for AI, everyone's just using it

646
00:35:07,320 --> 00:35:10,160
Speaker 4: as part of their job. The way email was an

647
00:35:10,200 --> 00:35:12,840
Speaker 4: innovation in the nineties, the way you know cloud was

648
00:35:12,840 --> 00:35:15,719
Speaker 4: an innovation in the twenty tens, and we thought, how

649
00:35:15,719 --> 00:35:17,279
Speaker 4: are we going to use this? What impact is it

650
00:35:17,320 --> 00:35:19,759
Speaker 4: going to have on productivity? All these people who are

651
00:35:19,760 --> 00:35:22,000
Speaker 4: spending their days typing up memos, like what are they

652
00:35:22,000 --> 00:35:24,520
Speaker 4: going to do? We're going to be past that fear

653
00:35:24,640 --> 00:35:27,239
Speaker 4: and we're all going to understand that it is this

654
00:35:27,520 --> 00:35:31,319
Speaker 4: like truly positive force multiplier. For you know, every employee

655
00:35:31,440 --> 00:35:35,200
Speaker 4: is able to do their best work and spend their

656
00:35:35,239 --> 00:35:37,279
Speaker 4: time on the things that only they can do, and

657
00:35:37,320 --> 00:35:39,480
Speaker 4: then the AI is doing the rest of that for them.

658
00:35:39,560 --> 00:35:44,160
Speaker 3: Right, AI is fun to enable many things to work together.

659
00:35:44,400 --> 00:35:47,799
Speaker 3: It won't be just one language model. We won't even

660
00:35:47,840 --> 00:35:51,040
Speaker 3: think about. It will be the difference between you know,

661
00:35:51,920 --> 00:35:56,360
Speaker 3: Malcolm having a fax machine, stereo and a telephone and

662
00:35:56,840 --> 00:35:59,600
Speaker 3: a memo board. Now it's in your pocket and it's

663
00:35:59,640 --> 00:36:01,880
Speaker 3: all one thing and you don't even call that, you know.

664
00:36:01,920 --> 00:36:03,680
Speaker 3: I said, walk them into my kids the other day

665
00:36:03,680 --> 00:36:07,319
Speaker 3: and they're like, what's a walk man? So I do

666
00:36:07,400 --> 00:36:09,319
Speaker 3: think it will. It'll be part of the past and

667
00:36:09,360 --> 00:36:12,520
Speaker 3: it's it will be the thought of the seamless connection.

668
00:36:12,640 --> 00:36:19,680
Speaker 3: That is secure seamless connection, of HR, of finance, of distribution, logistics,

669
00:36:19,680 --> 00:36:23,360
Speaker 3: of billing, all of those will have a capability to

670
00:36:23,400 --> 00:36:24,360
Speaker 3: work together.

671
00:36:24,719 --> 00:36:28,160
Speaker 2: Yeah, I have to do some social quick fire questions

672
00:36:28,640 --> 00:36:29,640
Speaker 2: you guys ready.

673
00:36:29,400 --> 00:36:29,879
Speaker 3: All right?

674
00:36:31,320 --> 00:36:34,000
Speaker 2: What's the number one thing that people misunderstand about AI?

675
00:36:35,440 --> 00:36:38,680
Speaker 3: The reliance on data? What do you mean by that?

676
00:36:39,480 --> 00:36:42,000
Speaker 3: I think that it's just assumed that it's happening and

677
00:36:42,200 --> 00:36:44,680
Speaker 3: it can just go out and grab data anywhere.

678
00:36:45,680 --> 00:36:47,319
Speaker 2: Yeah, you have.

679
00:36:47,280 --> 00:36:50,520
Speaker 3: To have good data, reliable data, and access to the data.

680
00:36:51,239 --> 00:36:53,000
Speaker 4: I mean people are too afraid of it.

681
00:36:53,400 --> 00:36:56,480
Speaker 2: Checkbox and image generators are the biggest things in consumer

682
00:36:56,520 --> 00:36:58,440
Speaker 2: AI right now. What do you think is that next

683
00:36:58,480 --> 00:36:59,759
Speaker 2: big business application?

684
00:37:00,760 --> 00:37:04,000
Speaker 3: I think it's the tying together of multiple capabilities. I'm

685
00:37:04,080 --> 00:37:07,360
Speaker 3: hinted towards this earlier is I think tying together the

686
00:37:07,480 --> 00:37:10,160
Speaker 3: disparate systems that sit in different parts of the organization

687
00:37:10,200 --> 00:37:12,839
Speaker 3: front office, back office, making it one office and tying

688
00:37:12,840 --> 00:37:14,560
Speaker 3: together those different functions. That's it.

689
00:37:15,640 --> 00:37:18,080
Speaker 4: I mean, it's workflow automation. I think back to your

690
00:37:18,080 --> 00:37:21,319
Speaker 4: point on the reliance on data seems easy. It's a

691
00:37:21,320 --> 00:37:22,759
Speaker 4: lot harder than you think, because you have to have

692
00:37:22,800 --> 00:37:24,760
Speaker 4: everything set up in exactly the right way to get

693
00:37:25,000 --> 00:37:27,680
Speaker 4: all of your systems automated and the sort of the

694
00:37:27,680 --> 00:37:29,839
Speaker 4: more boring jobs taken care of so that humans could

695
00:37:29,880 --> 00:37:30,880
Speaker 4: do the strategic ones.

696
00:37:32,600 --> 00:37:35,080
Speaker 2: How are you already using AI in your day to

697
00:37:35,120 --> 00:37:35,600
Speaker 2: day life?

698
00:37:37,800 --> 00:37:40,720
Speaker 4: I mean I use it at work all the time,

699
00:37:41,360 --> 00:37:43,319
Speaker 4: and then I've found right now I go to chat,

700
00:37:43,400 --> 00:37:46,520
Speaker 4: GPT instead of Google to look things up. I like

701
00:37:46,560 --> 00:37:47,400
Speaker 4: having a conversation.

702
00:37:48,840 --> 00:37:52,920
Speaker 3: We have a wonderful capability in our consulting business called

703
00:37:53,520 --> 00:37:57,719
Speaker 3: our consulting assistant, and consulting advantage is the proper name

704
00:37:57,760 --> 00:37:59,760
Speaker 3: for it, but I look at it as that assistant.

705
00:38:00,400 --> 00:38:02,680
Speaker 3: It's a force multiplier for me. So if I need

706
00:38:02,719 --> 00:38:07,520
Speaker 3: to pull together content proposals with the teams, we go

707
00:38:07,600 --> 00:38:08,120
Speaker 3: straight to that.

708
00:38:08,960 --> 00:38:11,440
Speaker 2: We are one more we hear, so many definitions of

709
00:38:11,520 --> 00:38:14,799
Speaker 2: open related to technology. How do you define it and

710
00:38:14,840 --> 00:38:16,960
Speaker 2: how does the concept help you innovate?

711
00:38:18,120 --> 00:38:21,600
Speaker 4: By definition? In cybersecurity, you don't want to be too open, right,

712
00:38:22,040 --> 00:38:26,040
Speaker 4: So I think we enable openness with this concept of

713
00:38:26,120 --> 00:38:28,400
Speaker 4: zero trust and saying like everyone's invited in as long

714
00:38:28,400 --> 00:38:31,080
Speaker 4: as you have the right credentials, right. So that's that's

715
00:38:31,160 --> 00:38:33,000
Speaker 4: one way, and then the other way is just making

716
00:38:33,040 --> 00:38:36,560
Speaker 4: sure you're connected to all the different systems in order

717
00:38:36,560 --> 00:38:38,880
Speaker 4: to be able to have that visibility and see what's happening.

718
00:38:38,880 --> 00:38:42,719
Speaker 4: Because if you are blind, that's the minute you have

719
00:38:42,719 --> 00:38:43,560
Speaker 4: that vulnerability.

720
00:38:43,719 --> 00:38:49,680
Speaker 3: Yeah, and I'd say it's moving quickly with security. It

721
00:38:49,760 --> 00:38:53,439
Speaker 3: sounds contradictory open. Oh then that means you're not safe. No,

722
00:38:53,520 --> 00:38:54,960
Speaker 3: you are safe and you can move faster.

723
00:38:55,280 --> 00:38:56,960
Speaker 2: Yeah, thank you so much. It's fun.

724
00:38:57,080 --> 00:38:58,680
Speaker 4: Thanks a lot, Thank you great. We'll see you in

725
00:38:58,719 --> 00:38:59,200
Speaker 4: five years.

726
00:38:59,280 --> 00:39:03,720
Speaker 2: Yea years, five years, man, I'll be old in five years.

727
00:39:05,960 --> 00:39:09,719
Speaker 2: Thank you to Jason kellyot IBM and Christy Fredericks at

728
00:39:09,760 --> 00:39:14,440
Speaker 2: Palo Alto Networks for that fascinating conversation about the threats

729
00:39:14,480 --> 00:39:20,280
Speaker 2: and opportunities in cybersecurity today. As Jason and Christie stressed,

730
00:39:20,640 --> 00:39:25,120
Speaker 2: AI can be a force multiplier for enterprise across industries.

731
00:39:26,120 --> 00:39:28,600
Speaker 2: When you're working with multiple products and have your data

732
00:39:28,640 --> 00:39:32,920
Speaker 2: in distributed environments, you need technology that will work across

733
00:39:33,000 --> 00:39:37,560
Speaker 2: your organization, and with Palo Alto Networks platform, you can

734
00:39:37,680 --> 00:39:43,600
Speaker 2: enhance cyber resiliency and simplify your operations. Through their collaboration,

735
00:39:44,000 --> 00:39:47,520
Speaker 2: IBM and Palo Alto Networks are charting the future a

736
00:39:47,640 --> 00:39:54,880
Speaker 2: fully integrated open end to end security solutions. Smart Talks

737
00:39:54,880 --> 00:39:58,200
Speaker 2: with IBM is produced by Matt Romano, Joey fish Ground,

738
00:39:58,320 --> 00:40:03,080
Speaker 2: Amy Gaines McQuaid, and Jacob Goldstein. We're edited by Lydia

739
00:40:03,120 --> 00:40:07,000
Speaker 2: Jane Kott. Our engineers are Sarah Bruguerer and Ben Tolliday.

740
00:40:07,320 --> 00:40:10,200
Speaker 2: Theme song by Gramoscope. Special thanks to the eight Bar

741
00:40:10,520 --> 00:40:14,239
Speaker 2: and IBM teams, as well as the Pushkin Marketing team.

742
00:40:14,640 --> 00:40:17,400
Speaker 2: Smart Talks with IBM is a production of Pushkin Industries

743
00:40:17,680 --> 00:40:21,960
Speaker 2: and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,

744
00:40:22,280 --> 00:40:26,080
Speaker 2: listen on the iHeartRadio app, Apple Podcasts, or wherever you

745
00:40:26,200 --> 00:40:31,440
Speaker 2: listen to podcasts. I'm Malcolm Glapham. This is a paid

746
00:40:31,520 --> 00:40:36,400
Speaker 2: advertisement from IBM. The conversations on this podcast don't necessarily

747
00:40:36,440 --> 00:40:51,160
Speaker 2: represent IBM's positions, strategies, or opinions,