When ATMs Fail: Banking Outages, Black Swans & Digital Risk

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Hello, and thank you for joining me.

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I'm Bill Alderson with Disaster Stream.

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This is where we talk about data

disasters that are extraordinary, and

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the responders who have experience

dealing with these type of disasters.

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We look into the lessons learned,

and on this particular occasion,

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we're gonna talk with Bill Genovese.

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He's with Kyndryl.

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And he's gonna talk about a

situation sometime back where a

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major bank lost all of its ATMs on

a holiday weekend, not a good time.

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He's also going to discuss some

concepts that may or may not be

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new to you, but he talks about.

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A gray swan and a black swan.

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Now a black swan is

like a zero day attack.

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No one's ever seen it before.

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Unanticipated risk.

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And then a gray swan is

something just a little bit less.

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But his discussion will talk about how

multiple of these type of things can

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equal a much larger situation and event.

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Each week we talk about various

disaster responder stories.

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I'd like to tell your story.

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No doubt you have some lesson learned

that can be turned into a best practice

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to help a major institution either

recover faster or obviate some sort of

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a problem that they have been having.

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I invite you to contact and let

us know that you're interested.

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Now this is Bill and he has a huge

resume of solving a lot of various

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problems with his critical problem

resolution teams flying in all around

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the world, solving problems when

he was at IBM and other locations.

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Bill is at Kyndryl and he advises

leaders of large institutions.

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You can read a little bit of his

resume and he's gonna introduce

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himself in just a couple of minutes.

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I like to introduce you to

organizations and or people who are

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consequential and helpful to all of us.

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And one of those organizations

is ISSA, the Information System

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Security Association International.

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I happen to be a board member

of our local Austin chapter.

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I take and make sure that we

record those sessions, and so

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they're out there publicly and I

will introduce one of them to you.

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I'm gonna give you just a little

bit of a teaser about one or

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two minutes during this session.

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And also introduce you to Nick

Leghorn who's with the New York Times.

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Nick takes care of application security

at the New York Times, and he is going

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to take us through an insightful ability

to learn how to write Cyber policies

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that aren't miserable for everyone.

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And he takes into a lot

of experienced consider.

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How to go about reviewing your policies

and then how to write good ones that

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are not just click the box, good

but truly working Cyber policies.

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I'll break in during our session and

I'll introduce you to Nick, and he'll

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pop in and give you about a one or

two minute talk, and then you can

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decide in the show notes, I'll give

you links to Nick's entire session.

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Now these are some of the slides

that we're gonna go through.

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I just wanna make sure that you

see what you're getting into here.

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This is what Bill Genovese is gonna

talk about a little bit, and he

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goes through and explains these

multidimensional events and issue.

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And risks and market risks

and operational risks.

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He also shows us the top global risk

because Bill is a global perspective

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person, and so you're gonna enjoy hearing

his perspective on these different things.

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And he's gonna talk about the Basel

committee on international banking,

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you're gonna really enjoy this.

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He also talks about the

governance and readiness.

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And these are international standards

and supervisory agencies, some of

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which are US agencies, but many

of these are international banking

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agencies that are the regulators

of the world's banking systems.

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He'll go through that with you.

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The purpose of our broadcast is to

take the best practices that we glean

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out of the responder stories in other.

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What did they learn?

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What were the lessons learned during

the disaster recovery that we can

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take and turn into best practices, and

then we can imput those best practices

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into your organization so that you can

either obviate problems or reduce the

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impact of problems in your organization.

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Thank you so much.

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Now we're gonna get right into

it and Bill Genovese will.

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And introduce himself.

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I will go back and forth with him a little

bit, but he's got some great stories

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that you're really gonna love to hear.

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So thank you so much.

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And here comes the interview

with Bill Genovese.

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Hello, I'm Bill Alderson and we're

talking here with a leader at Kyndryl,

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and he's going to introduce himself.

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Bill.

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Hi.

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Thanks, Bill.

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My name is Bill Genovese.

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I live in St.

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Augustine, Florida, but I've lived

and worked all over the world.

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In terms of full stack architecture

and technology at the intersection of

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primarily one major vertical, which I

would say financial services in that

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umbrella, banking, capital markets,

securities, investment banking and

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insurance, and diversified healthcare

to a second extent, and then telco.

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And mainly from the provider side.

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So I'm a second generation IBM er.

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I worked in four divisions

in IBM on five continents.

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I've been all over the world

with big blue ex Big4 consultant.

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So I was with KPMG, two tours of

duty with KPMG as a senior consultant

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and then a director on a contract

basis in their technology M&A group.

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Doing due diligence for

acquisitions for clients.

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So I've worked in a number of areas

and I've never really escaped risk.

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And everything that comes

under that umbrella.

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I've worked in a number of business

units in IBM, high availability

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Center of competency as part of a lab

services executive consulting firm all

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over the world to their technology in

major Fortune 50 banks in enterprises.

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So this is a topic near

and dear to my heart.

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look forward to the discussion and

seeing where this kind of takes us over

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the next few minutes, an hour or so.

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Bill, it's really a pleasure to

have met you and started to engage

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hearing some of the incredible high

visibility, high stakes stories.

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Of response and problem resolution

for yourself and your organizations.

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Really looking forward

to some of these stories.

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Now, I'm not really exactly sure

how much we're gonna be able to

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get done in the next hour or so,

because you have a prolific career.

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If you go and look at

your LinkedIn profile

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. It's just littered with large

organizations with critical problems.

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Let's get right into it.

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Bill, what do you think are some of the

stories, and we can go into more detail,

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but just give me a synopsis of some of the

type of issues that you've dealt with in

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the area of critical problem resolution,

disaster recovery, unanticipated risks

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that have become actual risks today.

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So help me understand some of the

things that you might be able to

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help us with in this particular area.

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Yeah, my, my experience and , what

I've encountered throughout my career,

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primarily, contextually this is

mostly relevant to my work with IBM.

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And Kyndryl is a division of IBM

or was a division of IBM Global

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Technology Services, where I worked

for almost six years in two countries.

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So I know this space quite well and

I am a CIO executive advisor as part

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of our advisory services practice

in Kyndryl now working with CXOs.

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So resiliency is still

very much top in mind.

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It's part of implementing

digital transformation.

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But when I was with IBM Global

Technology Services gts, which is now

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Kyndryl, a lot of the content in where

my career took off was in the result

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of outages, a nd stability issues.

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So I followed the career path from

consultant and I moved more towards

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the technology engineering side of

the house in IBM and architecture and

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number of profession certifications.

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And as you cover more and more

architecture and technology from an

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enterprise perspective, obviously

you're working in different camps,

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different layers of that architecture.

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So what may have started earlier

career as an application or software

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architecture engineer move more

into infrastructure and data center.

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And up and down the platform.

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So invariably when there is an

outage in a major enterprise,

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where does it occur first?

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It's usually a cross-platform service

or it's unknown to the firefighters.

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Is it the application?

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Is it the database?

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Is it the infrastructure?

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Usually the investigative

discovery process starts at the

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infrastructure in the data center.

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And that's where a lot of the

focus is in terms of remediation

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teams and support teams.

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Which is fine if it was 15, 20,

25 years ago where one application

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sat on one platform, but in digital

transformation in a major enterprise

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in a major industry, vertical and

international bank, internet banking,

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. Or ATMs don't all sit on

one hardware platform.

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So it creates required correlation

across support teams to see

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exactly where the issue is.

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In my career in IBM, as I became more

and more of a senior architect and chief

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engineer, chief architect client technical

leader, so these were the folks that

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were actually advising CIOs and banks.

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And, I was the most senior

technical leader on the account

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for selected Fortune 20 accounts.

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If there was a major issue like an

outage, we would get called in and

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work with the teams over the contract

period to remediate what that is.

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I've seen a number of situations

and issues ATMs going down

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right before a holiday weekend.

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Performance degradation issues.

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It could be as rudimentary as annual

or biannual disaster recovery testing.

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That goes fine in terms of flipping over

to the DR site, but the client or customer

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can't come back . And it's good to see

that your DR is working in the event of

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a smoke hole in the ground situation.

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But how do you come back

to restore everything?

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And if that can't be done,

that's a challenge as well.

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So everything in between.

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Another highlight in my career

after I left GTS Global Technology

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Services and IBM, I moved to

systems and technology groups, high

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availability center of competency.

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So with that team of experts.

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The best of the best.

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We were a very elite SWAT team that would

parachute into anywhere in the country,

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world, I should say, on a moment's

notice to remediate outages and have week

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long discovery sessions to get to the

real root cause of what was going on.

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And more often than not, there's a

familiar pie chart that is always

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in burned in my mind in memory.

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Outages are not caused primarily

by infrastructure problem.

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It's usually service process management.

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Or applications first and foremost.

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Then infrastructure.

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So these workshops and the remediation

efforts that we would get into would

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be carved into technology days and then

service or process management days.

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because we would want to see exactly

what's going on that's contributing

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to the outage in future remediation.

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I think that gives you a good cross

composite in more recent years in

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between working for big IT providers,

I've done work in the M&A space.

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I've worked with smaller

companies and startups and

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tier two, tier three companies.

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And my knowledge and expertise

has helped me do due diligence

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in terms of acquisitions.

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So if a private equity firm was going

to buy a company, a smaller company

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for their portfolio, what types

of things should they look for in

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terms of a risky investment in terms

of stability in the infrastructure

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and cloud provider, as an example.

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Yep, I'm very lucky, honored

and humbled to be here and I've

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had a very good career, I think

and hope to keep contributing.

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Very nice.

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Bill.

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It's really interesting to, to hear

some of those stories, especially

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on the international level, that it

wasn't merely in one market, but a

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myriad of markets across the globe.

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So in, in our initial discussions in

talking, I remember a few different

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scenarios that you spoke of in detail.

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Are there some of those that

you'd like to highlight today?

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Yeah, one, one long-term contract

that I was involved with IBM, it

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was a very important account for us.

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It was very high up in terms of

account focus, our relationship

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with the customer and the client.

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We had a 10 year managed service

strategic outsource deal.

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It was a bank in Southeast Asia.

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I had finished up another

engagement in the same division

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in IBM for another bank in Europe.

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And I was in between assignments

and I was due to return back to my

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home country, the United States.

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But then I found out, I was contacted

about this other opportunity and

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actually there were two banks that were

experiencing some level of stability

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issues, both in Southeast Asia.

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And both were managed service accounts.

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And it turns out I was supposed to

go to Thailand but I got rerouted

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to a higher situation crit, sit.

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For a bank that experienced island wide

ATM network outage the day before major

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holiday weekend, public holiday weekend.

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Now Bill when ATMs go out whether it's

a weekend or not, but in particular

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holidays, what happens to the community?

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And then how is that high visibility,

high stakes kind of issue, how

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does that get pushed back onto a

system provider or somebody who's

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providing assistance or services?

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How does that affect.

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the company who's experiencing

the problem, and then yourself

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on the other end trying to help.

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Yeah.

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So to frame that a bit, it's good to time

box it in terms of when this happened.

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Because that should give some context in

terms of where we are in the industry and

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overall as a planet in that timeframe.

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The question I would throw

out was mobile banking.

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And the ability to consume digital

financial services from a payment.

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Payment transfer, moving money around

between accounts, paying from your

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phone, paying from your face, ordering

stuff remotely, whatever you want to do.

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Was it the same as it

is now post pandemic?

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I think we all know the answer to that.

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No.

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It wasn't So if you experience a mainframe

outage, , with the lack of mobile

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banking and mobile financial services,

that's widely pervasive and used almost

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as a default mechanism as it is today,

there's gonna be an impact to a society,

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? People need to get money.

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Out of their ATMs.

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Before holiday weekend

before they travel somewhere.

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Or I can think of a myriad of situations

why you need to get to the atm.

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And so very highly impactful situation.

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In terms of the core livelihood

of a banking institution.

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I can only imagine if, like today a lot of

us have reduced the size of our physical

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wallet and we put one bank card inside a

sleeve of our cell phone to take with us.

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And that's the only one that we have.

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And now, if that were the chosen bank

for our a hundred percent dependency

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and we're on a motor trip from one

location to another location, The

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ability to get petrol, the ability

to get food, the ability to stay

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in a hotel is now highly impacted.

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So no doubt customers of the bank are

screaming bloody murder at this point.

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Yeah, exactly.

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And, you hit the nail on the head

in: 2010

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wallet and essentially living from a

digital financial services consumption

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model from the perspective of the

brick in your hand, didn't exist.

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or it was just starting.

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, so you were tied to that atm,

you're tied to that debit

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card more so than you are now.

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13.

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13 years later.

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Yeah.

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You can't just add another card to

your Apple Pay or your Google Pay

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wallet and change cards easily.

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Right on, on the run, you're pretty

much stuck with a physical card

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that it either works or it doesn't

work, and when it doesn't work

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it's essentially catastrophic.

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It's a catastrophic disaster for that

person in that situation out of town.

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So did the companies that you were

working with, The level of urgency that

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their customers were pressing them with?

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Absolutely.

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Once again this was a very successful

account up to that point, with IBM.

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Who I was working with at the

time and it was year eight of

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the first 10 year contract.

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So we were entering, when you go

into a renewal for a managed service

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outsource contract, you're not

waiting until year 10 or year four.

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You're starting the discussions year

seven and eight, and you're positioning

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what's gonna be in that renewal.

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So for this to happen in year eight,

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, Is catastrophic potentially.

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So very high visibility from

the provider perspective.

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The other perspective is, I'm not gonna

get into naming any clients or customers

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here, but in smaller countries, ? In

Southeast Asia, other parts of the

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world, the most successful banks are

the poster children in terms of their

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visibility with the regulatory bodies.

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? So the smaller institutions can be a

little bit waffly, they can be unstable.

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They don't have the wallet

share of the population, they

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don't have the visibility.

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They're not the media darlings.

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So if one of the big anchor

banks goes down, , that is the

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wallet share of the country.

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That's a major ordeal.

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So it's no longer just the

customer of a particular brand

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of bank, but now it has national

significance within the geopolitical

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organization that they're a part of.

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And of course that goes on the

nightly news, yeah, you can

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continue that thread of thinking,

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. This is a representative pillar of

industry representing where that

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country is going in terms of technology

innovation, and it has a failure.

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That's not a good thing.

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So that's proverbial the black eye

that we talk about in industry, right?

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Exactly.

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Exactly.

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: 00:20:23

So you get a pretty clear picture

of the back backdrop and the

317

: 00:20:27

context of what I was facing.

318

: 00:20:30

So how did that end up

coming to, to closure?

319

: 00:20:34

How did you navigate your way through?

320

: 00:20:37

That critical problem?

321

: 00:20:39

It was a holistic approach.

322

: 00:20:41

All roads led me to that as a enterprise

architect in my career developing.

323

: 00:20:47

So I didn't go into that situation being

a web architect or an Oracle application

324

: 00:20:54

architect or a DB2 database architect.

325

: 00:20:59

At that point, I had worked across

all layers of the architecture

326

: 00:21:03

in a number of banks globally.

327

: 00:21:05

And then also I have a patent in

terms of automation and provisioning

328

: 00:21:10

and cloud environments with IBM I am

certified as a technology consultant

329

: 00:21:15

and architect in terms of systems

management and service management.

330

: 00:21:19

So when I went into that context

and why I think I was brought

331

: 00:21:23

in, I know why I was brought in.

332

: 00:21:25

It was that full comprehensive

diagnostic that I would need to do.

333

: 00:21:31

People, process and technology

in terms of, going into that dark

334

: 00:21:36

room and flipping on the light.

335

: 00:21:38

Where is everybody scattering from?

336

: 00:21:40

And it's not one, one situation.

337

: 00:21:43

We've gotta look at everything, ? And

rebuild the estate, the culture, the

338

: 00:21:49

people, the process, the organization,

the technology, the infrastructure

339

: 00:21:52

of the data center, and raise it from

three nines, availability to six nines.

340

: 00:21:59

And it was a complete, that's all I

did for two years to help my company

341

: 00:22:04

and the client and the regulator.

342

: 00:22:06

I was involved in discussions

with the regulator.

343

: 00:22:09

How do we turn this situation

around and in a preventative sense,

344

: 00:22:13

make sure it never happens again.

345

: 00:22:15

And looking back at that exact type of

situation, what are some of the lessons

346

: 00:22:21

learned that you brought forward to help

the organization improve their resiliency?

347

: 00:22:29

Good.

348

: 00:22:29

Very good question.

349

: 00:22:31

And this is why I brought up this example,

because, none of us is infallible.

350

: 00:22:35

We're always learning, I don't care

if I have 27 years of experience.

351

: 00:22:38

You have 40, somebody else has 60.

352

: 00:22:40

A couple of key points that

have stuck with me in my career.

353

: 00:22:43

Every single client situation that

I've been in, any country all over the

354

: 00:22:49

world, I could be in the same city,

in the same state in the United States

355

: 00:22:53

on the other side of this, and I'll

come across a different situation.

356

: 00:22:57

The context or the symptoms may be

very similar, but the solution is

357

: 00:23:02

never a hundred percent repeatable.

358

: 00:23:05

I There's always a wrinkle.

359

: 00:23:06

There's always something new that pops up.

360

: 00:23:09

Bill, are you basically saying that

there's no cookie cutter solution?

361

: 00:23:13

So essentially if, let's just say, of

course IBM has a lot of major clients

362

: 00:23:19

around the world and almost that one

time or another, almost every company,

363

: 00:23:24

but taking the solution from bank

A and simply applying it to bank B,

364

: 00:23:32

that doesn't seem from what you're

saying to be the way things work.

365

: 00:23:37

Saying it another way,

there's no silver bullet.

366

: 00:23:39

I will say even another way, if you

have a 95% silver bullet that you

367

: 00:23:43

implemented in the United States.

368

: 00:23:46

And then you went to Europe and maybe in,

in one or two countries it was a 92 or a

369

: 00:23:51

93% silver bullet due to other reasons.

370

: 00:23:55

And then you said you

based your assumption.

371

: 00:23:57

I've lived and worked all over

the United States in small,

372

: 00:24:00

medium, large environments.

373

: 00:24:02

It's worked everywhere here.

374

: 00:24:03

It's worked in two or three

countries with some differences.

375

: 00:24:06

Minor it's gonna work in

any eight country in Asia.

376

: 00:24:10

I learned the hard way.

377

: 00:24:12

Culturally that's not the case.

378

: 00:24:14

This is where I got slapped a bit in the

face with cultural and people differences.

379

: 00:24:21

In terms about technology

and services are delivered.

380

: 00:24:26

And in terms of risk appetite.

381

: 00:24:29

And approach and thinking.

382

: 00:24:31

Approaches to enterprise

architecture approaches to DevOps.

383

: 00:24:35

Approaches to teams working together

in terms of rigor and testing.

384

: 00:24:41

I can go on and on.

385

: 00:24:42

And I had to learn that certain ways,

mindsets of thinking in the United

386

: 00:24:47

States and the West and Europe.

387

: 00:24:50

I had to jettison and adjusts on

the fly from my experiences in Asia.

388

: 00:24:55

So Bill, can it be said that regardless

whether we're using the same technology,

389

: 00:25:03

i e mainframe or certain types of systems,

that almost every implementation of

390

: 00:25:11

a company's architecture has a unique

fingerprint that requires specialization

391

: 00:25:18

and theorists who can really look

at the true underlying technology

392

: 00:25:23

architecture, that it's not simply.

393

: 00:25:26

the same fingerprint that

company A has, and you can

394

: 00:25:31

simply apply that to company B.

395

: 00:25:32

I haven't found that to be

the case in almost anything.

396

: 00:25:38

You have three banks who have

mergers and all three of them have

397

: 00:25:43

completely different technology.

398

: 00:25:45

Even if they're using the same IBM

mainframes, their communications

399

: 00:25:49

architectures, everything requires some

type of specific planning to approach

400

: 00:25:57

their architecture in the way that

their architecture works and their

401

: 00:26:01

fingerprint of technology, so to speak

for that particular organization, which

402

: 00:26:06

makes it a lot more complex problem.

403

: 00:26:09

And like you said, you can't simply

use a paint by the number or a cookie

404

: 00:26:15

cutter plan to take disaster recovery

for company A and apply it to company B.

405

: 00:26:22

Absolutely.

406

: 00:26:23

So as architects, as engineers

we're all familiar with reference

407

: 00:26:26

architectures for industry.

408

: 00:26:28

What does an internet banking

reference architecture look like?

409

: 00:26:31

What does a core banking deposit

systems architecture look like?

410

: 00:26:34

And its deployment patterns.

411

: 00:26:36

But to your point, as everything

around it has transformed those

412

: 00:26:40

reference architectures are a point

in time or a specific point in an

413

: 00:26:44

organization from a pattern perspective.

414

: 00:26:47

What we need to be more adept

at is identifying outliers and

415

: 00:26:52

anti patterns as architects.

416

: 00:26:54

Those anti patterns that pop up right

now in the next outage or tomorrow become

417

: 00:27:01

inputs to the next reference architecture.

418

: 00:27:03

And that's how I would best describe

what you're framing up there.

419

: 00:27:06

And that's what I've

loved about my career.

420

: 00:27:08

I've been exposed to not only looking for

and being hung up on this is the reference

421

: 00:27:15

architecture I know and how it should

be, but I look for the anti patterns.

422

: 00:27:19

Now first, can you explain to us a

little bit about what you mean by those

423

: 00:27:25

anti-patterns so that we can get our

arms around that a little bit more?

424

: 00:27:30

Yeah, once again, going back in time

you had basic client server architecture

425

: 00:27:34

based technology, ? And, you had an

application with a call to a database, a

426

: 00:27:38

client, a thin, a thick client in a call

back to a database, via client server.

427

: 00:27:43

The application database could be

on a mainframe, but it was a thick

428

: 00:27:46

client on an application that was

installed on a workstation, then you

429

: 00:27:50

went into thin client, ? Then you

started to get involved with internet

430

: 00:27:54

banking, transforming in terms of a

anti pattern where you had front ends

431

: 00:27:58

on web server X 86 farms thin client.

432

: 00:28:02

Then you had some type of compilation.

433

: 00:28:05

Logic, computational logic,

mid-tier engine, which could

434

: 00:28:09

be on Unix risk-based systems.

435

: 00:28:12

And then you have, in terms of

messaging and connectivity, MQ

436

: 00:28:15

back to the mainframe database.

437

: 00:28:18

Yes.

438

: 00:28:18

And yeah, the, all of those technologies

can be used in a different pattern.

439

: 00:28:23

The message queuing systems, of course,

that IBM's famous for the database thin,

440

: 00:28:29

thick clients and all those various

architectures, even though they're the

441

: 00:28:34

same technologies, they're implemented

differently, perhaps different vendor

442

: 00:28:40

interfaces, different vendor computers.

443

: 00:28:43

So each one of those represents

an institution that has their own

444

: 00:28:48

fingerprint of technology that you

have to, as a theoretical expert,

445

: 00:28:54

you need to be able to look at.

446

: 00:28:55

Holistically, like you said,

and look at the exact problem

447

: 00:29:00

situation that they have.

448

: 00:29:01

And, teams in a support mechanism,

troubleshooting firefighter context

449

: 00:29:06

have not necessarily changed.

450

: 00:29:09

Along with those anti patterns.

451

: 00:29:11

In a holistic sense that says, I am the

internet banking service support guru, and

452

: 00:29:16

I'm gonna look across all three platforms.

453

: 00:29:19

No, it's more often that they're broken

out by infrastructure, platform and data

454

: 00:29:24

centers, and you've got three separate

individuals each looking at their monitor.

455

: 00:29:30

Representing their tier of the service

and them trying to figure out where's the

456

: 00:29:35

bottleneck, where's the outage occurring?

457

: 00:29:37

The front end's fine.

458

: 00:29:38

It must be you in the

middle . Yeah, exactly.

459

: 00:29:41

Exactly.

460

: 00:29:42

Bill, one of the things that I was

hoping that you might be able to share

461

: 00:29:45

with us is some of the messaging that

you find to be cogent and relative that

462

: 00:29:52

you're presenting now inside of your

advisory services, that you might wanna

463

: 00:29:57

take a couple of those anecdotal places

and show us a little bit or talk to

464

: 00:30:02

us about some of the messaging you're

helping large institutions understand

465

: 00:30:08

from a particular experience viewpoint.

466

: 00:30:11

Do you have anything to share with

us that you'd like to discuss?

467

: 00:30:16

Yeah, I mean there's a, there's, this

whole concept of multidimensional risk,

468

: 00:30:22

and, catastrophic events, we've all

heard the term black swan, ? And that

469

: 00:30:28

more or less can, be framed up of a

smoking hole in the ground scenario,

470

: 00:30:33

? That comes around once

every hundred years.

471

: 00:30:36

What, if you have two or three of these

events that are not necessarily smoking

472

: 00:30:42

hole in the ground, lights out events,

but they're problematic enough to disrupt

473

: 00:30:48

operations, and if you have two or

three of them happening at the same time

474

: 00:30:54

in different areas of the world the.

475

: 00:30:57

Combined aggregated result can

be even worse than a black swan.

476

: 00:31:02

And some of these are being

characterized or defined as gray swans.

477

: 00:31:08

So covid and the pandemic, not

necessarily a black swan a transformative

478

: 00:31:13

enough to it and the business that

major adjustments had to be made.

479

: 00:31:19

In terms of digital transformation I've

been speaking for a number of years

480

: 00:31:23

on what I call next generation digital

transformation with the advent, for

481

: 00:31:27

example, of mobile financial services.

482

: 00:31:29

This Gray Swan event of Covid.

483

: 00:31:33

The pandemic with some environmental

hurricanes, typhoons, regional war.

484

: 00:31:41

May have kept us cooped

up in our houses more.

485

: 00:31:44

And may have directly and indirectly

fueled the focus in institutions

486

: 00:31:50

to move further and faster with

digital transformation, including

487

: 00:31:54

mobile financial services.

488

: 00:31:56

Obviously we had to do things

more from a mobile position.

489

: 00:32:01

Just a case in point, bank of America

in my Austin community closed probably

490

: 00:32:09

a dozen bank branches two of which are

quite near to my home that I used to

491

: 00:32:16

avail myself to, and they were gone.

492

: 00:32:20

I drive over there and

they're completely closed.

493

: 00:32:23

Now, they kept the ATMs open at those

locations, but subsequently, two of

494

: 00:32:29

those are now completely closed down.

495

: 00:32:31

They even removed the ATMs.

496

: 00:32:34

So vast changes.

497

: 00:32:36

I think when you talk about this gray

swan, which is an interesting concept

498

: 00:32:41

that I'm definitely gonna wanna study

and keep my ears to the ground on,

499

: 00:32:46

because you're right, exactly what

you just said has major repercussions.

500

: 00:32:53

But it wasn't a black swan

catastrophic zero day event

501

: 00:32:59

that brought everything down.

502

: 00:33:00

It was kinda like putting the lobster

in the pot and turning up the heat.

503

: 00:33:06

It changed a little bit by little bit and

fundamentally affected how we do business.

504

: 00:33:14

I'll share a couple of slides here

if I can, to of frame up a bit

505

: 00:33:17

more than a bit more about what

I'm talking about further here.

506

: 00:33:22

Let me know when you can see my screen.

507

: 00:33:25

I can see it and I'm broadcasting it.

508

: 00:33:29

All right.

509

: 00:33:29

From a kind of framing up exactly

what I'm I was saying here, you know

510

: 00:33:33

what the background and the problem,

and this is not only, impacting

511

: 00:33:37

financial services it's multiple

industries in terms of a trend.

512

: 00:33:41

? So you have risk that's compounding,

due to multidimensional events.

513

: 00:33:47

Obviously as technologists we focus on

that middle pier pillar on the bottom

514

: 00:33:51

half of the diagram, operational risk,

but you also have a run on credit.

515

: 00:33:56

Based on the confluance of

multi-dimensional events, you

516

: 00:34:00

have a run on market risk of pay.

517

: 00:34:02

People are selling securities,

they need to become more liquid.

518

: 00:34:06

If you can't get into your broker

to make an appointment to sell

519

: 00:34:10

securities, and some still operate

that way, you wanna be able to sell

520

: 00:34:13

and liquidate your stock holdings or

options from the palm of your hand.

521

: 00:34:17

So there's other factors culturally

too that's further compounding this and

522

: 00:34:22

fueling from a consumer perspective the

need for the institution to be more agile.

523

: 00:34:28

If these events do come up

and they are compounding.

524

: 00:34:32

So interesting statistics across the top.

525

: 00:34:36

From a consumer experience perspective,

if these incidents and situations are

526

: 00:34:40

going to occur, 50% of customers will

give their bank only two chances to fail

527

: 00:34:47

before considering a change in banks.

528

: 00:34:51

That's somewhat dated as a statistic a

few years ago at least right now, due

529

: 00:34:56

to everything that's been going on in

the world for a number of years, I don't

530

: 00:34:59

have all my money in one institution.

531

: 00:35:01

I don't invest with one institution,

? A number of the institutions

532

: 00:35:05

that I invest with, I've never

had a discussion with a broker,

533

: 00:35:10

I do my trades via Robinhood or other

institutions like that, acorns or Stash.

534

: 00:35:15

So I'm spreading my money around from a

risk avoidance perspective for the very

535

: 00:35:21

reason that, a regulator or bank would

not go with one technology provider.

536

: 00:35:26

And the more that you have correlation

of risk events, , the more that you're

537

: 00:35:31

exposed from a consumer experience

perspective, by being with one player.

538

: 00:35:36

So the traditional icons and titans

of the industry in terms of market

539

: 00:35:40

share, need to be aware of that.

540

: 00:35:42

And that's what's fueling investment

in adoption with fintechs and smaller

541

: 00:35:46

institutions with consumers, and

some of which, have a lower risk

542

: 00:35:51

appetite to get those consumers,

but those risks have not gone away.

543

: 00:35:55

So it's a double-edged sword there.

544

: 00:35:57

And then you see some other

statistics across the top.

545

: 00:36:00

Cybersecurity attacks, 93% still

focus mainly on the finance sector.

546

: 00:36:06

And then just the sheer volume in terms

of performance and capacity degradation.

547

: 00:36:13

Global trading systems

and transaction systems.

548

: 00:36:16

The constant discussions that I get

involved in mainframe is costing

549

: 00:36:21

too much for us, Kyndryl or IBM.

550

: 00:36:24

How do we move to a

distributed environment?

551

: 00:36:26

Can that distributed environment

process like visa, , and what types of

552

: 00:36:30

technologies in terms of containerization

in cloud can compete with a mainframe

553

: 00:36:36

environment in terms of its stability?

554

: 00:36:38

Very complex picture,

very complex problem.

555

: 00:36:41

That tied back to what I said

earlier, there's no silver

556

: 00:36:44

bullet solution especially go.

557

: 00:36:48

And it looks like these type of

statistics and drawing our attention

558

: 00:36:54

to this type of a problem the type

of responses that customers have to

559

: 00:36:58

these type of problems and what people

do as a result of experiencing these

560

: 00:37:04

things are a key part of decision

making in these large institutions.

561

: 00:37:09

From a technology by Location or IBM.

562

: 00:37:12

In solution development it

usually falls into fiefdoms or

563

: 00:37:16

camps in terms of ownership.

564

: 00:37:19

Being a bit colloquial and colorful

and how I'm saying that, I just

565

: 00:37:22

heard a recent saying, if it's

not my pasture, it's not my BS!

566

: 00:37:26

If it's not my operational

platform, if it's not my mainframe,

567

: 00:37:31

it's X 86 or somewhere else.

568

: 00:37:33

It's not my database, it's not

my P&L it's not my problem.

569

: 00:37:38

So even within each of these pillars,

you've got silos, ? And all it takes

570

: 00:37:43

is two or three correlated gray swans,

some morphin to a once in a hundred

571

: 00:37:48

year black swan event that blows the

walls of those silos completely down.

572

: 00:37:53

Very interesting.

573

: 00:37:54

Thank you.

574

: 00:37:54

Thank you Bill for that.

575

: 00:37:56

So what are we what are we looking at

in, in some of these textual things?

576

: 00:38:01

I know that there's probably some key

components that you can talk to or

577

: 00:38:06

bullet points that you can talk to

about some of these materials that

578

: 00:38:09

you have created for your customers

that are using your advisory services.

579

: 00:38:16

What kind of things are you helping

industry trends and state of the industry

580

: 00:38:22

are you helping people understand so that

they can make the better decisions within

581

: 00:38:27

the environment that we're in today?

582

: 00:38:29

Very good point.

583

: 00:38:30

And we're always we've always been

a in a catch up mentality or mindset

584

: 00:38:35

as humans, regardless of what

country we're in, especially from a

585

: 00:38:39

reactionary regulatory perspective.

586

: 00:38:42

So if it's not broke, don't fix

it or how it was broke the past

587

: 00:38:46

becomes the road for the future.

588

: 00:38:48

What if we haven't encountered

new ways that things have gotten

589

: 00:38:51

broke or how they're even measured.

590

: 00:38:54

So in terms of, reserves for risk

protection and banks, and determining

591

: 00:39:02

its level of risk in terms of

society, traditionally all along

592

: 00:39:08

it's been the size of the bank is

based on, its in terms of assets.

593

: 00:39:14

So here is a little bit more about

Nick and the writing Cyber policies.

594

: 00:39:20

He knows that every organization

has to have these policies, and a

595

: 00:39:25

lot of times they're, they drone

on and they're not very relevant.

596

: 00:39:29

He's gonna help you figure out how

to make them relevant, effective and

597

: 00:39:34

something that everyone can live within.

598

: 00:39:36

So you'll enjoy hearing from from

Nick Leghorn on this particular topic.

599

: 00:39:42

So here we go.

600

: 00:39:43

We're gonna go talk about Nick.

601

: 00:39:46

This is an example of the information

security policy for the University

602

: 00:39:50

of California which is pretty

indicative of normally how they

603

: 00:39:53

look and how they come together.

604

: 00:39:55

Like it's a giant document.

605

: 00:39:57

There's a bunch of sections in it.

606

: 00:39:59

It's got all these different

components to it, like it is a

607

: 00:40:02

hot mess of a of a big document.

608

: 00:40:04

And generally makes it unreadable

and your eyes glaze over after

609

: 00:40:08

about the first two minutes, right?

610

: 00:40:10

And it's useful for some cases, like

it's useful to get the information

611

: 00:40:16

out there, but it's not useful for

actually getting people to follow it.

612

: 00:40:19

And that's not unique to the

University of California.

613

: 00:40:22

A little organization called The New

York Times did a investigation into

614

: 00:40:26

a bunch of privacy policies that

exist around the internet including

615

: 00:40:29

their own and figuring out like how

readable and comprehensible are they.

616

: 00:40:33

And it turns out that they're.

617

: 00:40:35

Pretty bad.

618

: 00:40:36

Like the majority of information security

policies and privacy policies and other

619

: 00:40:40

stuff are pretty much incomprehensible.

620

: 00:40:42

They're massive.

621

: 00:40:44

It takes a long time to read them over.

622

: 00:40:46

You need more than a college degree in

order to understand what's going on.

623

: 00:40:50

It is miserable to try and

understand what's going on in.

624

: 00:40:53

And that's a a situation that isn't

unique to the privacy policies.

625

: 00:40:58

That's also the way that we

write every other policy we

626

: 00:41:00

do at our companies, right?

627

: 00:41:03

Typically the reason why there are

just incomprehensible mass is because

628

: 00:41:09

we're focusing on the drivers that

we see commonly for InfoSec policy.

629

: 00:41:16

And that really boils down to the

lawyers, so legal obligations.

630

: 00:41:21

Compliance people meeting HIPAA, PCI,

GDPR, CCPA, SOC2, like all the word

631

: 00:41:27

salad stuff that you gotta get done.

632

: 00:41:29

And then HR wants some cover for

being able to terminate people for

633

: 00:41:33

if they do terrible things at work.

634

: 00:41:34

Those are typically the drivers

behind policies and the real

635

: 00:41:37

force behind how they look and

why they look a certain way and.

636

: 00:41:42

By nature, the policies reflect

the audience because these are

637

: 00:41:45

the drivers for for your policies.

638

: 00:41:48

So how much is it holding?

639

: 00:41:50

But that necessarily doesn't get

into how interconnected it is on a

640

: 00:41:56

world basis with other ecosystems.

641

: 00:42:00

So that needs to change to

reflect where we are going in

642

: 00:42:04

terms of multi factorization.

643

: 00:42:06

And assessing and

preparing for risk events.

644

: 00:42:09

So an alternative approach,

645

: 00:42:11

. In global regulators such as the Basel

committee are looking to tweak this

646

: 00:42:15

further, are carrying it past size of the

bank institution and asset holdings alone.

647

: 00:42:22

So how interconnected is it?

648

: 00:42:24

There's a great chart, another chart

I have that shows all of the cloud

649

: 00:42:28

providers working with the major banks

and how interconnected this landscape is.

650

: 00:42:33

Between AWS, Azure, G C P.

651

: 00:42:39

and the foreign bank providers in

China, the foreign cloud providers.

652

: 00:42:43

And when you look at the

interconnectedness picture there, from

653

: 00:42:46

a potential risk issue perspective,

and if one, one piece goes down, how

654

: 00:42:52

it affects everybody that's connected.

655

: 00:42:55

That's critical.

656

: 00:42:56

So it's no longer independently how

much money each of those is holding.

657

: 00:43:00

Then you get into other factors.

658

: 00:43:01

Of suitability.

659

: 00:43:02

This is component, component failure,

but on a more macro sense, how

660

: 00:43:06

can things be swapped in and out?

661

: 00:43:08

So the ecosystem keeps going.

662

: 00:43:10

How complex is the interconnectedness?

663

: 00:43:13

Is there cross jurisdictional activity?

664

: 00:43:15

So it seems like Bill what you're

talking about and trying to help

665

: 00:43:19

us all understand is that there's

a high degree of dependence.

666

: 00:43:23

And what I just heard

you say is not only on.

667

: 00:43:26

A particular mainframe technology or

a particular cloud technology or a

668

: 00:43:32

brand of cloud, but there are national

inst ances of cloud capabilities that

669

: 00:43:40

are not even AWS's Azure or Google.

670

: 00:43:44

They're another localities

type of cloud or different

671

: 00:43:49

institutions or nation state cloud.

672

: 00:43:51

My, my company works in

multi-cloud management.

673

: 00:43:54

We recognize this issue.

674

: 00:43:56

Many of our c lients are not in one

country in terms of data centers.

675

: 00:44:01

And based on that, whoever they're working

with in terms of a cloud provider in

676

: 00:44:05

one country, they may not be able to, in

another country, the world where they're.

677

: 00:44:10

So how do from a risk management

perspective, get your arms and head around

678

: 00:44:14

that to see exactly what's going on?

679

: 00:44:17

The dependency matrix must be absolutely,

incredibly complex to consider.

680

: 00:44:23

Yeah.

681

: 00:44:23

And is that something that you help

people with, is to look at that complexity

682

: 00:44:28

and those dependencies and factor

that into the way that they have to

683

: 00:44:32

respond and what they have to manage?

684

: 00:44:35

Absolutely.

685

: 00:44:36

We have a number of

solutions and capabilities.

686

: 00:44:38

There's one we launched this year

called Kyndryl Bridge for IT operations

687

: 00:44:43

and multi-cloud management that we're

using internally in our managed service

688

: 00:44:47

context, but it's completely open in

terms of the architecture to work with

689

: 00:44:52

multi-cloud vendors for this purpose.

690

: 00:44:54

So we can gather that information

and provide better visibility into

691

: 00:45:00

a complete estate for those reasons.

692

: 00:45:02

Absolutely.

693

: 00:45:02

Getting into a bit of I'm showing a chart

now with, some terminology again in the

694

: 00:45:07

past, and based on how we approach things,

myopically, once in a hundred year storms.

695

: 00:45:13

The financial crisis of 2008.

696

: 00:45:15

But what happens if you have

Covid and then a major regional

697

: 00:45:18

war like Russia and Ukraine?

698

: 00:45:20

And the impact on commodity

and energy markets.

699

: 00:45:25

And supply chains that were

already crippled by Covid

700

: 00:45:28

that are even more so now.

701

: 00:45:29

With the regional war

in Russia and Ukraine.

702

: 00:45:32

So these are independent black swans that

are of morphing into they could be the

703

: 00:45:39

next generation black swan, but they're

gray swans right now that are boiling up.

704

: 00:45:43

And I'll explain more on what I

mean on that, on the next chart.

705

: 00:45:46

But there's also new types of black swans.

706

: 00:45:49

As digital transformation, decentralized

finance, the fueling of the everyday

707

: 00:45:56

layman getting into investing in trading

from the palm of his hand without

708

: 00:46:00

getting professional investor guidance.

709

: 00:46:04

So there's a great movie everybody should

go take a look at when you have time, if

710

: 00:46:07

you haven't seen it called Money Monster.

711

: 00:46:09

And this is all about hedge funds

Jerry rigging outcomes and commodity

712

: 00:46:16

markets to get a short position

and it benefits the hedge fund.

713

: 00:46:21

But everybody else that's invested

in that, and everybody that's

714

: 00:46:25

participating in those shells and

concentric circles in that hedge fund

715

: 00:46:30

gets screwed and lose everything.

716

: 00:46:32

What is there out there that

prevents something like that.

717

: 00:46:35

FTX is an example, in the crypto space.

718

: 00:46:39

That's what I was just gonna, I was just

gonna ask, talk about a current event.

719

: 00:46:43

We're talking exactly about that type

of occurrence and we've seen it in

720

: 00:46:49

the past with some of the other big

organizations that have been subject

721

: 00:46:54

to various scams both small and large.

722

: 00:46:58

How.

723

: 00:47:00

How does blockchain enter into

this equation to some degree?

724

: 00:47:05

What are you looking at solutions that

use blockchain technology and I'm making

725

: 00:47:12

the assumption that we can use blockchain

technology for more than simply crypto.

726

: 00:47:18

Yeah.

727

: 00:47:18

So a number of points before I get into

that a bit further, but that's part of

728

: 00:47:22

general trend in technology overall.

729

: 00:47:24

That the technologies are not

legacy technologies anymore born

730

: 00:47:29

and bred and developed in the

enterprise of the institution.

731

: 00:47:33

As the economy has moved more towards

a consumer economy out of necessity.

732

: 00:47:39

Less to a market economy in terms

of technology, people transacting

733

: 00:47:44

from their mobile devices.

734

: 00:47:45

Using digital wallets that

are supported by blockchain.

735

: 00:47:50

In a decentralized sense,

public blockchains Ethereum

736

: 00:47:53

outside the enterprise walls.

737

: 00:47:56

To move money around payment

transfers, remittances.

738

: 00:48:00

Via the blockchain Bitcoin transfer.

739

: 00:48:02

You can pay and PayPal

in cryptocurrency now.

740

: 00:48:05

As an example.

741

: 00:48:07

So a lot of disruption going on

there from a consumption base.

742

: 00:48:11

That, that is definitely occurring.

743

: 00:48:13

Let me stop sharing for a minute here.

744

: 00:48:15

Go back to the Sure.

745

: 00:48:18

While you're getting set up there, I'll

just talk a little bit about the fact that

746

: 00:48:23

our world is moving at a very rapid rate.

747

: 00:48:26

With technology and what you talked

about it with digital transformation and

748

: 00:48:32

disaster recovery because of covid is

having to change and transform along with

749

: 00:48:39

all the various digital transformations.

750

: 00:48:42

And it seems that I hear people talking

about digital or digital transformation

751

: 00:48:47

and a disaster recovery, having to

keep up with those type of things and.

752

: 00:48:54

Make sure that their plans are

still relevant and meet the needs

753

: 00:49:00

of the new digital transformation.

754

: 00:49:03

And then you talked about

the nationalization, or the

755

: 00:49:07

internationalization or the globalization

of all of these things for so many of

756

: 00:49:12

the organizations that you are serving.

757

: 00:49:15

And of course, blockchain is a big issue

as well as some of these other things.

758

: 00:49:20

And so if you wanna queue up the

next slide there, you go ahead

759

: 00:49:25

and I'll bring it on board for us.

760

: 00:49:27

Yeah.

761

: 00:49:27

So the a as you bring up that point,

the individual institutions from

762

: 00:49:33

a regular regulator perspective,

and what I'm specifically talking

763

: 00:49:37

about are the central banks.

764

: 00:49:39

? They're the ones in each country

and each of them across the world.

765

: 00:49:43

This chart is showing.

766

: 00:49:46

supervisory agencies that have

the capability holistically to

767

: 00:49:51

look at black and gray swan events

and what needs to be in place to

768

: 00:49:57

remediate in a preventive sense, any

bad things from really happening.

769

: 00:50:03

Now, across the top you may

recognize some of those acronyms.

770

: 00:50:07

SEC obviously is the United States

Security Exchange Commission.

771

: 00:50:11

Monetary Authority Society

of Singapore d n b.

772

: 00:50:14

, so these are all big institutions.

773

: 00:50:17

Bank of India, Boi, in powerhouse

countries that are the policemen

774

: 00:50:24

of the banking institutions.

775

: 00:50:26

? What you see here is on

the left side, supervisory.

776

: 00:50:31

And then you see color coding in

terms of if there's a solution in

777

: 00:50:34

development and experimental stage in

development or operational production.

778

: 00:50:41

Now this came from a report from the

Central Bank of Central Banks, the BIS.

779

: 00:50:47

Bank of International Settlement.

780

: 00:50:48

So that's the police dog watch

organization over the central banks.

781

: 00:50:54

And they took a look at

this a number of years ago.

782

: 00:50:57

There's white space all over this.

783

: 00:50:59

First of all, as we're moving

into blockchain, decentralized

784

: 00:51:04

finance, emerging tech, these

supervisory areas are not silos.

785

: 00:51:10

They're more and more correlated,

especially with interconnectivity

786

: 00:51:14

with big tech in the cloud providers.

787

: 00:51:16

You'll see.

788

: 00:51:18

That even if you pick one of

the supervisory areas and go

789

: 00:51:23

horizontally across the chart,

not one is fully operational for

790

: 00:51:28

all of the big central banks.

791

: 00:51:31

That's illuminating

792

: 00:51:33

Number two.

793

: 00:51:34

Going vertically down.

794

: 00:51:36

From a comprehensive, holistic correlated

perspective, from a supervisory risk

795

: 00:51:42

perspective for remediation, nobody's

got 'em fully operational either.

796

: 00:51:48

So those are the ones that are all

yellow in that second line there

797

: 00:51:52

where the realtime monitoring

and all the various efforts, and

798

: 00:51:57

some of 'em are not even yellow.

799

: 00:51:59

They're blank.

800

: 00:52:00

Yeah.

801

: 00:52:00

What does that say?

802

: 00:52:02

Haven't started.

803

: 00:52:03

So this is largely reflecting

too, if you trace back.

804

: 00:52:07

So like I said, this is a bit dated.

805

: 00:52:09

It it's at least three, four years old,

so there could be some improvements

806

: 00:52:13

on this, but for the most part

it, the picture is still the same.

807

: 00:52:17

But if you trace back in terms

of the big black swan events,

808

: 00:52:21

and I mentioned some of them,

809

: 00:52:23

. In the past 10, 15 years, you'll

see that the color codes that are

810

: 00:52:29

operational that are put in place,

especially in certain jurisdictions.

811

: 00:52:35

Those are the red ones, I presume.

812

: 00:52:38

Yeah.

813

: 00:52:38

You'll see that those reflect

a reaction to the major event.

814

: 00:52:43

There's the pattern . Got it.

815

: 00:52:46

So if you went, so if you went back

and looked at a timeline of this, , you

816

: 00:52:51

would basic a chronological timeline

of when these things started to

817

: 00:52:56

appear to be regulating or to work.

818

: 00:53:01

They would correspond to, for

instance, some of the big world events

819

: 00:53:06

of 2008 and other such situations.

820

: 00:53:10

And that they didn't really do that

for that kind of correlating regulation

821

: 00:53:15

until after those type of events.

822

: 00:53:17

Is that what you're saying?

823

: 00:53:18

Yeah.

824

: 00:53:20

And there's been some intentions,

for example, to modernize this view

825

: 00:53:25

based on how the world is changing.

826

: 00:53:26

So the best example I think I have

on this chart is macro financial

827

: 00:53:30

risks and emerging risk signaling.

828

: 00:53:33

So some of the major players of

joining forces and saying, look,

829

: 00:53:37

we need to be more holistic on an

international basis, on a, how we're

830

: 00:53:41

all interconnected on a macro level.

831

: 00:53:43

And start developing solutions in that

context instead of our own sandboxes.

832

: 00:53:51

So basically at this point, any of those

areas that are not yet operational are

833

: 00:53:57

pretty much wide open to black swan

events and to all sorts of disruption in

834

: 00:54:03

international commerce and banking that

might Absolutely, Bill, let me just throw

835

: 00:54:11

a complete wild card scenario out at you.

836

: 00:54:14

Everybody right now, in the past 60

days or so, maybe a bit longer is all

837

: 00:54:20

excited about this open AI chat G P T

initiative that was fueled by Microsoft

838

: 00:54:30

and natural language processing.

839

: 00:54:31

I've read now that and what it can do.

840

: 00:54:33

So you can ask it a question.

841

: 00:54:35

and the response accuracy it comes

back with is phenomenal in many cases.

842

: 00:54:40

In terms of how fast.

843

: 00:54:41

So let me add some almost

instantaneous feedback on that.

844

: 00:54:46

, over the weekend I was on chat and I was

exercising the system and I'm a computer

845

: 00:54:54

network analyst and so I asked it how

to write a computer network analyzer

846

: 00:55:00

called Wire Shark, how to put in a

filter for a certain TCP application

847

: 00:55:07

port and if it would write that for me.

848

: 00:55:09

And sure enough it came right

back and it delivered the exact

849

: 00:55:14

appropriate syntax for that.

850

: 00:55:17

And then I started asking it some

questions about disaster recovery

851

: 00:55:21

plans or disaster recovery surveys,

and it was amazingly accurate.

852

: 00:55:28

Now it's not going to do a comprehensive.

853

: 00:55:33

Amount of work for us, but in a small

amount of work that you ask it to

854

: 00:55:39

do, write me a disaster recovery plan

for the major risks in Austin, Texas.

855

: 00:55:49

I actually asked it that, and it came

back and it told me the natural disaster

856

: 00:55:55

type specific problems that Austin

would have that others wouldn't have.

857

: 00:56:00

So it's an incredibly accurate, albeit

very specific and to compare the chat

858

: 00:56:09

AI with something like Google Google

makes money when there's clicks.

859

: 00:56:14

So when you stop clicking,

Google stops making money.

860

: 00:56:17

Yeah.

861

: 00:56:17

So Google makes us click.

862

: 00:56:19

This technology, you ask it a question and

it gives you the exact specific answer.

863

: 00:56:26

With context that you're looking

for in almost pretty much every

864

: 00:56:31

area of technology or information

that I could quiz it with.

865

: 00:56:38

Amazing.

866

: 00:56:39

But that's based on a certain

context, like you said in dataset

867

: 00:56:43

that it's available and has access to.

868

: 00:56:45

And it's not gonna cover the unknowns.

869

: 00:56:49

How can it, what I'm saying is there is an

inherent risk there as it evolves further.

870

: 00:56:55

And how do we know that what

it provides as an answer

871

: 00:56:58

. Is the best solution in, in

terms of its input and output.

872

: 00:57:02

It's still binary to some degree, right?

873

: 00:57:06

Oh, absolutely.

874

: 00:57:07

It's very specific and it's also it warns

you that it's not accurate in all cases.

875

: 00:57:14

And it also says I haven't

really been taught anything

876

: 00:57:17

prior to 2001 or after 2001.

877

: 00:57:22

So my, it gives you these

pieces of information.

878

: 00:57:25

The other interesting anecdote

on this is that it remembers

879

: 00:57:29

everything that you asked.

880

: 00:57:30

So I asked it to create a Cisco router

configuration for five VLANs, and

881

: 00:57:37

then I asked it a second question,

which is a very technical thing.

882

: 00:57:40

It drew out and it gave me the exact

syntax for that Cisco router config.

883

: 00:57:47

And then I said, now put Ether channel

connections between the various.

884

: 00:57:52

Switches and it gave me the exact Cisco

syntax to do all of those sort of things.

885

: 00:57:59

So it's actually quite capable, but it,

like you said, it, it doesn't know or

886

: 00:58:04

understand or anticipate other things.

887

: 00:58:09

So it's very good for very specific

tasks with very specific outputs.

888

: 00:58:15

But like you said, it does not

know the future, but it is pretty

889

: 00:58:19

amazing to, to utilize the tool

and to get some experience with it.

890

: 00:58:24

I just did that this morning

and over the weekend.

891

: 00:58:27

So I wanted to let you know that

is something that's happening

892

: 00:58:30

today, although I could not imagine

anybody depending upon that for any

893

: 00:58:36

type of mission critical system.

894

: 00:58:40

Yeah I bring this up as an example

because there's the row in this

895

: 00:58:45

chart here, and then I'll stop

sharing machine readable regulations.

896

: 00:58:50

In this context, I can see how

that type of solution may want to

897

: 00:58:55

fill in some of that white space.

898

: 00:58:57

But it's still in its current way,

shape, and form is open to bias

899

: 00:59:02

in a limited dataset perspective.

900

: 00:59:04

So there, there's still some

inherent risk in that solution.

901

: 00:59:09

It's just very interesting as AI

develops and takes a life on its own.

902

: 00:59:14

I You've read about, AI being able

to program itself or code itself.

903

: 00:59:19

There's gotta be a base starting point.

904

: 00:59:22

For all of that.

905

: 00:59:23

So yes.

906

: 00:59:25

Now as we start to wind down, I'm

wondering if you could summarize for

907

: 00:59:32

us some of the lessons that we have

learned in today's session and prepare

908

: 00:59:38

us for some future sessions that we

might do on some of this very complex

909

: 00:59:44

global dependencies on technology.

910

: 00:59:47

And as digital transformation takes us

forward, what are some of the lessons

911

: 00:59:52

learned, you think that we have gained

and what things like you just mentioned,

912

: 00:59:57

do we need to take care of in the.

913

: 01:00:00

Yeah.

914

: 01:00:00

It goes back to basic hygiene

and, should be baked into dna.

915

: 01:00:04

So make your core solid number one.

916

: 01:00:06

If you look at the picture of the

concentric circles when you cut

917

: 01:00:10

down a tree, ? And innermost circle

is the oldest part of the tree as

918

: 01:00:15

it, and then it, grows outward.

919

: 01:00:17

You get newer pieces of the bark and

the layers added into the tree trunk.

920

: 01:00:21

That's the liquid ecosystem, ? As

you're growing that tree outwards and

921

: 01:00:26

you add more circles around it, ? And

the way that I visualize that from

922

: 01:00:30

an analogy perspective, that's more

disruption to your core business.

923

: 01:00:35

? So you've gotta make sure you've got a

solid core, ? And, in terms of frameworks

924

: 01:00:39

and methodologies that have further

evolved, zero trust architecture,

925

: 01:00:44

? Not only protecting you from.

926

: 01:00:46

outside in threats,

but inside out threats.

927

: 01:00:49

Looking at your application

and service estate.

928

: 01:00:52

Not just applications, because

it's not applications anymore,

929

: 01:00:55

serving one function or one service.

930

: 01:00:58

There's interconnectedness.

931

: 01:01:00

So do you have a proper view and

inventory from a categorization in

932

: 01:01:07

terms of criticality perspective

along confidentiality, data

933

: 01:01:12

integrity and availability.

934

: 01:01:15

So for example, a, B, C, 1, 2, 3.

935

: 01:01:19

So if an application or more

appropriately a function or service

936

: 01:01:23

is rated one for confidentiality,

integrity, and availability, that

937

: 01:01:29

is lifeblood to the enterprise.

938

: 01:01:31

That's like oxygen, right?

939

: 01:01:33

You can't live without it.

940

: 01:01:35

So that means from an outside in

perspective, strengthening the core,

941

: 01:01:41

you need high availability six,

nine s and full DR for that service.

942

: 01:01:45

? And, most institutions don't

even do what I just described.

943

: 01:01:50

And as you go further out from that

core and add those additional concentric

944

: 01:01:54

rings, there's gonna be different

permutations of the 1 23 ratings.

945

: 01:01:59

You could get into 1.5,

946

: 01:02:01

2.5,

947

: 01:02:02

and don't you see don't, doesn't it

seem like we are also transforming

948

: 01:02:07

some of our technologies?

949

: 01:02:09

We're still moving additional

resources and systems to the cloud.

950

: 01:02:14

Those cloud systems are still

moving into microservices.

951

: 01:02:18

They're moving into various containers.

952

: 01:02:21

That, again, shift the paradigm in being

able to build those systems up and scale

953

: 01:02:28

them rapidly, but at the same time, it

increases the complexity and changes

954

: 01:02:33

the technology in those like you said,

it beautifully said is the concentric

955

: 01:02:38

rings on the outside that continue

to affect even though we have a good

956

: 01:02:43

core, we're still changing as we grow.

957

: 01:02:48

Yeah.

958

: 01:02:49

And to, to some extent, it goes

back to the piece of slogan

959

: 01:02:54

that I mentioned earlier.

960

: 01:02:55

It's not my pasture in the core ring.

961

: 01:02:58

It's not my bs I'm shifting it off to

a container to, to an outside ring, but

962

: 01:03:04

that doesn't necessarily reduce the risk.

963

: 01:03:07

Exactly.

964

: 01:03:08

And then disaster recovery.

965

: 01:03:10

In some of the things that you

discuss with your customers are

966

: 01:03:14

you finding that the ability to have

disaster recovery is more synchronous

967

: 01:03:21

obviously than it is asynchronous?

968

: 01:03:23

In other words, it has to people cannot

tolerate any downtime or minimal downtime.

969

: 01:03:31

And do organizations who spend a lot

of energy, money, and budget building

970

: 01:03:37

out realtime capabilities, do they also

sit back and look at the potential for

971

: 01:03:45

catastrophic situations where they have

to accept that they may have to triage

972

: 01:03:51

for something that's unforeseeable?

973

: 01:03:56

My experience, it's usually been

reactionary, so there's been some type

974

: 01:04:00

of incident, there's been some type

of monetary loss, reputational loss.

975

: 01:04:05

And, the amount of focus that they go back

and look at this varies depending in some

976

: 01:04:13

correlated sense to what that loss was.

977

: 01:04:15

And it really depends.

978

: 01:04:17

Some of them still treat

it like a speed bump.

979

: 01:04:20

It was an annoyance.

980

: 01:04:22

We learn from it, they look at it

in context to the same or similar

981

: 01:04:25

things happening to their competitors

982

: 01:04:28

and they more or less chalk it

up to the cost of doing business.

983

: 01:04:32

And I don't necessarily agree

with that, especially with that

984

: 01:04:35

statistic I showed earlier.

985

: 01:04:36

50% of customers are gonna give the

bank two chances to get it right.

986

: 01:04:40

You can get it right now.

987

: 01:04:41

A hundred percent.

988

: 01:04:42

I'm still taking my money elsewhere

because in terms of consumer

989

: 01:04:45

experience, I won't name my bank,

but I used to work for them.

990

: 01:04:50

I've been in and out of them for

projects through two companies, and

991

: 01:04:54

I'm still with 'em over 20 years.

992

: 01:04:57

Do you think they send me unsolicited

offers to make my life better?

993

: 01:05:01

Hell no.

994

: 01:05:03

Meanwhile, I, meanwhile, I've

been with PayPal for two years,

995

: 01:05:07

two and a half, three years.

996

: 01:05:09

I've got credit lines with them.

997

: 01:05:10

I can buy crypto with

them every six months.

998

: 01:05:12

They're offering me some new

way to improve my financial

999

: 01:05:15

situation, unsolicited.

: 1000

01:05:17,956 --> 01:05:21,629

And that's all it took for me to

move some of my business to PayPal.

: 1001

01:05:22,229 --> 01:05:22,629

Yes.

: 1002

01:05:22,644 --> 01:05:26,958

So it's interesting because today and

I know we've been talking about large

: 1003

01:05:26,958 --> 01:05:32,459

corporate banking global and other such

things, but have you take into account.

: 1004

01:05:32,471 --> 01:05:37,532

Companies that are utilizing things of

like Facebook for all their marketing.

: 1005

01:05:37,592 --> 01:05:43,733

They get all of their sales from Facebook

marketing large companies who are

: 1006

01:05:44,003 --> 01:05:47,032

utilizing that for all their new business.

: 1007

01:05:47,332 --> 01:05:52,642

th,: 2021

went down for six straight hours

: 1008

01:05:52,706 --> 01:05:56,968

catastrophic outage, a black

swan, a zero day, so to speak.

: 1009

01:05:57,388 --> 01:06:01,266

And, it's not so easy to just

say, , I'm moving all my marketing

: 1010

01:06:01,271 --> 01:06:07,608

for to my Twitter right folks, or I'm

moving everything over to some other

: 1011

01:06:08,118 --> 01:06:09,778

LinkedIn or something of that nature.

: 1012

01:06:09,778 --> 01:06:12,630

It that, that requires not just.

: 1013

01:06:12,657 --> 01:06:17,606

A disaster recovery capability, but

something that has to be baked in for,

: 1014

01:06:17,643 --> 01:06:23,133

many years to move all your marketing from

one social media platform to the other.

: 1015

01:06:23,188 --> 01:06:27,126

And of course the lessons learned

that Facebook had that, that cost

: 1016

01:06:27,126 --> 01:06:31,326

them 25 to 50 Billion in that one day.

: 1017

01:06:31,326 --> 01:06:35,646

And of course it went, this talk went

back up, but somebody on that day

: 1018

01:06:36,096 --> 01:06:41,486

lost between 25 and 50 Billion of

value and may have made decisions.

: 1019

01:06:41,491 --> 01:06:44,538

Like you said, banks are not

very, forgiving of customers

: 1020

01:06:44,544 --> 01:06:45,799

are not very forgiving.

: 1021

01:06:45,866 --> 01:06:49,049

They see this happen once and

they say it might happen again.

: 1022

01:06:49,049 --> 01:06:52,349

They'll give you that one, but they

probably wouldn't give you a second one.

: 1023

01:06:52,408 --> 01:06:57,849

Is there any allegory to the banking

world and, revenue production or

: 1024

01:06:57,849 --> 01:07:02,619

nonstop systems that we can take

away from that type of an event?

: 1025

01:07:04,350 --> 01:07:08,176

. The disruption of the traditional

industry, the traditional

: 1026

01:07:08,176 --> 01:07:11,566

bank has not moved other to

other providers that quickly.

: 1027

01:07:11,686 --> 01:07:15,856

For those reasons, they just

don't have the industrial strength

: 1028

01:07:15,861 --> 01:07:20,326

capability in context with the

volume that they need to protect yet.

: 1029

01:07:21,176 --> 01:07:23,576

And it's the not, it's

not the same type of data.

: 1030

01:07:25,091 --> 01:07:25,541

As well.

: 1031

01:07:25,541 --> 01:07:26,467

Which we all know.

: 1032

01:07:26,467 --> 01:07:29,303

Even like your example using PayPal.

: 1033

01:07:29,693 --> 01:07:29,783

Yeah.

: 1034

01:07:29,783 --> 01:07:36,741

You can't move between Stripe and PayPal

and banking, traditional banking . Very

: 1035

01:07:36,741 --> 01:07:41,621

rapidly, probably more rapidly than you

could move from Facebook to Twitter.

: 1036

01:07:41,621 --> 01:07:47,640

But nevertheless it's a macroeconomic

change that and it's hierarchical based

: 1037

01:07:47,640 --> 01:07:49,890

or tiered based in terms of risk appetite.

: 1038

01:07:49,890 --> 01:07:54,174

So the consumption model that I look

at and use and present, in, in terms of

: 1039

01:07:54,174 --> 01:07:59,034

the bastion of what's being disrupted,

payments has already left the building.

: 1040

01:07:59,214 --> 01:08:01,494

That's fair Game to a number of providers.

: 1041

01:08:01,494 --> 01:08:02,884

The bank doesn't own that anymore.

: 1042

01:08:02,884 --> 01:08:04,818

Anybody can do payments.

: 1043

01:08:05,593 --> 01:08:05,958

these days.

: 1044

01:08:05,958 --> 01:08:10,295

That's why Facebook and Apple and Google,

they've all gotten into this space.

: 1045

01:08:10,356 --> 01:08:13,667

When you the next piece is

really lending and credit.

: 1046

01:08:13,667 --> 01:08:16,846

And some of these other

alternative providers have

: 1047

01:08:16,846 --> 01:08:18,767

moved into that PayPal credit.

: 1048

01:08:19,057 --> 01:08:20,857

So they know what you're

spending your money on.

: 1049

01:08:21,067 --> 01:08:24,817

It's a natural extension to offer

you credit vehicles in financing.

: 1050

01:08:24,888 --> 01:08:26,448

Not much more risky.

: 1051

01:08:26,497 --> 01:08:28,444

There's a credit risk scoring algorithm.

: 1052

01:08:28,457 --> 01:08:30,947

You've gotta have reserves

in place to protect.

: 1053

01:08:31,407 --> 01:08:34,667

But there's all kinds of buy

now pay later schemes as well.

: 1054

01:08:34,977 --> 01:08:35,176

Yeah.

: 1055

01:08:35,567 --> 01:08:37,410

So that's being disrupted.

: 1056

01:08:37,429 --> 01:08:42,269

The key part that's staying away

from the disruptors has been really

: 1057

01:08:42,269 --> 01:08:45,629

asset preservation i e deposits.

: 1058

01:08:46,089 --> 01:08:47,229

To a certain extent.

: 1059

01:08:47,259 --> 01:08:49,029

And where there's more regulation.

: 1060

01:08:49,779 --> 01:08:53,578

You need a more intense banking

charter to hold wallet share

: 1061

01:08:53,578 --> 01:08:55,709

of a customer in your system.

: 1062

01:08:56,368 --> 01:08:56,849

Got it.

: 1063

01:08:57,429 --> 01:08:59,548

And same with investments or insurance.

: 1064

01:09:00,457 --> 01:09:04,658

Because if there's a, if there's a total

disaster or loss and you're holding

: 1065

01:09:04,658 --> 01:09:09,548

people's money when you're promising

some type of return, or you're ensuring

: 1066

01:09:09,548 --> 01:09:15,488

it, if they lose it, then your risk

quotient is much higher than if you're

: 1067

01:09:15,493 --> 01:09:19,264

offering 'em the credit or just processing

payments from point A to point B.

: 1068

01:09:19,304 --> 01:09:19,514

Yeah.

: 1069

01:09:19,904 --> 01:09:20,203

Yeah.

: 1070

01:09:20,252 --> 01:09:20,702

Good point.

: 1071

01:09:21,122 --> 01:09:21,211

Yes.

: 1072

01:09:21,211 --> 01:09:26,234

So in closing I wanna give you the last

word and let you just talk to our audience

: 1073

01:09:26,313 --> 01:09:31,077

and discuss some of these lessons learned

and where you think things are going and

: 1074

01:09:31,077 --> 01:09:36,344

how you and your organization might be

able to help people that are struggling

: 1075

01:09:36,344 --> 01:09:38,113

with these exact type of issues.

: 1076

01:09:38,948 --> 01:09:38,950

Yeah.

: 1077

01:09:39,042 --> 01:09:41,743

Number one, this is a

holistic perspective.

: 1078

01:09:41,743 --> 01:09:45,582

And it's number of analogies I

used in terms of peeling back

: 1079

01:09:45,582 --> 01:09:49,813

the layers of the onion or the

concentric trees in the circle.

: 1080

01:09:49,813 --> 01:09:53,157

And the other point is it's people

processing technology not to use,

: 1081

01:09:53,197 --> 01:09:57,513

a common term that's been bandied

about for decades, but it still

: 1082

01:09:57,513 --> 01:09:59,283

is definitely all about that.

: 1083

01:09:59,923 --> 01:10:02,683

Digital transformation

is not a technology play.

: 1084

01:10:03,193 --> 01:10:05,452

Only it covers your organization.

: 1085

01:10:06,272 --> 01:10:10,426

It covers how you're interacting with

your target customer and who that

: 1086

01:10:10,426 --> 01:10:12,736

really is to improve their experience.

: 1087

01:10:13,346 --> 01:10:16,196

Whether you're B2C, B2B, or B2B to C.

: 1088

01:10:16,207 --> 01:10:20,686

And we primarily plug ourselves in

terms of B2B and to a second extent

: 1089

01:10:20,691 --> 01:10:23,626

B2B, B2C context to help clients.

: 1090

01:10:24,106 --> 01:10:27,466

But our approach is really digital

transformation, not only from a

: 1091

01:10:27,471 --> 01:10:32,776

technology perspective, but business

strategy enabled by technology as well.

: 1092

01:10:32,791 --> 01:10:33,322

Very good.

: 1093

01:10:33,322 --> 01:10:34,222

Thank you so much.

: 1094

01:10:34,222 --> 01:10:40,417

We've been talking with Bill Genovese

and he is the CIO advisory partner

: 1095

01:10:40,687 --> 01:10:44,155

and CTO of technology strategy.

: 1096

01:10:44,155 --> 01:10:50,559

Kyndryl, a former IBM

technical services company.

: 1097

01:10:50,860 --> 01:10:54,610

So I just want to say thank you so

much, Bill, for joining us today.

: 1098

01:10:54,615 --> 01:10:58,960

We look forward to having you

on a future broadcast, and thank

: 1099

01:10:58,965 --> 01:11:00,730

you so much for joining us.

: 1100

01:11:01,059 --> 01:11:04,256

And folks, if you want to get in

contact with Bill, we'll give you

: 1101

01:11:04,256 --> 01:11:10,912

his contact information in the down,

in the show notes so that you can

: 1102

01:11:10,912 --> 01:11:16,740

contact Bill or ask him for some type

of a presentation to talk about your

: 1103

01:11:16,740 --> 01:11:19,650

particular issues in your environment.

: 1104

01:11:20,250 --> 01:11:22,080

So now thank you Bill.

: 1105

01:11:22,110 --> 01:11:25,230

Really been a pleasure to talk

with you and to get to know you.

: 1106

01:11:25,230 --> 01:11:31,050

Look forward to additional times

on disaster stream, disaster

: 1107

01:11:31,540 --> 01:11:33,210

recovery responder stories.

: 1108

01:11:33,510 --> 01:11:34,140

Thank you.

: 1109

01:11:34,140 --> 01:11:34,220

Thank you.

: 1110

01:11:34,530 --> 01:11:35,430

Thanks for having me, Bill.

: 1111

01:11:35,970 --> 01:11:36,360

Thanks.