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Toma Bravo fund. Hello

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we're only getting started. Thank

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you for listening. We appreciate your support. And

1:54

now it is my pleasure to introduce Toma

1:56

Bravo managing partner, my

1:58

friend and more important. Certainly behind the

2:00

deal co-host Seth Bora. This

2:03

is Behind the Deal Live from

2:05

Miami. Well,

2:10

that was awesome. I'm

2:12

a little nervous here.

2:14

I've not done a live

2:16

video podcast before in

2:19

this format, but I think we're going to be okay.

2:21

We have an awesome, awesome topic today

2:23

that I know a lot of people have

2:25

a lot of interest in because we get

2:28

this question constantly about AI and

2:30

what's going on in our portfolio. Here

2:33

today joining me from three of

2:35

our flagship companies in fund 14 and fund 15

2:37

are Sumit Dhawan

2:40

from Proofpoint, Mike Capone

2:42

from Click, and Charlie

2:44

Gottdiner from Aniplan. It's a

2:47

nice representation of applications

2:49

with Aniplan, security in

2:51

the email cyberworld with

2:53

Proofpoint, and then data

2:55

and analytics with Click. Maybe

2:58

just to level set, AI

3:00

now is in

3:02

the vernacular constantly. It's

3:06

something that a couple of years ago, of course,

3:09

we knew about, but we weren't talking about it as much. It

3:11

would be great just to get each of your

3:13

perspectives in terms of how

3:16

we got here. Why is it today

3:18

that we're talking about

3:20

it? It's hard to read a

3:23

business article or a company article

3:25

without talking or thinking about AI.

3:27

What's changed, Sumit? Maybe we'll start with you.

3:31

Good afternoon, everyone. I sort

3:33

of say Proofpoint has always

3:35

had AI. We did cybersecurity.

3:37

You can't have people defending all these

3:40

other attackers. You have to use technology,

3:42

and the technology needs to keep learning.

3:45

By definition, that's classical machine learning.

3:48

I jokingly say Proofpoint did AI

3:50

before AI was cool, and I

3:52

call it the before cool era

3:54

or BC era of AI. We

3:59

Have done all forms of AI. Machine Learning and

4:01

a i might have to

4:03

essentially prevents attackers that are

4:05

constantly attacking through email and

4:07

other forms of communication. That.

4:10

People have. And a eyes

4:12

the technology of the models as the way

4:14

you beat all of you. You do pattern

4:16

detection and then you'd you detect. This is

4:18

a potential attacks and you don't let it

4:20

go. To. The to the employee

4:23

your the and uses as we

4:25

call it in the tech job

4:27

and and now what's happening is

4:29

with the generative A I. Basically

4:31

it's consumer rights where everyone can

4:33

experience the power of a I

4:36

because you can actually talk to

4:38

it is can generate responses, images

4:40

all forms of move models that

4:42

are available in terms of how

4:44

you experience Ai so it's readily

4:46

available. And. It's readily available

4:49

for both. Good. Guys like

4:51

us to use as well as in

4:53

the world of cyber. The bad guys

4:55

they are. You know, a I can

4:57

be used to potentially create new forms

5:00

of attacks and a I can be

5:02

used Now. For. Preventing those new

5:04

forms of attacks. So but in

5:06

the world of cyber as we

5:09

are certainly seeing more and more.

5:11

How. This sensitive a I both in

5:13

terms of. Threats. As well

5:16

as how you prevent that sets to

5:18

become more and more relevant and important

5:20

going forward. Mike What

5:23

some. Yeah. You've obviously in

5:25

class been delivering insights your

5:27

customers in various ways overtime.

5:30

How has this? Shift.

5:33

To Generative enabled you to deliver more about

5:35

now I'd actually be curious. Also, Charlie Tatty

5:38

hear from you when make his gun in

5:40

terms of what kind of business value

5:42

you're able to generate today with this with

5:44

this major platforms chef that damn that's

5:46

taking place really over the over the last

5:49

year, right? right? Well suited, right? First of

5:51

all, this is not new. I mean, we're

5:53

we're writing. I'm a I programming when I

5:55

was in college. You know, some twenty thirty

5:58

years ago. This kind of breakthrough. In

6:00

to consumer with Judge should be T and

6:02

L A Lamb's I'm has been able by

6:04

the masses computer power this out there to

6:06

it's available to us and cloud computing and

6:09

just more sophistication in Canada techniques. Are we

6:11

a cliff? We've been getting raped his moments

6:13

the last five years. We. Were

6:15

a analytics company and yeah we had

6:17

a I built into our platform. We've

6:19

been to an L P, Naturalize Processing

6:21

and our platform you will interact with.

6:23

Data. Using modern Ai techniques for long

6:25

time. Or what changes with his

6:28

massive compute his ability or for companies

6:30

out a harness all the information as

6:32

a finger tips in this kind of

6:34

explosion of ai in the scanner. Renaissance.

6:37

Had happened last year was a huge wake

6:40

up call to companies. You suddenly realize I

6:42

have to build a foundation. take advantage of

6:44

a i can just plug and and el

6:46

amor chatty bee Jay's at the and yeah

6:49

to do the work is what we see

6:51

this country's release. I'm start to scramble to

6:53

build and infrastructure in a foundation that secures

6:56

and governs their data. For. Ai

6:58

and make sure that you know run into

7:00

kind of problems I you're seeing out there

7:02

today our are false positives, hallucinations and things

7:04

and so the series of acquisitions I we

7:06

did including leading up their talents and was

7:09

really to get ready for this moment swimming

7:11

and races mom for last five years below

7:13

harness data for enterprises actually effectively use a

7:15

I as part of a corporate structure vs

7:17

on consumer. Riding High School term

7:20

papers for example is not Weber for China Do.

7:23

Yeah so I'm before. Talk about the

7:25

value points. Iceman One thing agree with

7:27

both of us. Everything that buses and

7:29

said. I think the other thing that's

7:32

important to recognize is that Ai and

7:34

Ml was really domain of. Data.

7:36

Science experts right and that expertise really

7:38

isn't needed as much as it still

7:40

needed Said to make advances when not

7:42

meet needed to engage in a I

7:44

like the ones was because it hasn't

7:46

and summarized by the alarms and and

7:48

the of the open A I architecture

7:51

so I think that was also a

7:53

breakthrough right where he just don't need

7:55

the decides expertise to engage with a

7:57

ice that we did. Two. Three

7:59

years ago. I'm. No

8:01

better and plan So we think about

8:04

creating value for customers with they are

8:06

in three different ways. The first is

8:08

driving more insight for them. So.

8:11

In a Plan in what customers do

8:13

on and a plan is they forecast

8:15

and plan their businesses and what a

8:18

eyes. And we've had a I products

8:20

now for five years. When Ai allows

8:22

them to do is to make more

8:24

accurate forecasts because we're we're running Mls

8:27

algorithms cause is mostly machine languish models

8:29

across much larger datasets, much deeper datasets,

8:31

much larger datasets both first party and

8:34

third party denim and that improves forecasting.

8:36

so that's kind of the first doctor

8:38

a value. The second is access. So.

8:41

This is where we get into Jenner

8:43

and right where where Release allowing customers

8:45

and all different levels of the organization

8:47

to access their models in a much

8:50

simpler way. So today primarily people access

8:52

it during a plan. Models or model

8:54

builders are experts that have to go

8:56

into the platform and dugout the inside

8:58

stuff. Hard to dig them out, but

9:01

you have to know something about the

9:03

platform. Tomorrow. We're going to

9:05

leverage network or really web prototype now.

9:07

Where. People can build to ask

9:10

their models questions using natural language, so

9:12

a Cfl could ask for a forecast

9:14

right on his way to work and

9:16

get that answer back when I haven't

9:18

got to go through an interim intermediary

9:20

that's expert in the platform. So.

9:22

That's that's today in the nuts. Actually getting

9:24

tied to work flocks so they can actually

9:26

asked. Sending the question to somebody has a

9:28

follow. So. That's really second

9:30

point of value. The third is really

9:33

efficiency. So. We think about developing a

9:35

copilot for modeling so said he asked again

9:37

you have to be an expert to build

9:39

a model and and a plan that takes

9:42

a lot of times. You start from scratch

9:44

and what the copilot will do is will

9:46

be a little old, build a model for

9:48

you and then model blurs become others as

9:51

opposed to craters that that's much more productive

9:53

and obsession. Sumit

9:55

In in cyber there is some

9:58

so many threats sectors. And

10:00

you know clearly. The

10:02

advent of generative ai has created yet another

10:04

one uses how are your customers managing those

10:06

threats today? Maybe walk through an example of

10:08

what you've seen in the feel just in

10:10

terms of some of the more modern day

10:12

threats that are coming out of that. The

10:14

use of as you said that you know

10:17

that they're bad guys has engendered of a

10:19

I of course it helps you know that

10:21

group as well and it's very well funded

10:23

and in what are you doing? On the

10:25

flipside. To. Help combat some of

10:27

these new threats and how quickly

10:29

have you had to react to

10:31

that? Yeah, Firstly, I think every

10:33

one of the was probably experienced

10:35

an email. What? a text message

10:37

that comes in either trying to

10:39

make you click on something. Or.

10:42

Sometimes. Even. Some. Something

10:45

coming from your Cfr Ceo for

10:47

in on discard that you want

10:49

a by. It's pretty common attacks

10:51

both for. Fraud. As

10:53

well as far as basically in

10:55

of planting the little malware into

10:58

your network or your computer which

11:00

can then eventually become ransom with

11:02

attacks and it i'm many of

11:05

your phones have either been through

11:07

it or in always prevent some

11:09

doing it. turns out more than

11:12

three quarters of those attacks stock

11:14

from email. And. They continue

11:16

to be that way. So

11:18

if you think about cyber

11:20

cyber already started from network

11:22

you know he added events

11:25

my computer network and makes

11:27

your bad guys. Can't. Get

11:29

In! But social engineering which is

11:31

people attacking people is the biggest

11:33

surface area for most attacks coming

11:36

in and that's what we focus

11:38

on. We have three zillion. Emails

11:41

that me that go through our

11:43

system. About one and a half

11:45

trillion Sms messages that go through

11:47

our system which give us a

11:49

mechanism to build. Models.

11:52

Models that can predict. Which

11:54

is a good email And what's a bad

11:56

email? So. It. At. Any given point

11:58

of time that able to see the

12:01

as a company. probably the only company

12:03

in the world who can really see

12:05

ahead of the curve on what is

12:08

the emergence of new types of attacks

12:10

which is what sets you would asking.

12:12

So using all this data we have

12:15

it set research and said intelligence team

12:17

that continually continuously publish published to our

12:19

customers and the world how the attacks

12:22

and evolving. what we're seeing now is

12:24

that generate of Ai at this point

12:26

of time. Is. Being used

12:28

more and more by attack as

12:30

which we can tell we and

12:32

tell when and the tax which

12:34

is coming in through an email

12:36

or a spurious looking website that

12:39

users that being induced to click

12:41

on because they looked familiar that

12:43

all been created. using. Sensitive

12:45

Ai. So in other words the

12:47

same attacks but are being created

12:49

by gender to me I so

12:51

what does that mean? That means

12:54

often times these attacks are created

12:56

with non native language speakers. That's

12:58

no longer an issue so we

13:00

can just sort of make these

13:02

emails and website through language assessment.

13:05

If. They are as threats or not,

13:07

that's no longer the case. We have

13:09

to throw that out and we have

13:11

to use other indicators to tell if

13:14

it's upset or not. Secondly, there is

13:16

more contacts You can bet is often

13:18

times now generative ai based attacks are

13:20

not going to be read this and

13:22

one. Email. To

13:24

a bunch of people as a

13:27

campaign. Instead. Because they're

13:29

being robotically generated, Generative A

13:31

I enables effectively a robot

13:33

to have a conversation with

13:35

several individuals in this room

13:38

which is more conceptual to

13:40

you as individuals, which makes

13:42

the models that we have

13:44

to have to protect against

13:46

those kinds of threats. Even.

13:49

More sophisticated it needs to have

13:51

more information on if I'm as

13:53

a send us sending you those

13:55

kinds of emails and trying to

13:57

induce you to click on some

13:59

things. Then using our technology now

14:01

which is also based on these

14:04

laws language models we can detect

14:06

you know what summit never really

14:08

sends. An email to

14:10

Orlando asking for credit card numbers

14:13

or. Just.

14:16

A difference but they have

14:18

such but so so that

14:20

would basically created them sexual.

14:23

Model. To. Say this.

14:25

Senders. Is not than

14:28

this is not the right context for

14:30

this type of female and that's the

14:32

kind of sophistication both said actors are

14:34

using and v at evolving an our

14:36

models to make sure we can protect

14:39

and that's a good thing in my

14:41

assessment for incumbents. Such. As us

14:43

because I started this by saying we

14:45

have three trillion emails a year that

14:48

we process one point three trillion you

14:50

know Sms seventy million euros That me

14:52

see what does. That mean that means we

14:54

have the data. Leak Insane. These models

14:56

better than anyone else. It's hard

14:58

for an incumbent like us to

15:00

be disrupted by someone because building

15:02

the tests and the code for

15:04

these martin is not the hard

15:07

thing. Training that's with the right

15:09

morning is a hard thing and

15:11

that's what incumbents. Benefit

15:13

from. Clique

15:15

Clearly the importance of data.

15:18

As. Is something and we've talked about for a while.

15:20

but if you it feels like we're now in a new realm.

15:22

Of. The value and only mad

15:25

and make I know you're neighboring customers

15:27

in this journey Can you help us

15:29

understand what your customers you're asking for.

15:31

Today's they get ready. To.

15:34

Use these large language models

15:36

term. Cradle. You for their

15:38

customers or internally and what does that look like?

15:40

It clicked. a customer is ready today. You do

15:42

their budget to the of the towel and have

15:45

a what What does that? You know what is

15:47

a process look like read now so are So

15:49

you're a year ago were all sorry mound this

15:51

explosion. And. There is almost a panic

15:53

out in the market rate as and boards

15:55

are the only a C O saying do

15:57

something, do anything and people are announcing an

15:59

issue the sending money. but really it was

16:01

just to show that they were paying attention

16:03

and and not paying for a been disrupted.

16:06

The good news is a lot of that

16:08

is settled down right now. The. Hype is

16:10

sort of settled in our into this phase of

16:12

people being a lot more thoughtful which is great

16:14

and what they're saying is and it's build a

16:16

foundation A double the right foundation. For.

16:18

My future of Ai and really comes down to three. Thanks

16:20

for says how can I get. The. Maximum value.

16:22

The second is how can I government and

16:24

be secure site on land in. In.

16:26

Jail he saw the each past really

16:29

sweeping legislation. Recently. Arm

16:31

around. What can happen if you miss use

16:33

a ice and then thirty cost right? there's

16:35

There's caustic up there is so say we're

16:37

building up his infrastructures. I'm on a date

16:39

aside. What? What we've been building where we

16:41

do is really simple which is. Data.

16:44

From anywhere any source cloud on

16:46

promise or wherever at high velocity

16:48

in real time and scam arm

16:50

cloud data like structures but also

16:52

governed catalogs with veracity proven data

16:54

lineage. I'm on on placing garden

16:56

aware not only way that is

16:58

where it came from and then

17:01

again allies. Easy modern kind of

17:03

techniques like analytics, an ai and

17:05

that's long as processing and then

17:07

something try some for is really

17:09

important. Can. Accidents and once you

17:11

get the inside is see Ozil Tommy I

17:13

had a got the answer the analytics Me:

17:15

I gave me the inside the now

17:17

my com. he didn't do anything with their

17:20

rights to the village actually take that

17:22

and put it into a workflow, put it

17:24

into an automation, send an alert. I'm integrate

17:26

to an Rp a platform supercritical otherwise

17:28

you'll get a value on that's what happened

17:31

so that that's Canada. The holistic view

17:33

of things and nematode say is on. On

17:35

the on the cassada things people are starting

17:37

to get the first anniversary of their

17:39

their cloud. Data like belay the starting understand

17:41

what's happening and how much money they're spending.

17:44

The mood date around. And they're freaking

17:46

out either saying I can't This isn't sustainable as

17:48

someone harness all my data. so we did

17:50

was. We built a series of. Analysts

17:52

have season a Powwow! It's platform a

17:54

top of our data platform so customers

17:57

manage putting data in the most cost

17:59

effective place. Don't drive the right outcome

18:01

but make sure that you know when you're

18:03

paying for computer you're paying for state I'm

18:05

I'm restores as you putting these in the

18:07

most cost optimal place and still getting me

18:09

I'll com as really how people are thinking

18:11

about in the going is is is moving

18:13

and a more thoughtful pace. Now scientists do

18:15

anything to say that you doing something am

18:17

there are budgets people are spending I'm a

18:19

lot of a soft down. C O sang

18:22

an earmark lot of money to get on

18:24

a I but now that money's being deployed

18:26

very very thoughtfully. Charlie

18:28

Obviously you need internal towel to

18:30

work with all these new. Technologies.

18:33

An Attorney You consider this a

18:35

platform shifter enabling technology Bad. Maybe

18:37

help us understand. You know

18:40

what that is look like as you're

18:42

running the company him in terms of

18:44

what sort of internal resources you need

18:46

actually turn this into a product that

18:48

revenue generating for you and has up

18:50

in a challenge given how quickly moved

18:52

are or how how the matter said

18:54

so far. Yeah so I'll go back

18:56

to a someday I talked about earlier

18:58

so you know this all started west.

19:00

Really a data science. Sort. Of

19:02

academic experiment right? That's where machine

19:04

learning really started. It became more

19:07

commercial impractical over time as we

19:09

the hardest more compute. larger.

19:11

Datasets And so the system

19:13

we've seen Intel on is.

19:16

Assessing whether our data scientists can make

19:18

that chef to be more commercial. To.

19:21

Drive commercial outcomes,

19:23

From. Their. Of machine learning

19:25

models and really drive a I

19:27

that's going to drive the business.

19:29

So I talked earlier about. You.

19:31

Know us are leveraging generative

19:34

in. In. I'd tell the of

19:36

nib the prior to ask Anna plan

19:38

South Pacific could actually query miles through

19:40

natural language. That's. A

19:42

harder shift for. Academic

19:45

data scientists. They don't think about the

19:47

world that way because they want to

19:49

run the next experiment. They. want to

19:51

build the next in a large language

19:53

model that may or may not have

19:56

a practical applications so that's really our

19:58

challenge right as turnout figuring out who

20:00

can really make the leap to commercial

20:02

because we're not an academic institution, right?

20:04

We want to leverage the knowledge of

20:07

academic institutions and we want to commercialize

20:09

it. So that's the journey that we're

20:11

on. We've got a fairly big team

20:13

that's trying to make that journey that's in Israel

20:15

that came through in acquisition for us. Mike,

20:18

you mentioned the EU legislation

20:20

that came out last week. Maybe

20:23

walk people through because you're very

20:25

deep in that. You've formed an AI

20:27

council around governance and ethics. I think

20:29

it'd be great just to get your

20:31

perspective and then Sumit and Charlie, your

20:34

perspective on just from a governance and

20:36

ethics perspective, how this is

20:38

being managed internally at your companies because it is

20:40

a big issue. It's

20:42

a huge issue. Just like every

20:45

other technology, the innovation is

20:47

going to outpace society's ability to kind of

20:49

regulate it. It's just how the

20:51

world works in today's world. We

20:54

in the corporate world actually have an

20:57

even greater obligation to govern this and

21:00

to make sure that what we're deploying, the tools we're

21:02

deploying and the technology is used in

21:04

the right way and can be governed in a way that you can

21:06

control the ethics of it. So

21:08

we're really thoughtful about that at Clique. For

21:10

the most part, I'd say most companies in

21:13

our industry are behaving that way. Some

21:15

aren't, but most are. My

21:19

favorite kind of paradigm is if you think about

21:21

social media 20 years ago and how that started

21:25

taking off and what that's done to teenagers

21:27

in today's world, would we have approached that

21:30

the same way? Would we have let that go

21:32

the way it did, completely ungoverned for so long?

21:34

Would we have been more thoughtful about it? I

21:36

think that's where we are with AI today. We

21:39

at Clique spend a lot of time

21:42

thinking about our responsibility to the ethics and

21:44

the morality of AI. What

21:46

we know is that we don't know. We

21:48

haven't figured everything out. We formed a council of

21:51

people mostly outside of industry, people

21:53

from public sector, people from academia. These

21:55

are our four very respected individuals, come from

21:58

Cambridge and things like that, who actually... advise

22:01

us and our customers on the

22:03

ethical deployment of AI and how we can do

22:05

this in the most, you know,

22:08

capture all the value, certainly we're in the

22:10

value creation business, but also do it in

22:12

a thoughtful way where people view us as

22:14

a trusted partner that helps them be successful

22:17

the right way rather than the wrong way.

22:19

The EU legislation is a great example where

22:21

if you've read it they've gone quite deep

22:23

and they've categorized different uses of AI, some

22:26

of it being completely prohibited. So using

22:28

AI to predict criminal behavior, even

22:30

to predict ethnicity or gender,

22:33

some things you might not be able to do anymore in Europe

22:36

with your platform if this really takes

22:38

hold and they've categorized it from ways

22:40

and it puts a huge burden on companies to make

22:42

sure they're using AI the right way and govern their

22:44

data the right way and like, you know, the EU,

22:46

they never do anything like light, right, the EU Parliament,

22:48

so the penalty is up to 7%

22:51

of turnover, 7% of revenue if you

22:53

screwed up, right. So you really need to be

22:55

very thoughtful, very reminiscent of GDPR and so, you

22:57

know, we have to help customers, all of us

22:59

have to help customers make sure that they're doing

23:01

this not only fast and capturing a

23:04

lot of value but the right way morally,

23:06

ethically and in compliance with the law. Yeah,

23:09

I think given with proof

23:11

point I mentioned we have

23:13

data from customer data, so

23:16

one of the big things in addition

23:18

to obviously our models being trained

23:20

the right way and are doing the right things

23:23

that are compliant, we also have to be extremely

23:25

careful with data residency laws

23:28

that are there, so data residency

23:30

laws are different across the globe

23:32

and they become even more important

23:34

and critical when you're applying these

23:36

models on them. You know,

23:39

it becomes quite complex like for example,

23:42

I think everyone here has heard

23:44

of or maybe even familiar with

23:46

or experienced co-pilots from Microsoft. One

23:49

of the banks that maybe we have

23:51

folks here as well, I

23:53

heard from them that they were

23:55

doing a pilot and co-pilot was

23:58

linked into certain business applications. as

24:00

well as, and I don't know if

24:02

it was Copilot or some other generative

24:05

AI chatbot that was prepared, that was

24:07

connecting to different business applications and they

24:09

were running a pilot and they enabled

24:11

a set of users to start doing

24:13

queries. And once you

24:15

enable business users to have

24:17

that form of free form

24:19

access to information that's in

24:22

multiple business applications,

24:25

the type of queries that people

24:27

start making can expose

24:29

the information that sometimes regulate

24:31

it and protect it in

24:33

certain ways which you

24:36

never thought anyone

24:38

would sort of be able to access. So

24:40

all of a sudden it is opening up

24:43

a whole new dimension of

24:45

data security protection, governance, requirements

24:48

for customers. And so I do

24:50

see in terms of adoption of

24:52

the technology, even from the customer

24:54

end, where they are all going

24:56

through this sort of phase of

24:59

how they are gonna tackle cost and governance

25:01

elements. And for our end, we have to

25:03

be much more thoughtful in terms of what

25:05

we do with our own data governance for

25:07

all kinds of regulations

25:09

that exist from sovereignty

25:11

perspective, residency perspective, or in

25:13

general compliance because of the new rules that

25:16

are coming in. Yeah,

25:18

I agree with that. I think many of

25:20

you may be asking, so what now? What

25:23

do companies do? I think the

25:25

news is actually a little better than it was five,

25:28

10 years ago from a data

25:31

privacy perspective because there

25:33

are regulations and there are models

25:35

that companies have followed for data

25:38

privacy and data governance. And so

25:40

there are techniques like privacy by

25:42

design that really govern the

25:45

use of data in product innovation in

25:47

most companies. Not every company is great

25:49

at it, but at least these kind

25:51

of concepts exist. And I

25:53

suspect AI will leverage those to begin

25:55

with, but we'll need, as Mike, you

25:57

point out, a lot more governance because...

26:00

it can be a free-for-all and

26:02

privacy by design is actually a pretty

26:04

rigid process that forces data governance rules

26:07

but if AI is in everybody's hands

26:09

then it would be hard to really

26:11

manage that and police it. So

26:13

but I do think the news is a little

26:15

better there's a starting point and most companies have

26:18

data privacy and data governance committees

26:21

set up that as a starting point

26:23

and that's where I where I see

26:25

a lot of companies starting is

26:28

in those committees. You

26:30

hear talk about the fact that generative

26:32

AI could be an issue as it

26:35

relates to IP and

26:37

you know the the competitive

26:40

landscape within certain

26:42

industries especially within software where maybe

26:45

there's an ability to spin up a new company

26:47

quickly. Can you share your thoughts

26:49

on you know on that how big

26:51

a risk is it to your businesses

26:53

what should we all be thinking about as

26:56

investors in software as it relates to that

26:58

maybe submit. Yeah I mean first of

27:00

all I think as I mentioned

27:02

in the world of cyber broadly

27:04

speaking there is infrastructure and human

27:06

level protection we focus on the

27:08

ladder that's the biggest surface

27:11

area of risk or threats and

27:14

the generative AI is actually to

27:16

some extent bit of

27:18

a tailwind for us because it

27:20

increases the potential of attacks makes

27:22

it creating an effective attack easier

27:25

and more available and

27:28

accessible to bad actors at

27:30

lower cost than them trying to do

27:32

it themselves. Okay so we are

27:34

seeing that as potentially

27:36

a tailwind and as

27:38

long as we have the right technology

27:40

to defend against those types of attacks

27:42

in general that sort of grows the

27:44

need for a sort of robust solution

27:46

like proof point. Secondly as

27:49

I mentioned at the end

27:51

of the day you need more sophisticated

27:53

models to prevent against those types of

27:55

attacks and those models can

27:57

only be sure you can have coders

28:00

write code faster, but without access

28:03

to that data, you're not gonna

28:05

be able to develop those models.

28:07

And so there is a inherent

28:09

incumbency benefit. Can you get data

28:12

elsewhere? Maybe possibly, but nowhere the

28:14

same quality of data that we

28:16

have as an incumbent when we

28:19

are already serving enterprise customers and

28:21

delivering their critical asset like mail

28:23

and other data protection solutions. So

28:26

I think second is that it

28:28

does give benefit to incumbents. I'd

28:30

say third, I don't think we are sitting

28:33

still. We are

28:35

leveraging the power of generative

28:37

AI and these large language models

28:39

to create more upsell and cross-sell

28:41

opportunities. For example, you

28:44

may have all experienced it. You may

28:46

have fat-fingered yourself, a

28:48

wrong person and sent, not

28:51

just an attack, but just information

28:53

that was not meant

28:55

to be sent to someone that

28:57

you sent an email to, and then you sort of

29:00

sent a note, oh, can you please delete it? That

29:02

was not for you. It's happened to all of us.

29:04

Because we are in the mail flow, we've built a

29:06

solution and we already have the context,

29:09

the example that I gave where I

29:11

sent that attack to Orlando. Instead of

29:13

an attack, it may have been just

29:15

an email that I accidentally was sending

29:17

to Orlando. And our technology now would

29:19

prompt me to say, hey, do

29:22

you really intend to send it to

29:24

Orlando? This doesn't look like the type

29:26

of information you usually share to him.

29:28

So it's a nudge as

29:30

AI technology. And that's extremely helpful if

29:32

you can think about even in your

29:35

businesses, in large banks, there are teams

29:37

of 40 to 50 people that are

29:39

simply checking outbound emails to all sorts

29:41

of clients if that email is the

29:43

right one to be sent or not.

29:46

We can take all of

29:48

that cost out and create that

29:50

incremental monetization all because of the

29:52

power of Gen AI. So enabling new

29:54

use cases because of the technology. So

29:57

A, it's a tailwind for us in

29:59

the cyber. B, incumbency helps us,

30:02

doesn't hurt us in any ways. And

30:04

C, with us standing still, we're building

30:06

new monetization and new solutions that are

30:08

serving real problems for our customers, which

30:10

are much, much easier for us to

30:12

bring to market than anyone else. Charlie,

30:15

do you worry about somebody building an Anna

30:18

Plan competitor quicker

30:20

and easier now

30:22

that code's easier to generate and you don't need

30:24

to be a specialist anymore? I

30:27

don't, primarily because if I'm

30:29

exposing my customer's data to

30:31

public generative AI models, I'm

30:34

done, right? Because they

30:36

now own my customer's data. And

30:38

so we have a proprietary platform that

30:40

really protects that data and the usage

30:42

of that data. So to

30:45

me, a lot of this comes back to data

30:47

at its core, right? Generative AI

30:49

is gonna allow us to get a lot more insight

30:51

out of data. And so what

30:55

we're working on to advance the insight

30:57

dimension that

30:59

I talked about earlier is really

31:01

leveraging generative AI to do

31:04

more dynamic forecasting. So really

31:06

early warning systems. So

31:08

you can imagine a company, let's say it's

31:10

a manufacturer and they've got distribution,

31:12

they've got big retail customers, think

31:14

Walmart or Target, really big customers.

31:16

And now they've got external events

31:18

that they actually wanna do, put

31:21

into a early warning system

31:23

like weather, right? Would be the simple example.

31:26

So if my big customers are running a

31:28

massive promotion on my product and I've got

31:30

a weather event, I could be

31:33

pretty out of sync with where inventory

31:35

is in my distribution centers. And I

31:37

could really risk irritating them because they

31:39

have a lot of stockouts when they've

31:41

got a big promotion coming, right? So

31:43

we're gonna use AI to actually create

31:45

early warning systems. And that was a

31:47

simple example Across multiple data

31:49

sets. So We can help our

31:51

customers forecast even more accurately and

31:53

actually get ahead, really get in

31:55

the predictive business so they can

31:57

avoid stockouts in my example. Rather

32:00

than you know what, what are we going to do

32:02

about a forecast? Will we know we're going to miss

32:04

inventory levels? That kind of

32:06

where we're headed out with Gen Vi. We

32:11

talked a lot about our our

32:13

portfolio today and driving efficiency. And.

32:16

You know? currently running at and. Forty. Percent

32:18

margin, sometimes more. It would be

32:20

great if you could walk through

32:23

examples internally so we talked to

32:25

Buy your product. We talked him

32:27

away during the customers the market.

32:30

How can you leverage this next

32:32

generation of Ai technology to across

32:34

your organization to drive operational efficiencies.

32:37

In all aspects or and when and where do you

32:39

see today when you see a going. On

32:42

today today our our called the low

32:44

hanging fruit exercises so where we are

32:46

left so for example cogeneration I were

32:48

at in the day. We write software

32:51

The ability actually I generate code that's

32:53

fairly standard. We use wall. Is.

32:55

With a eyes gotten much much better with

32:57

gone so far beyond you know can open

32:59

source in our those those those a public

33:01

of Alwan and that's how are a little

33:03

rhyme. Or. Indie percentage lower than

33:05

lot of our competitors raid. In addition, a

33:07

low cost locations here there everywhere. I'm a

33:10

big impact right now isn't so the sales

33:12

and marketing area where we're able to do

33:14

a lot more. Targeted are

33:16

selling targeted marketing. I'm.

33:19

Using our own capabilities inside o'clock in the

33:21

uses some third party tools like six as

33:23

exaggerate actually looking kind of buying patterns. Who's

33:25

in the mark Airless target them on. make

33:28

sure your our Santa Me is is not

33:30

knowing rights. I'd I'd data and I'll be

33:32

ideal coming out before we see them. Now

33:35

it's kind of the tools out there. We

33:37

can look out in the market with his

33:39

during certain searches and predict who's about the

33:41

likely by. Our software. Computer software

33:44

an axe ago. Target? That's so. be

33:46

adding that. See ya, That's the. As

33:48

I was it easy stuff doesn't have it's easy

33:51

but it's is right in front of us rightists?

33:53

it is right there and then you know going

33:55

forward. I think I'm the own for us. We're

33:57

We're pretty simple business right? We buy software. Me.

34:00

All software. But. There are opportunity

34:02

to help our customers who got massive

34:04

massive data, who got a lot of

34:06

bomb. Yeah. I'd say kind of

34:08

money processes and noom son of fight

34:10

through. stop is gonna be is going

34:12

to be almost unlimited and enter the

34:14

real. The the real kind

34:17

of differentiation we have on on. Really sad

34:19

about the town business because you're a lot

34:21

of people think that having more data is

34:23

better and more is not always better. some

34:25

has better is better. I said ability to

34:27

actually take the data the customer has Were

34:29

talking earlier sad about a large automotive manufacturers

34:31

to build their own. Infrastructure.

34:33

Their own L am their own, their own

34:36

ecosystem and will be helping them do now

34:38

is harness all the data inside of their

34:40

four walls. I'm including the data comes off

34:42

the cars and the sensors and everything in

34:45

real time and make decisions about him and

34:47

to worry about arm of where put the

34:49

next charging station for example and as a

34:51

sluggish use productivity I left for them and

34:54

then to protect Ip nothing goes out. And

34:56

when they bring. Data. From

34:58

the outside bacon. Approved.

35:00

Or acid of it. They can carry that day and

35:03

make sure that it's not going to inject bad

35:05

date or bias into their dataset. So for me, that's

35:07

the most exciting part of all. This is what we

35:09

can do for all of our customers. Semi.

35:13

To be great to hear about how

35:15

you. Today. You're in mid thirties

35:17

margin would you see operationally internally day

35:20

move that to and in mid forties

35:22

and fifty percent for be recorded to

35:24

see now. It's

35:26

a commitment as a commitment. Yeah, yeah.

35:29

wedding. I think. First of all, we're

35:31

We're We're in a similar state. starting

35:33

with you can call it low hanging

35:35

fruit. although the results of those have

35:37

been astonishing li positive in the code

35:40

development. Ah, Since developers have

35:42

embraced it, the success that we're

35:44

seeing in terms of code that

35:46

comes in as a suggestion that

35:49

people except it's basically is another

35:51

proxy to show and that is

35:53

productivity intrinsically being improved in terms

35:55

of how fast week been new

35:58

code so that certainly there's. So

36:00

how are you managing the security on that code,

36:02

which I know is a big issue? No, we,

36:05

you know, code right now sort

36:07

of goes into your own instance

36:09

of GitHub largely. So these are

36:11

appropriately sort of governed private

36:13

instances of code that is

36:15

running. So the right security for

36:18

IP protection is being put in place. But

36:20

I do, we do think that is improved

36:23

productivity without any compromise of IP

36:25

leakage that's been done. I

36:27

think where I'm most excited

36:29

about where we are just starting to

36:31

do is more predictive

36:33

analytics on churn. Because

36:35

we have a lot of indicators

36:37

of information from customers

36:40

on usage of the product and

36:42

how through that we can

36:45

create patterns and very quickly then

36:47

have the right reactive or

36:49

quickly corrective measures in a more

36:52

proactive manner put in place. Today,

36:55

a lot of that

36:57

is done somewhat manual, somewhat reactive

36:59

and just, you know, you can

37:01

imagine resource allocation is not

37:04

sort of really been put in

37:06

place as proactively as we could.

37:08

So using

37:11

models, you can think of them as

37:13

still fairly simplistic AI models, but these

37:15

days those models can greatly improve your

37:18

gross retention rate. And all of a

37:20

certain, just a single point

37:23

of gross retention rate can give a

37:25

significant boost to the earnings flows through.

37:27

So I think that's the one in

37:29

addition to sales and marketing I'm excited

37:32

about. We're starting on that. And then

37:34

there is some education,

37:36

tech support, again, public information

37:38

that's available, how we can

37:41

just streamline it so that there are less calls

37:44

coming in and improves the productivity

37:46

of when we actually serve The

37:48

customers calling in, we are doing so in

37:50

a faster way and whatnot.. Because Usually there's

37:52

a lot of time taken by tech support

37:54

engineers to train them and whatnot.. And these

37:57

days, no one really wants to call. So

37:59

If there is..., Agent that can serve

38:01

large volume of support support requests that

38:03

the another investment we're making to make

38:05

sure we can be more efficient. Straight

38:08

Charlie and any saying the using your

38:10

is so very similar theme so lives

38:12

I want we're working with these guys

38:15

said But what one thing I'll add

38:17

as we're. We're. Starting to leverage

38:19

ai pretty deeply and cells productivity

38:21

in that scenario that we're working

38:23

on improving in the business michigan

38:25

a small margin expansion. And

38:28

we've lovers a third party or a I

38:30

told to do this. but one of things

38:32

that we discovered is that I'm We went

38:34

into Loesser thinking that your head of a

38:37

A meetings a week to reach her father

38:39

as a salesperson at that activity level would

38:41

drive it. We. Learned was that

38:43

says she not a meetings especially for

38:45

meetings with director level or above that

38:48

generates I shall hundred and twenty five

38:50

percent of one in shipments. And

38:52

so then the question becomes well who

38:54

has the relationships right? What's the relationship

38:57

map look like Of course we find

38:59

that we're not where we need to

39:01

be to generate those for high quality

39:03

meetings. a weird across some buddies portfolio

39:06

and so that allows a sad than

39:08

as the next question which is how

39:10

we're going to leverage Ai to access

39:12

the right relationships whether it's third party

39:14

data. Be a linked in

39:16

or something else and that's really

39:19

what we're working on now because we

39:21

know that that relationship exists, that you

39:23

know better meetings, words more senior people

39:26

allows us to focus the activity

39:28

and be more productive at the same

39:30

time. I'm and in a when we

39:32

marry that with our product and we

39:35

actually have an Ai product called Predictive

39:37

Insights And what their product does is

39:39

it helps our customers are identified

39:41

the ideal customer profile. So now you've

39:44

got the right customer profile. You got

39:46

much more targeted meetings and that should

39:48

be a much more productive formula for

39:50

driving new bookings. So.

39:53

All of that leverage their. Maybe with

39:55

the last question? How are

39:57

you balancing? Where. We're at

39:59

today and the pace of innovation just in

40:02

this area going forward in at you know

40:04

how much time are you spending on the

40:06

future versed in the present because. I

40:09

think of this and this may be

40:11

this platform Sept is moving out of.

40:13

the platform shifted as generate this. You

40:15

know the technology innovation here is moving

40:17

as fast as anything we've seen. And

40:20

the absorption rate has been really high. and

40:22

the fact that you're using it today we're

40:24

talking about it in a way that very

40:27

tangible is impressive given we're really just a

40:29

couple years and it has generated a i

40:31

say but simeon have had. how do you

40:33

think about that a manner sad, just that

40:36

piece of innovation and any sauce and what's

40:38

next day? I think first of all this

40:40

technology as many of the have club probably

40:42

dead in terms of just adoption from filled

40:45

with population all that it's off the charts

40:47

in terms of sexy pity adoption. so intense

40:49

for us. We have to be

40:52

as a cyber defense company,

40:54

a step ahead of the

40:56

bad actors at this point

40:58

the time. the internals machinery

41:00

at proof point is assuming

41:02

that the. Bad actors

41:04

have access freeform access to this

41:06

technology that sets will be created

41:08

using this and be are leveraging

41:10

the technology to ist fullest when

41:13

it comes to building the defense.

41:15

Against it that is no this.

41:17

that said topic as our city

41:20

offices sort of squarely responsible for

41:22

it or we did an acquisition

41:24

of the company. contests in there's

41:26

a lot of effort was as

41:28

going in to integrate. It brought

41:31

in allies language model of our

41:33

own. With. The right sort

41:35

of costs up to that can

41:37

build evades it's contextual grass. You

41:39

may have heard this term which

41:42

essentially created relationship between large number

41:44

of objects so that you can

41:46

very quickly you know identify patterns

41:49

that eyes off the you know

41:51

the norm and so both through

41:53

organic efforts as well as odd

41:55

investments via Amman A V R's

41:58

on that front yard. No,

42:00

we're not assuming this is something that's

42:02

that's gonna happen in the future. We

42:04

have to stay a step ahead and

42:06

that sort of gives us credibility. With.

42:09

Our customers that creates momentum with

42:11

customers. a drive somewhat of growth

42:13

as well for us. So that's

42:15

sort of autism sin. Or. When

42:17

it comes to a cold business, Us

42:19

Cyber. For. Areas that

42:21

we have: Data Protection. Portfolio.

42:24

And Desist as well which is a

42:26

growth business in addition to email security

42:29

or data protection business where we are

42:31

closely monitoring. There's what. Tangible.

42:34

Innovations we could build so

42:36

that when customers start adopting

42:38

a I for their critic

42:40

the lines of business. Applications

42:42

use cases. The example that I

42:45

was giving bad there was a

42:47

chalkboard talking to various applications. Power

42:49

data protection engine can just how

42:52

we can ride that wave at

42:54

that adoption happens. So for Descend

42:56

solutions we're assuming it's years for

42:59

our data protection solutions. We have

43:01

a closely monitoring how and when

43:03

the customers adopted so that odd

43:05

innovations and on behind of But

43:08

it's it's A it's a topic

43:10

we're discussing on an ongoing basis.

43:12

Because his can't afford not to. Heading

43:16

for us the a week we built a

43:18

lot of structure to make sure that we're

43:20

intern or sorts ottoman or Ai council which

43:22

is really important rather gives is one perspective.

43:24

We have us a large customer council or

43:27

that advises on a i'm what they're doing,

43:29

what they're seeing in the markets and we

43:31

have representation from all industries on that that

43:33

council to help us go. I'll tell you

43:35

though, the the biggest secret weapon we've got

43:37

actually is our I'm In A Strategy and

43:40

our partnership with Summer Bravo. We got me

43:42

on my top in In and Money's and

43:44

we're constantly scanning the market looking. For. Cool.

43:47

A item is where the during a lot of

43:49

times the best source of innovation is to look

43:51

at what's out there and the market. Where's the

43:53

metro money flowing and what's going on out there

43:55

to predict how know what the next wave of

43:57

innovations gonna be and that is super helpful and

43:59

and understanding. What we need to get ready for

44:01

a while we we will. We should be buying. And

44:03

you know as B C Young dries up in some

44:05

of these opportunities were going to be all over him.

44:08

China to buy you know, pennies on the dollar for

44:10

went into these things. Now.

44:12

And I would say in a we're

44:15

going at a measured pace right word

44:17

center earlier in the whole than I

44:19

think as some other companies have just

44:22

gone through a big transformation of the

44:24

early stages of transformation. So we're we're

44:26

gonna in continued of take Arrayah legacy.

44:29

Leverage it into the products in the platform

44:31

that we have. I can imagine a fusion.

44:33

I've started talking to my. I had

44:36

a product and technology about this where we

44:38

have a leader of a I products I'm

44:40

in that will help us with an with

44:42

they are and M and m in a

44:44

strategy to us to augment what we do

44:46

their and that's a conversation which we just

44:48

started but I suspect we'll get there are

44:50

some time this year because it is such

44:52

a big topic and some big topic for

44:54

innovation will we are of the shot clock

44:56

is almost out. We this

44:59

has been great. I think the thing

45:01

that's most interesting is often times you

45:03

know you you hear a lot about

45:06

in the venture industry innovation taking place

45:08

in in areas like A I. One

45:10

of the big take away today that

45:12

we would like to born here to

45:15

understand is that are companies are innovating

45:17

in and around these areas in big

45:19

ways with massive are d budgets not

45:22

only delivering innovation to customers and to

45:24

the industry, also creating policy. you know,

45:26

thinking about ethics in. Which

45:28

is incredibly important but then also taking

45:31

this new technology and driving it into

45:33

internal operations to produce better business results.

45:35

So thank you for the great work

45:38

as and fascinating and that we we

45:40

really appreciate or times you being here

45:42

tonight and a surplus of. This

45:49

is our final episode of season since I

45:51

wanted to take a moment to say thank

45:54

you to all of you for coming along

45:56

on this journey with us. We've

45:58

done to share something from those

46:01

stories of hundred partnerships and we

46:03

appreciate of illicit. Will be taking

46:05

a brief break to record more

46:07

episodes but we'll be back again

46:09

to take you behind the deal

46:11

later this year. Hum Orlando Bravo!

46:13

Thanks from is. Certain.

46:26

Statements about tell Him Babbel made

46:28

by portfolio companies exodus are intended.

46:30

To illustrate Summer Bibles business relationship

46:32

with such persons, Rather than tell

46:34

my brothers capabilities. Are expertise with respect.

46:36

To advisory services portfolio company

46:39

executives were not compensated in

46:41

connection with their podcast participation,

46:43

although they generally receive compensation

46:45

and investment opportunities in connection

46:47

with their portfolio company. Roles

46:49

and in certain cases are also

46:51

owners of portfolio company securities and

46:53

or investors and Tell the Bravo

46:55

funds sense compensation and investments subject

46:57

podcast. Participants to potential conflicts. Of

47:00

interest.