Imagine you dedicated your career to artificial intelligence before anyone
believed in it, before plentiful computing, before ChatGPT, before the
breakthroughs that changed everything.
Today's guest did just that, surviving the second AI winter, and now helping
businesses mitigate AI related risks.
My name is Alexandre Nevski, and this is Innovation Tales.
Navigating change, one story at a time.
We share insights from leaders tackling the challenges of today's digital world.
Welcome to Innovation Tales, the podcast exploring the human
side of digital transformation.
Alec Crawford is the founder of Artificial Intelligence Risk, Inc., a company leading
the charge in AI security, safety and compliance, earning top rankings in gen AI
cybersecurity, and regulatory compliance.
With a Harvard background in AI and decades of experience managing investment
risks at firms like Goldman Sachs and Morgan Stanley, he understands both the
potential and the pitfalls of AI adoption.
In this episode, Alec shares how enterprises can move beyond AI
hype to real world implementations.
You learn why not every gen AI use case is worth pursuing, how to build
AI governance that actually works, and what business leaders must do
now to stay ahead of emerging risks.
Without further ado, here's my conversation with Alec Crawford.
Alec, welcome to the show.
Alex great to be here!
Oh, it's great to have you.
You've been involved with artificial intelligence long
before it was mainstream.
So let's go back to where it started.
I believe it was 1987 and you were writing your master thesis at Harvard.
What led you to take that initial step?
Yeah.
I was at Harvard.
I was actually, still in undergrad when I started.
and I was building neural networks from scratch.
And back then, if you had walked around the street and said,
Hey, what's a neural network?
People would be like, oh, that's your brain, right?
people just didn't even know what it was.
Even people in computer science didn't, A lot of them didn't
know what it, know what it was.
And it was pretty cool to be there at the beginning.
and the 1980s was really a period of excitement because,
it was starting to work, right?
You could do some natural anchorage processing, you could
teach computers to do things.
I was teaching them to play poker as an example, and bet and bluff and
things that people obviously associate with humans more than computers.
and, my thesis advisor was a guy named Bill Woods who would invent something
called Lunar, which was a natural language processing system to let you ask questions
and get answers about the rocks that have been brought back from the moon.
So you could say things like, how many rocks were there, how many were
igneous, what was the largest rock?
It would give you answers.
So back then that was revolutionary.
It was amazing.
And, uh, while I was, at Harvard you know, we were still at the upslope
of the hype cycle for AI back in the eighties, so people were super excited
about it and just dreaming of what else you could do with it all the time.
1987 sometimes is mentioned as the point, like the beginning
of the second AI winter.
So from a hype cycle perspective, it was the peak.
Is that what you meant?
Yeah.
I tend to agree with that.
I think that, people were, had developed some, expert systems,
which were supposed to mimic what experts do, and I think people were
starting to see the limitations.
And by early nineties, I'm not gonna say AI was dead, but Wow, and at least in
corporate America, it was done, right?
And obviously people were still doing research and that's what eventually led to
the invention of the transformer and large language models as we know them today.
But yeah, there was a very dark period of time there for AI.
I actually did a little bit of AI right when I came out of school
at the bank I was working at.
And it was more me saying oh, they've given me a job to do.
I think AI could actually, do this pretty well.
and I was right.
but it never, really took off and it was all, homegrown stuff.
You couldn't go out and get a model from Google or anyone like that.
It was like, if you're doing it, it's 'cause you built it.
That's it.
There's nowhere else to go.
And so what was that use case for the bank?
Yeah, so there, there was an enormous amount of, data that came in.
I happened to be working, in the, the mortgage department or the
mortgage research department.
and we would ingest massive amounts of information, every month about
mortgages, mortgage rates, and did people pay off their mortgages?
Were they delinquent?
Things like that.
So I used the model to do some inferences, around that, which, was
interesting and is kinda a good use case for AI, even back then looking
at lots of data and trying to, train a model and see what you get out of it.
So it was predictive kind of analytics, early days.
Early days, predictive analytics and, yeah, it, we had modest success with that.
What I'd say is that kind of standard parametric models, and
non-parametric models ended up being longer term more successful
than a neural network in that case.
So some interesting things there, but in the end just wasn't additive.
And that's actually an important lesson for today.
AI is so hot that people run around and go, oh, we gotta use AI for X and, they
use AI for X, but you may actually get a better outcome using a more traditional
model or approach in some cases.
And it's also highly likely in that traditional model or approach that ends
up being faster and cheaper to run.
That instinct to immediately go to gen AI sometimes needs
to be vetted to some degree.
Yeah, well 1987 or the couple years later when you were working at, at
the bank, that's way before deep learning and neural networks had
the computing power to actually be useful in the way that they are now.
how did that AI winter, impact you?
Did you like regret studying artificial intelligence in, in, as a undergrad?
Oh, not at all.
No, it was awesome and I loved it and I stayed in touch with it,
all the time throughout my career, whether reading research or doing
my own projects or, occasionally, adding things to, to work, Hey, we
should try this and see if it works.
And, obviously, it's back, but what I would say is.
There really wasn't much of a budget for it.
You'd have to sneak it in on the side as opposed to today you've got
CEOs going to heads of technology, going like, do some AI thing.
I don't know.
I don't care how much it costs, or, here's a million dollars, figure it out.
That was not, the story back then for sure.
Yeah, certainly things have changed dramatically.
What's the inflection point that for you is the start of what we are
experiencing today in the business world?
Yeah, I think I take it back to really the public launch of ChatGPT,
two years ago in November, which really got people's attention.
So what's interesting is a lot of these you know techniques and models
have been out there for a while.
Until OpenAI put it out on the internet and said, Hey, anybody can do this, a lot
of people just didn't even know about it.
And even in the first couple months, you had a number of people using it,
but also a number of people not using it and certainly not understanding it.
But it obviously, the concept went viral pretty quickly.
And the strides made even in the different model versions as we went
from Llama two to three and OpenAI up to version 4o, were tremendous.
And that's something that I look at all the time, like how capable of
the models and how robust are they and, can you trick them into doing
things that they're not supposed to?
Things like that.
And just their capabilities across those different areas, both being
able to, answer questions, solve puzzles, but also do it in a robust
manner, ethical manner, have improved.
That, that being said, not the case for all models.
So it's a pretty famous that, DeepSeek fails almost every one of the 350
different safety tests, right?
Hey, tell me how to build a nuclear bomb.
Okay.
Not great.
But looking at that in the eighties, we weren't even dreaming of that, right?
We didn't realize that someday someone would have the capacity to
basically take everything from the internet and training AI model.
Like what?
That wasn't even in the realm of possibility in our minds at that time.
So when Chan GPT was announced and made available, what
were you up to at that point?
I had just, retired.
I was from finance.
I was at a company called Lord Abbett, which is a mid-size
asset manager in the US.
They manage mutual funds and institutional money, about $250 billion.
And I was running risk and doing a bunch of other things around AI.
And, I played a hundred rounds of golf.
And, actually pretty early that year I was watching these really big companies
onboard AI with just zero guardrails.
And we're talking big companies.
This is crazy, right?
it's not only bad for.
Those companies if something goes wrong, but also potentially their
customers releasing customer data, their employees, putting them in
bad or awkward situations or worse.
Right off the bat started, writing up, the first piece of code that was gonna do AI
governance, risk management, compliance, and cybersecurity as a platform as opposed
to single point of use or just one thing.
And honestly, I didn't know if it was even doable, right?
To do all four things.
And I had, I got my, basically taught myself Python 'cause I'm
more of a c#/c++ programmer.
But it was easier to template in Python.
And got it running and then, showed it to my co-founder, Frank Fitzgerald.
He's like, oh my God, this is amazing.
And I don't think I've written more than three lines of codes
since then 'cause he took it over.
So, but it was fun to go learn Python and he's, guess what?
Writing it all in C# on .Net.
which is, obviously where you need to, what you need to do
to professionalize things.
So
All right, well, since you've broached the, the topic, then
let's talk about that, approach that you use to help clients with,
mitigating, I guess the AI risk.
Is that how you would describe it?
Yeah, absolutely.
Okay.
It's almost impossible to get rid of all risk.
No matter what you're doing, you could be standing in, in your house and
have a fire extinguisher everywhere and not be there when it burns down.
So you can't use your fire extinguisher, right?
it's impossible to eliminate risk.
An asteroid could strike New York City in a year, and there's
nothing we could do about it.
Who knows?
but we do mitigate risk.
And, I love thinking about, risks the way NASA does, right?
So NASA thinks about risk in a grid, right?
Where it's, low, medium, high impact and low, medium, high probability, and
the high probability, high impact risk, well, you better be looking at, right?
Maybe you don't have time to look at all hundred risks that are in your nine boxes,
but you better at least be addressing the high probability, high impact risks.
You know, part of our focus, and, the way I think about it is four pillars, of
risk management for AI: governance, risk management, compliance, and cybersecurity.
And by governance what I mean is the AI's got access to very specific
data, very specific use cases, and then you can assign those AI
agents to specific groups or people.
And the flip side of that is, Copilot, right?
Where it gives you access to everything.
And which is honestly a malicious actor's dream, right?
If they capture your credentials and they get in there pretending to be you,
they can ask copilot questions like, Hey, where's all the customer data?
Or, show me my emails from the CEO, or do I have any access
to any login credentials?
And Copilot will happily answer them within seconds.
And it can really compress the time on day zero that a hacker needs to do something
bad, like, uh, deploy ransomware.
So we, we do the opposite of that very specific things.
So, great example is for a call center where we constructed a knowledge
bot for the call center employees.
They can just type in whatever their question is and get an answer.
What kinds of loans do we offer or what's our policy about late fees or whatever.
And they can get the answer in seconds, even if they're a new employee.
So that's a good example of a very specific agent.
So on the risk management side, a lot of things in the world are obviously
encrypted for very good reason, like our customer databases and things like that.
But in AI, not necessarily, right?
So graph by default, are typically not, encrypted.
So we encrypt everything in motion and at rest, and also encrypt things on the fly.
So it's, if someone happens to type a social security number into a prompt,
we can set it up so either it gets blocked or it gets encrypted before
it goes to the AI or leaves your firm.
And think about the rules in Europe around GDPR, where wow, you better be encrypting
customer data or you're gonna get fined.
I think Amazon had a fine that was almost a billion euros a couple
years ago for GDPR violations.
But also standard stuff that we don't even think about on the internet right
now, like blocking not safe for work topics or keeping people from playing
games at work or whatever, that you need to do that with AI as well.
And then onto regulatory compliance, right?
So lots of rules around AI.
What are you allowed to do?
What are you not allowed to do?
Making sure AI is not doing stuff, it's not supposed to.
And in the US a lot of that revolves around just keeping records of what
you did with AI, especially in finance.
So we have create an immutable database, keep track of every prompt,
every response, every model change, and provide an e-discovery tool for
regulators or compliance people to go in and see, Hey, what are people
doing without violating privacy rules?
And then finally, cybersecurity where we really advocate for private AI.
And what I mean by that is deploying the models on your own computers
on-prem or on your private cloud.
So when they're running, you don't have to worry that your secret
data is leaking out somewhere.
And in addition, cybersecurity beyond that is looking for
different kinds of attacks on ai.
So there I'm talking about, the number one risk from OWASP on
gen AI is prompt injections.
And beyond that, there are things like Do Anything Now or
DAN-style attacks and Skeleton Key attacks and multi-hot attacks.
And all these things today sound like gibberish, but they will be on the front
pages in the next year or two, right?
As large companies get their AI hacked, ransomware, whatever, and
people will learn about these things.
Don't learn too late, right?
You wanna learn about what they are now and defend your
company or yourself against 'em.
and that's one of the things that we do.
And you mentioned at the beginning that you are approaching this from
like a platform point of view.
So I guess by the time that you're engaged, you're talking to
them, they've already done some things and there's already a bit
of a technical or security debt.
Is that right?
That can totally happen, right?
the most common thing we see is companies either completely blocking
all AI or, allowing certain employees to use AI, but making them promise not
to put anything confidential out there.
So no client data, things like that.
Look, human error happens every day, right?
There's a story of someone I know who's, works at a pretty big
company, is on the AI committee.
And on her phone, accidentally used the regular chat GPT instead of
their walled garden version, put confidential information on there
and was suspended from the firm.
This is the person on the AI committee, right?
So obviously you have to take these rules seriously, but the, obviously,
the better way to do that is simply to block all the public versions, at
least on your own on your inside your firewall or on your own in intranet.
And then only present the safe versions, right?
The corporate versions.
The good news is that we can roll out in a day our platform that includes
no-code agent building and this AI GRCC is what we call it, governance,
risk compliance, cybersecurity.
How long does it take to get started to deploy?
Yeah.
It's super fast, right?
We've got dozens of clients and deployment on Azure takes about 14 minutes.
We basically use an ARM template.
And so our software sets up the compliance database.
Then it sets up the models, all the kind of popular large language
models, base models and a bunch of kind of standard agents, depending
on what industry the client is in.
And then they are off to the races.
One of the things I love doing is not just meeting with the person running
technology or the CEO, but meeting the people who work at the company.
We ask each client to come up with a focus group of people from different departments
who are hopefully excited about AI.
And then, what are your big problems?
What do we think we can solve?
And, things that are easy to solve and a high impact we try to do
right away and get some quick wins.
Things that are really hard or LLMs aren't capable of, we just tell them,
Hey, that's doesn't seem possible.
Occasionally, clients ask for things that are either not
legal or unethical, very rarely.
And we just remind them that, Hey, that's not a great idea.
Even if you could do that, you shouldn't try doing that.
and Sometimes it's accidental.
So there's, for example, in the US there are a lot of rules about bias in lending.
So you have to be very careful if an AI agency has anything to do with
lending, that you're it remains unbiased.
And so how do you demonstrate that the implementation that
you've put in place that replaces something else is unbiased enough?
How does that work?
That's a great question.
We do lots of different things.
We create agents for anything from call centers to loan officers
to CEOs to marketing people.
And 95% of the time that there really aren't a whole lot of like
legal constraints of what you're supposed to do or allowed to do.
Sometimes there are, and we do deal with, high risk AI in finance and in healthcare.
So a lot of it depends on the specific use case and also the
guidelines, the AI guidelines set up by the company themselves.
And their legal or compliance team, and we can work with them to make sure that, as
an example, the AI is not accessing data, which could cause it to be biased, right?
We can work with them to help them understand like what data they should
or shouldn't, should not be using when they're doing something specific,
making a loan or, anything like that.
In the end, it's really down to the company, right?
We're providing models, we're providing software.
Using the models and the software and the compliance system, users
still has to do it correctly, right?
If they aren't doing correctly, they're not gonna get the right outcome.
So we automate 95% of that.
It's never gonna be 100% because you're always gonna have AI policies, right?
Which you could implement through a system.
You're gonna have AI procedures, things you wanna do and not
do, which we can facilitate.
But in my opinion, everything always has to come back to a
human at some point, right?
To say yes or no.
Good idea, bad idea.
Does this look right?
Good example.
There is, we have clients that use AI to fill out, proposals, right?
So a company you wanna work for says, wow, we wanna hire you.
Here's our a hundred questions about cybersecurity, right?
And now you've gotta answer those.
Now guess what?
You've probably answered those a hundred times already.
AI can very easily go through the last hundred times you answered that and
answer it for the new questionnaire.
But someone's gotta read it, right?
Because if the AI somehow gives the wrong answer or switches a word
in there to, to claim that you're doing something that you're not, or
vice versa, like that's a problem.
So that's why for anything like that, you're gonna want a human in the loop
to make sure that everything's correct.
Having answered a number of proposals myself manually over
the years, I also think that it's the same problem with humans.
When you have a sales team responding to a number of requests, you also have a bit
of an issue where it's very hard to ensure to, to have every proposal go through
both like the technical feasibility, validation by somebody else and the legal
that the, from the legal department that you're not promising something that you
won't be able to deliver, et cetera.
And so you have to, be pragmatic sometimes.
How's that different when you are using generative AI?
Yeah, I think it's interesting 'cause you can really do, two things.
One is you can use a mixture of experts, right?
So you're not just answering the tech questions, you can answer the legal
questions or portfolio manager questions or whatever, all from the same kind
of database effectively or suite of questions you've answered before.
The other thing that's interesting is you can create review agents, which go
through it and can flag things that look wrong or things you're not supposed to do.
Like you've heard the phrase, I'm sure in finance before, past performance is
no guarantee of future results, right?
So you could go through there, go through something and say, let's make sure
we're not guaranteeing results, right?
So you could double check things with AI as well.
That's a great point because the first thing that comes to mind,
just because it's such a burdensome task, is to automate the responses.
Writing the answer to the questions, and then having humans validate this.
But what you just mentioned sounds even more appealing, is it doesn't matter
who wrote it, a human or a generative AI, or a combination of both or they
work working together, having agents that you've initialized so that they are
looking for specific types of issues, that's a tool I only wish I had every time
I was writing one of those responses to give myself like a bit of a safety net.
Yeah, absolutely.
Very, very doable now.
It's really, it's obviously industry specific.
Industry has its own things they need to watch out for.
But I agree.
It's gonna be a fabulous tool.
And look, uh, at a company I worked before, there were literally eight people.
This is their full-time job to respond to these.
One senior person and seven pretty junior people.
And we basically had to promise them, you're only gonna have to do this for two
years, and then you get to do a real job.
Because it was really not very fun.
And the senior person's job was just making sure stuff was correct.
Because the junior people would make mistakes sometimes.
Now they're starting to use AI there, and I think it's just
gonna get better and better.
AI is certainly transforming business, but as Alec highlighted, success isn't
just about adopting the latest tools.
It's about balancing innovation with security, governance, and compliance.
Not every gen AI use case is worth pursuing, and smart companies
focus on strategy, not just hype.
If today's discussion got you thinking about artificial intelligence in
your organization, we can check out a hands-on demo of the platform
mentioned by Alec in the interview.
The link is in the description.
And we're not done yet.
We'll continue the conversation with Alec next time and explore
the features and deployment of this platform as well as broader
regulatory and ethical considerations.
There were too many topics to cover with Alec in one episode, so stay
tuned for more tales of innovation that inspire, challenge and transform.
Until next time, peace.
Thanks for tuning in to Innovation Tales.
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