Imagine waking up tomorrow to discover a thousand AI agents are making
decisions across your company and nobody knows exactly what
they're accessing. Sound dramatic? Perhaps
impossible? Not anymore.
Hello and welcome back to Data Driven, the podcast. We explore the
emergent and industry of AI
data science. And of course, without the underpinnings of data
engineering, none of this would be possible. And with me today, I'm very
happy to once again have my favoritest data engineer in the world, Andy
Leonard. How's it going, Andy? It's going well, Frank. How are you doing?
I'm doing all right. I realized that this is no longer an emerging industry
after, what, 10 seasons? Now we're in season 10, and
pretty wild journey this has been. And I'm sure
by the time you're hearing this, we already launched our 10 season
10 retrospective and looking to the future episodes. I won't bore you
again with that. Today we have a special treat. We
have Ron Longo, who is the CEO of Trustlogix,
and I love the marketing material.
This is your AI control plane with a kill switch.
So welcome to the show, Ron. Great to be here. Thanks so much for having
me. This is what a control plane is, particularly around AI a little differently. So
what in your mind is an AI control plane? So from
our perspective, there's security, right? You want to keep AI
secure, and there's a variety of ways of doing it. And if you look at
the typical guardrails, they're making sure that there's not prompt injections.
There's no problems with the actual LLM, but what we focus
on is the data, because, I mean, that's the fuel
of AI. And so we basically put in place
a control, an access control plane.
So we ensure that no matter if it's an agent, a human,
a non human, if it's accessing multiple data sources, we just
ensure least privilege. And. And we can do that
in runtime. We can provide full auditability.
And as I think you read in that same description, we also
apply a kill switch because if you think about it, what happens if you.
Your agents run amok? You have to be able to control
them. And so we've introduced this concept of a kill switch just in
case something happens. So kill switch
tends to be a loaded word. This isn't like a
Skynet kill switch, right? Like, how dramatic is the kill switch? Is it
just a process kill request, or is it. What happens. Do you
reset the entire state? Run what. What exactly
happens when you press the emergency button? That's a great question.
So it's proportional to the risk. Right. So if it's a nominal risk
and a particular agent, and you can isolate the agent and
it's accessing data, it shouldn't be accessing. The kill switch can be something
as simple as, hey, we're going to force data masking or we're going to revoke
privileges for that particular agent. But if you've got
a massive threat, something that's actually going to take down your
systems, your SaaS service, or it's going to have access
across a broad swath of data sources, we have the
ability to basically block all of those, all of the agents.
And then once you at least eliminate the major threat, then you
have time to basically step back and say, okay, is it this
particular data source or is it this particular group?
And then you can basically allow the rest of the agents to operate
normally. But how you would look at it is it could either be
a pause, it could be a revocation, it could be data masking.
But the point is you gotta keep the data safe. And if you think about
there was this recent breach, I think it was anthropic
and it took them hours to isolate it. Enterprises don't
have hours. And so we introduced something that you can do at runtime.
Nice. Nice. Yeah. With the speed of bandwidth being
what it is, hours is. You could. How many gigs can be offloaded in
that period of time? Exactly, exactly. An
alarming amount.
Most enterprises won. Sounds like a
great idea though. Ron, I was going to ask, is there some
particular story, client, friend, some
news story you read that inspired this? It was a
couple of things actually. It was our founder, Ganesh Kirti actually had
read about that anthropic breach and
he then wrote a blog that followed after that happened. And
he said, look, what needs to be put in place is this concept of effectively
of a kill switch. Well, we happen to have that in our product.
As marketing got together with the founder, we said, hey, let's make that front and
center. We here's the reality in the world of
AI, it is so noisy, right? Just walk through rsa.
Or we've got Databricks Summit coming up, Snowflake Summit coming up.
What is everyone gonna do going to talk about? It's all about AI
and it's so confusing. And when we talk to our customers, which are
the some of the largest enterprise customers on the planet, they look, they come to
us and they say, look, we don't know what everybody's doing
because it all starts to sound the same. And what I wanted to
do and what I've asked the marketing team to do is look, we've got to
punch above the noise threshold and we have to make sure that we have a
message that resonates that people can look at. And, and it's very simple to
understand. And that concept of kill switch and that came from
our founders and from our marketing team was hey guys, that makes a lot of
sense because. And it also came from
an actual customer that came to us and said our CEO
wanted to develop an agent and run it and we had to tell him no.
And by the way, we've got a thousand agents, so we don't really know what
they're accessing. Can you help us? And when that came up,
we knew that we had to jump in and we had to offer things like
that access control plane. But also the concept of a kill switch
for protection, that makes sense.
So you talk about in that same thing, right underneath Kill Switch,
it says govern every agent, every mcp, every
tool and every data request. Right? Now speaking,
that really doubles down on the noise, Right. The noise of AI has gotten
ridiculous. Yeah. So how do you, would you
govern an agent? The same way as you would govern
a MCP server data request, things like that.
How does your platform, does your platform treat them the same or
does it treat them differently based on what they are? Yeah, so
that, that's a great question. And you have to treat them based on their
purpose, based on their intent. You can't treat
everybody the same. And so that's one, one of our capabilities,
right. So we're at the data layer, so we' we basically
ingest all the policies and we understand what the
agent's purpose is. We, we understand what the least
privileges are that the user that's accessing that particular
agent because the agents have broader access, right.
Service level access into multiple data sources. And
just by the very nature of the fact that they're there to basically help and
optimize. But the users may have privileges just to
get to one particular data source or one type of data. Maybe they're the financial
analyst and they can't get access to HR data. So we
treat that differently, right. We treat the intent and the
capability and the access privileges that particular
user has while, whether it's human or non human, while
it's accessing that agent or those MCP tools.
And all of that is effectively follows the concept of
least privilege all the way through the stack. Whether it's
agent mcp, whether it's agent orchestration
layer or data source. We have to make sure. That least
privilege is basically enforced all the way down to the data source.
Is that what the protocols was? A Spiff Inspire or something like that?
Oh, now you're getting into an area that. Okay, I'm just curious because
I. It was a non technical. Oh, okay. Don't
worry. I only heard about Spiff and Fire two weeks ago and I
know I'm mispronouncing them, but it's basically protocols that basically
immediately based on certain flags will immediately revoke an
agent's privileges or an agent has to request to do something and then
that window opens and then as soon as the task is done, that window
closes. Right. So you have really fine grain control. Andy's
nodding his head like he's heard of these protocols, but I don't know.
I've heard the terms, but I like you, I haven't dug into them. I've got
about what you got out of it, Frank. That. Okay. The idea and
what you're saying, Ron too. Governing the principle of least
privilege. And that sounds like a fantastic idea.
I'm not experienced with any of this type of automation in
practice, although I do interact with AI
genic AIs often. I'm not familiar with how this
works in practice. I was following your earlier explanations with
Frank there. It sounds good, I'll say that part.
It also sounds like it might present a bit of an obstacle to
the YOLO lifestyles that some AI
assistant developers are used to. Just tripping that
bit and having it just go. I imagine some of the
horror stories are related, but I don't know for sure. And you only
get what you get, right? Yeah, I think this is
definitely addresses that one issue because that is
prevalent and that goes back to the story I was telling earlier about the company
that came to us and said, we've got a ousand agents, we're AI first. However,
we've got to put some sort of control in place. And so that's why we
introduced this concept of registering the agents and making
sure that those agents behave. The Spiff
Inspire. While I don't. I'm not feeling familiar with those terms, I'm definitely
familiar with Just in time. We are. We do support that because you want
to be able to support time restricted privileges and then be able to
revoke that privilege over. And we're going to see that more and more
because agents are developed for very specific purposes. You may
launch this for a week, a day, an hour, and then you want to revoke
it and so that we fully support that. Yeah. And
it's funny because that's an old. That that whole it's an old thing, right? Open
the window, close the window. All these security best practices, this isn't new.
And I think speaking to your point about AI noise, right. Everything
old is new again, Right. The fundamentals are there. Right. I have a lot
of debates in the same meeting I was at where they were talking about.
And it's actually spiffy. S, P, I, F, F, E.
And the other one is spire. That's like spire, like a cathedral
spire or something like that. But it also. People, I think, are
realizing now that the AI hype wave, it's not crashed, but
we're definitely towards the crest of it. People are realizing as they
operationalize these workloads that fundamentals matter.
Fundamentals are boring, but they do matter. And like this whole
least privilege and things like that, rotating keys and all of that is,
is. It's everything old is new again, right?
Yeah. The more things change, the more they
stay the same. That's right. And it's the reality.
Yeah. And it's funny because I find myself using
more, more often in my career. I'm using the phrase garbage in,
garbage out than I have in 20 years.
Because if you put garbage into an AI, you're going to get
garbage out. And all that goes from the data it was trained on. Right.
All the way through the prompt that you give it. I was talking with someone
who, he recently lost his job and he's not an AI guy and he's
very, very stuck in a previous era. That's what I'll say. And
he was saying like, oh, when I asked the AI questions or to help me
with my resum. It gives me really a bunch of text that doesn't make
sense. And he said, well, what do you expect from AI? And I turned to
him, I'm like, Dude, this isn't 2023 anymore. What did you
prompt it? What did you prompt it? He goes, I just said, give me your
resume. No, no, no, no. You have to really spike the prompt
or not. That sounds bad, like prompt injection. But the
more information you give the AI going in, the
better quality you're going to go out. Right. It's kind of like talking to my
kids. Right. If I ask them how their day was, they'll say, fine, but if
I ask them what was your favorite part of your day? Right.
Find is not a good answer to that. Right. They'll have to answer me with
something more. Right. So it's like being a parent almost prepares you to be A
prompt engineer. Yeah, you have to architect and prompt engineering is a great way to
put it. You have to architect the prompt. And the problem with AI, I think,
and totally to your point, garbage in, garbage out. But they speak
with such authority, so much confidence
that you tend to believe what it says even though it's just delivering
back to you garbage. And that is one of the, like
you said, old principles apply, best practices apply.
Here's the difference, here's the foundational difference. And not
to date myself, but I've been through the broadband era,
the Internet era, the cloud era, the SaaS era. So I've been
part of, I've been fortunate to be part of companies that have driven these
sea changes. This sea change, I think despite
some of the hype, is significant and one of
the biggest issues that makes a difference is the fact that
it's running at machine speed. And you can run with a huge
number of these things. Just picture 10,000 agents
or even a thousand agents supporting at least that many
users or non human users running machine
speed. So not only do you have to apply best practices,
but you have to apply best practices very quickly. And
so that's one of the key differences in terms of
how do you have to evolve best practices from yesterday
to meet the demands of today. Yeah, and that
actually calls back to maybe two or three episodes ago depending on what
order releases in where we talked about the neuro resilient leader
where basically AI has basically made it. So the hustle culture has to
change because you said it machine speed, Machine speed
changes everything. Right. I don't care how fast you could run, you're
not going to outrun a Camaro. Right. Like it's really that
mentality, it's just not going to happen. Or I think Andy's a Dodge
fan, so whatever that Dodge Challengers, Dodge Challenger,
you're never going to outrun it. Right, but, but to your point, the
governance I think also like you said, has to happen at that, that, that
speed. So I don't know what the exact Latin
phrase is, but it's something like who watches the watchers? What's your gov like?
Who watches the governance on that you provide here? Is that something the customer
decides or is that built into your product? Yeah, I
want to make sure I understand the question. Obviously we apply
enforcement and we have to apply enforcement at machine
speed, but there's always going to be a human enforcement, the
middle. If you think about just the fact that AI
can hallucinate, things can happen. Going back to the kill
switch concept, Obviously we're not going to automate that. We have
the capability of automating that. But I don't think any enterprise in their right
mind is going to say if something happens, automatically kill
everything. So there has to be someone that's actually overseeing this,
but you can't turn it into a human speed
action. Right? Say we're going to display all this information. You got to read
through this information and make a decision who you're going to, what agents
are you going to revoke. It has to basically be a single
button. And that's what you have to simplify. You have to give them the
facts and then you have to say, look, we've discovered a
breach or someone's trying to get access to data as we narrowed it down as
an example to your S3 buckets in this particular, for this
particular group, then you can make a decision. I'm going to, I'm going to kill
that group of agents. But back to your question of
who's governing the governance. How our solution
works is we're running the enforcement at machine speed,
but everything is auditable. So we track all of this, provide the
logs, and then we provide the audit trail. And you have to do
that because what a customer is requiring
is it needs that audit trail down to the agent, down
to the user that's accessing the agent. Right. If you're just looking at the
agent that's got broad access to everything, you're missing all of the
access to that agent. So we have to provide that level of
auditability so that the enterprises can govern.
Okay, that makes sense. So you're not really. The human is in the loop, but
the human has a much more powerful hammer to, to break the glass
and stop the process line. Cool. If you think about how enterprises
have historically worked. So the problem that we solved, making it really
simple, enterprises have lots of data sources.
Anyone that says, oh no, I've got everything in one data
platform, so they're lying to you. That, that just doesn't exist.
Right, because they've got on prem Oracle SQL servers, they've
got S3 buckets. And they may, their aspiration
may be to put everything into a single data lake or a single
data source, but that's not how it is today. And so what we do is
we apply this unified control plane
across all those data sources so that you can manage all of
that simply from a single pane of glass. How it's done today,
customers will say, we solved that problem. We have JIRA tickets,
we've got folks that are running, doing Python scripting and SQL
and we've solved that problem, but they solved it at human
speed. Now all of a sudden you start adding in agents that are accessing those
data sources. That process is broken. And
so that's what we do. We basically take that manual human speed process
and we apply this unified control plane to basically
match the human speed requirement. Match the machine speed requirement.
Interesting. Yes. My question, and I
think Frank and I have been dancing around this, but I don't know if we
understand. And the answer may very well be it depends. One of
my favorite answers as a consultant, but the actual
trigger of what sets it off. So I've heard part of the answer
and I believe part of the answer is definitely human in the loop,
perhaps human in control. And then I've heard, then I've
heard machine speed. And I feel like there's, there's some
piece in the middle there. And again the answer may very well be it
depends. Is it? When Frank asked about who's watching the watcher
is the watcher. If the watcher is an AI,
then certainly that becomes the actuator, if
you will, of the kill switch, the actor that works on that. And
I could see that being a very important piece. If there's
some signal received or some pattern detected
that looks like a hack. Let's say you're,
it's two weeks ago and you're sitting in the dock at Canvas.
Okay. I have a daughter at the computer science program
at Virginia Tech and it's two days before exams and
she can't get to her material to study. That happened.
So if you're there and you're watching these
various signals pop up I monitoring
that that sort of system that has access to a kill switch
would have come in handy if data had been exfilt or if
heuristics detection was occurring and see a network
bombardment, denial of service or however
Trojans were making their way into the space behind
a firewall. If you got to rely on a human to do that,
then that could be a problem. And again, the answer may
be for Canvas or a scenario like that in the
future. Your product is sitting there and it is automated.
There is an AI with its virtual hand over hovering over
the virtual switch that does the killing. It may be for
another, another use case and I want venture to guess which
where that may still be there. But a human
has to make that final call. That's kind of what it is.
But if I can interject. Yeah, but if I can. I'm sorry, I Cut you
off. Andy, if I can interject and Ron can tell me if I'm off base
or on base, is that because enterprise systems
are so spread out and disparate across multiple systems? And that's not really
a new thing, it's just gotten worse. So I may have a dependency
in Aw bucket, I may have something in Azure SQL, I may have
something on Prem, I may have something God knows where else.
Right this way. I have a kill switch to kill agents X, Y
and Z and their system, correct me if I'm
wrong, would know. Oh, if you press that button, I know where all the
bodies are buried, so to speak. That's a terrible analogy, sorry about that.
But it knows where all the bad. Makes sense. But it knows
all the things it needs to shut down as opposed to if you're a sysadmin
and you get paged at 2 in the morning, you're like oh, I gotta do
this or I have to pull up a document and things like this. Whereas this
way I just say kill this process. It's kind of like task
manager. Right. But it kills all the related pattern. Yeah. Is that kind
of what. Let's back up a little bit because we were talking,
we were talking best practices. So when we talking about machine speed or
run, keep in mind that this would be
for access controls and access control governance of the
agents, right? So if you've got 100 agents or a
thousand agents and they're all accessing different pieces of data
based on the user, that's what we're providing basically at
runtime. We're doing all of that at runtime and ensuring
that you've got least privilege, which is a task. Right. If you think
about thousand agents accessing multiple data sources.
But because we have that automatically in
the system and we have all those agents are registered,
the kill switch portion of it is exactly what you said, Frank, is that
we have that visibility and we can then say, okay, wait a minute,
it's all the agents that are accessing the
databricks data sources. So let's, let's basically
isolate those particular agents, let's revoke them, let's
block them. And we can do that very quickly because
of this concept of this unified control plane. Let's look
at what happens if you didn't have a unified control plane. Right.
You have to have experts on data bricks, right? Access controls for databricks.
You'd have experts that understand snowflake experts that understand
S3, the databases. And if this
happened to be an agent that was accessing multiple data sources.
You'd have to coordinate between multiple experts to do
this. And that's what causes the hour long times, the
JIRA tickets going back and forth, the humans talking on the phone
versus I have a unified control plane that has the ability
to control all of this support across those data sources.
Press this. And I don't want to oversimplify, but press this
button. Yeah, yeah, that makes sense. Right? Because things, when things break or they go
sideways, they never happen during convenient business hours. Right. At
three in the morning, I'm like, oh God, do I have to call the databricks
admin or. Oh, I've been in jobs where I
did have a sheet. We would have a binder. It'd be like if something breaks,
you had to do your initial first cut, obviously years before AI, and
you'd be like, all right. And then you go down the list. The further down
you go to list, the less you really want to call those people.
Yeah. So if you can automate away a lot of that, and
I think that that's really the power I see is that these dependencies
can be embedded in your product and then the humans make the decision to
kill. No kill. But the triaging and the troubleshooting can,
like, you know, wait till the dawn breaks and then the next day people
can do this as opposed to after the data goes out. Yeah.
So because the kill switch is actually an artifact of the
unified control plane, it wasn't that we said, hey, let's build a kill switch and
let's build all the scaffolding behind it to be able to do that. No, we
started off with the basically best practices as a, how do you
unify access control, whether human or non human,
across this? And then the artifact of this was, hey, we have this
centralized control plane that we can isolate
and revoke. And so it's just an artifact of
the, I think of the benefit of having a unified control plane.
Oh, okay. That makes sense that. Because when this
doing some OSINT, as the coolest kids would say on
Trustlogic. Trustlogic has been around since 2020ish.
Right. So clearly it really was not a twinkle in Sam Altman's eye, so to
speak. But so it sounds like this originally was
a control plane setup and then you've pit. I think it's brilliant because I think
it's exactly what the market needs. Because I've been in a lot of
conversations of late where if they have agents,
they haven't. They say they have governance, but you really only know
if you have governance when something hits the fan, right? Yeah,
everyone's looking at the fan. And you did make a great
comment in terms of. We were around in 2020, we were providing
services to these large, large banks, large pharmaceutical companies
that have foundationally this messy underlying
data ecosystem. Right. And our founders are all former Oracle
folks. They understand these messy eco data environments. And so
we solved that problem with this unified control plane. And
naturally, as you. What is an agent? An agent is just a
much faster, algorithmic, controlled thing that
can act like a human. Now you got thousands of them and they act
much faster. So it's a natural extension of
hey, let's apply this to agents. I will say
this, that there are a number of AI companies and we've heard this, and I
don't mean to disparage these AI companies that are just not being form,
but we were talking to a large hospital and they came to us and said,
hey, look, we just met with dozens of these companies,
these Y Combinator companies that, that are solving AI
problems, but they don't understand our environment. They want
everything in a single cloud and they can solve that problem really well
and very quickly and they understand the models, et cetera, but
they don't understand this messy environment that we have.
And that's one of our advantages. And why I think we've been
successful in introducing our trust AI into these larger enterprises
is that we have that foundational, messy data
ecosystem background. We understand that that's the life
that we live. Interesting. That does
fit. And that explains how the kill switch concept came about.
And I agree with Frank. I think it's pretty cool that
you had a solution that was solving some of these other problems
already. And then all of a sudden AI starts going off the
rails. You're already in the business of detecting,
governing best practices and you found a new use case
for it. That's pretty cool. Yeah, yeah. And it's such a
catalyst right now. Look everywhere you look, these enterprises
know they have to be AI first, but in
doing it, they're discovering things. And the reality is this
is very nascent. And I'd love to say that we have
every answer. The trustlogix knows what's going to happen.
But even in this conversation you brought up this concept of why not
have an AI bot that's doing some level of control and
triaging. These are things I think that will evolve with solutions
like ours. One of the things that I
really enjoy about this job is that
every day is a new day. Right. I used to be
in networking. I worked for the phone company and it was pretty rough. You go
off, put a telephone pole there, hang this cable
and every day you knew what was going to happen in
this world. Every day changes.
When I was at Fortanix was right when LLMs
came out and then what was the next thing rag. Oh shoot,
we gotta figure out how to do rag. Next thing you know is agentic.
Things have changed so rapidly that
it makes every day exciting. And that's what I enjoy about
being in this particular. At this particular time,
in this particular startup, addressing these particular problems.
Because you don't know what's around the corner. You really don't. And
anyone that says they do is lying to you. Right. And you can make
good guesses but. And kind of educated guesses and be
directionally. Right. But no. And another thing you brought up, a lot of this really
goes back to the fundamentals, right. Tried and tested enterprise tech
and best practices that we people really
don't know how important they are until they try to make something
an agent. Right. People don't realize how bad their data is until they try to
wire up an AI to it. Right. Oh, because I think maybe it's the machine
speed makes you see the inadequacies pretty
quickly. Right. Using the car analogy, the
Dodge Challenger, right. You don't know the Dodge Challenger has a problem until you're
going maybe a little faster than the speed limit than you really know.
Maybe they don't make carburetors anymore, but you really don't know
what's wrong with your system until you. You're going until it's up and
going. I think that's the same with enterprise tech and data particularly. This
is why I always make a big deal to talk about data engineering as part
of this podcast is it's foundational to this.
I have a slide deck and I've talked about Maslow's hierarchy of needs. But for
AI, right? AI is the very top. And I would say agentic AI
is probably now the top layer, but at the bottom of it
we have power, networking and infrastructure. The former networking guy, right.
That's important. But also kind of like a big middle part is
the data engineering and kind of just your best practices
there. And I've seen organizations, both government and private,
you really get to see how well the. How well the dish is put
together when we start AI ing stuff because then it really
exposes, I think the inaccurate pretty quickly. Yeah.
Never heard anyone explain that using Maslov's
hierarchy of needs. And if it's okay, I'm going to borrow that. Go ahead, use
Ali. I'll even use the slide. Just give a shout out to the podcast.
That's all I ask. I absolutely will. Great example. And
I know it's cliche now and they'll say if you don't have a data strategy,
you don't have an AI strategy, but it's absolutely the truth. You've got
this pyramid of which you've got these foundational
principles that all lead up to now I can deploy my
AI. A, that it's going to work and B,
it's going to be secure. The thing that you just brought up in terms of
pressure testing the system, part of it is pressure testing the
technology and the process. Right. Do you have a
run a run speed process that can handle these?
But the other thing is the knowledge. Right. It comes back to your, the
point that you made of the guy that wrote the resume. He just didn't know
how to prompt the LLM correctly.
When people say AI is going to do away with all these jobs, the
reality is you have this level of tribal knowledge and this level
of human experience that AI just doesn't have. It
doesn't have that context. And so it's going to require
somebody that actually knows what the heck they're doing to be able to do that
prompt engineering. You just can't get somebody that was a journalist
major out of college to come in and say, okay, you're really good with words.
Go make some genetic testing or some clinical trial. I'll
LLM, no, no way. It's going to require an actual
a doctor or a researcher to, to foundationally develop
that LLM or that AI model to where it can do what it needs to
do. Absolutely. I also think that
you mentioned the journalist. Right. I think people who study
language, whether they're literature majors, journalism
majors, I think they actually have a unique position
in terms of writing, their writing ability. Assuming they don't entirely rely
on AI, they understand the nuance of language in ways that very few people can.
So I think lawyers in particular also might have an advantage in being.
I don't want to say that they would make great prompt engineers because I think
that really undersells the, the ability. But you know where I'm going with that,
right? Anyone who has understanding about linguistic
nuances, whether that's for artistic purposes or legal purposes,
I think has a unique advantage
over normies who don't think about language in that way.
Percent agree with that. And again,
back to the point I was making, I think someone that's great at
language link, linguistics, etc. They'll be great
at figuring out how to make this prompt and shape the
prompt such that it gets you the desired outcome. They
just need to get that experience with what exactly
are they writing that prompt for. But you could imagine that someone
that's good with words, but also has a kind of a technical brain
or a creative brain, they'll be able to. Things are going
to be developed that are way beyond what we're
envisioning today. And that's what I think is so exciting
about AI. For the first time, anybody can actually
be an application developer now. It's not that
everybody will be, but anybody can be.
And if you're exceptionally creative and you're good with words and
articulating things and formulating ideas, think
about the types of applications that can be built. You don't have to
know C and Python and this and that. You're not
trapped with the scaffolding that you have to know now
you can just let your brain run free and you can start to
ideate. Is that the right word? You can
conceptualize these things. And this
happened to me, right? I was telling Kim, I was trying to figure out how
can I create someone to look at my blogs
and see if they're reasonable blogs for could be published
in EE Times or whatever. And so I went into Claude and I
said, hey, can you help me create folks that can critique
my blog? And it literally came back with an app.
Is that nice? Here's your app. Upload your document
here, and here's seven Personas, one from EE Times, one from Harvard
Business Review, one from LinkedIn, and it will critique this.
And so I thought, wow, this is cool. So I refined that app to
where now I just upload my blog and I get 10 different reviews and it
says, here's the red line of where they think your blog
doesn't pass muster. And I was like, I didn't even go in
intending to build an app, but I did. I said, look what I did. I
built an app. No, it's very powerful, right? It's very powerful
in terms of really leveraging AI, particularly if you have them do
the actor critic kind of approach and you can iterate really quickly.
I have a Claude skill that will I give it a presentation,
Here's a presentation, make it more, or I get
a template for an event, whether that's Red Hat Summit, Databricks Summit, and I'll
be like, here's a template. Convert this and it does it now. Could
I have done that? Yeah, I could have done that. I can do that. I
can go get a cup of coffee, I can walk the dogs while it does
that. It's that type of thing. We also have techniques where
I actually wrote a command line tool a couple of years ago
called Dingo and been wanting to get more
people to use it because I find it very useful. It's a command line tool
written in originally.net
and then I migrated it to Python and then not everyone's into
command line tooling. So I actually have now a
process where we are going to make it like a SaaS, right? Where it basically
you could train it to write like you, similar to what you did like with
the critics. And that's basically a similar thing. If folks want to check it out,
you go to thedingo AI. The Dingo
AI. The Dingo AI. And we're going to launch
out public beta soon, hopefully by the time this goes live. And
it's actually named after one of my dogs who looks like a Dingo. There you
go, There you go. But you think you're onto something, right? You have, you
have the ability now to take an idea and then get it to
where you need to go. Now a lot of the critics of
vibe coding, which is, you know, is that. Will that scale to millions of
users? Do you need it to scale to millions of users? Right?
You built it for yourself, right? You have a tool,
you're obviously a CEO of a tech company, you can probably find a way to
make it scale a million. But do you
need to? You don't. But I think to your point earlier
about you still need the human experience, the
human capability to basically pressure test it and to
make it scale and make it enterprise grade and.
But I do think it speeds up the process if you look at the
amount of time savings, grab a cup of coffee while it's doing a lot
of the mundane work, but you come back, you look at it, you're
validating it, but you've saved so much time. But
back to the foundational aspect of AI and why I
currently exist at Trustlogix is I think what's
happening is you've got, within enterprises, you have folks that are
discovering this capabilities, ability and I, I can't remember
if Andy, if you had mentioned it or. But you've got folks that are used
to now doing anything they want
and bypassing the governance, bypassing the security. Because look,
I'm about to develop something and this is the most important thing for me to
do. That's a problem when you get when you're in an enterprise
environment, that's where we're trying to apply that
protection level that says, hey, we don't want to cramp your style.
We want you to be able to develop as fast as you want. Just register
your agent and that will take care of everything for
you. It'll make sure that the data is secure. It'll make sure you don't go
to access to data that you shouldn't be accessing. So we're trying to do
it in a very non intrusive way so that
people can be creative and develop those applications and do it
quickly without having to manually go in
and override things. We're trying to, we're trying to make it very non
intrusive. An AI sandbox, if you will.
Yeah, these are great ideas,
this concept, the sandbox. But as let's just
apply the governance layer. Make it very easy for whether you're an
engineer or whether you're a data analytics person. Just make
it easy so that you don't have to think about it, you don't have to
create a JIRA ticket and ask permission. We just make sure that least
privileges is applied. But this concept of a sandbox,
that's interesting. I'm getting lots of ideas on it anytime, come back whenever
you need more ideas. But, but also too, I think what you're providing is
the really the sweet spot of innovation and safety. Right?
Yeah. And I think that is sorely needed in this world. Right.
You look at the yolo. Is that what it's called, yolo or is that what
they. You look at openclaw. Right. And I have an openclaw instance and things like
that. And it was intentionally made without security in mind. So we can do
anything it wanted to. Now there are times when,
hey, that's fun. You have a little home lab, you're messing around, it tells you
for me, I have it tell me the pollen report and the weather every day
and then at the end of the day it gives me all the AI news
stories, right. I don't have it hooked up to my email. I don't have it
hooked up to my home, my bank account.
I'm okay in that sense. But I would love to have it read my email.
Right. You see this little thing there? That's the data. That little lamp there, for
those who are listening is a, a lamp I just picked up on Amazon
and I actually am going to vibe code it. Whatever stocks I'm tracking that day,
if it goes up, it'll turn green when it goes up and then it'll turn
red when it goes down. Just a little fun thing to do. Right. And I
get, I Vibe coded that, right? There's no published SDK for that. There is some
guy on GitHub who has a similar product that he's kind of reverse
engineered it. But I basically have the AI while we're talking
is going and hitting the endpoint because it gets an
IP address and then messing around with it and seeing what it does. And it
basically will ask me, hey, did it blink this color? Did it do that? These
are all things I'm doing while I'm doing something of higher value, which is talking
to you. Right. And this is something that I think is really the power of
this AI coding. Yeah, absolutely. One thing I
do want to mention. Yeah. We were talking about non intrusive and how do you
do that kind of vibe coding? One of the things that we found and
we were talking to development engineers and when you have to,
we have to stop and you have to put in security. Right?
You have to make specific MCP calls. You have to put certain
data access restrictions. You're doing that at the code level. Right.
And so our concept was how can we shift left and
give engineers a simple MCP call, One
MCP call that basically then can secure that
entire line of code. So the engineer can sit there and either vibe
code or just develop the agent without having to worry about
making very specific access control calls, MCP
calls and effectively shift left. And so that's how
we're, how we're trying to drastically simplify and make it
non intrusive for a development engineer that frankly doesn't give a crap about
security. They're just trying to get their job. They just want their lamp to turn
green or red, depending on the stock. And so that's our
mindset, is making sure it's non intrusive. No.
And that's honestly the best way for security because developers,
despite everything that's happened and if they haven't learned by now, they're not going to
learn. Right. Security is often an afterthought,
Right. I hate to throw fellow developers under the bus,
but it's true, right. My wife works in cybersecurity.
Funny story, actually. Again, this I think ties to the whole Vibe coding thing.
So I have dingo lives on Vercel. Vercel had their breach.
But one of the things that I was thinking in the back of my head
was I should probably have things encrypted at rest on
the credentials database. And I Asked
the, I asked Claude, how long will it take? I'll take an extra 30 minutes.
Like just do it because God forbid there's ever a breach. My wife will never
let me live it down. And fortunately I did. Somebody
asked me hey, or were you affected? I was like no, we had everything encrypted
at rest. Now in the past that would have been more than a 30
minute detour for an agent. Right. It would have been actual work
which as it when I was doing software engineering I
probably would have said well you know what that'll be. I'll put that on the
backlog. I'll deal with that later. Right now you can just kind of. Or if
you really want to get proactive you can have the, you can tell with
the Claude file, tell it to no always enforce security best principles.
So it'll kind of work around that. So I think that despite all the fear,
despite all the loathing, I think vibe coding or
this sort of thing will make software engineering easier
and safer and accessible to a lot more people. Will that
eliminate jobs in the near term? Historically,
automation has grown the economy and grown the job market.
I have faith in the trend line. Yeah, I think there's. It is a sea
change but I think it's just going to change job
descriptions. I think that's what's going to happen. It's going to change job
descriptions and you have to have the AI talent
to stay alive in the new economy. But it
doesn't preclude the fact that you also need to understand
foundational principles. Back to your earlier point, it is
interesting that you had talked about encryption. That was my life for about five years
and the fact that you've made it much easier because it's a
scary subject and a lot of folks are intimidated by encryption
and the concept of post quantum and all that. And you're right,
this can drastically turning it into a 30 minute problem.
That is unbelievable. And the AI and again
encryption is something that does scare a lot of people. Many moons ago I'm way
older than I care to admit but even before I knew Andy and I known
Andy 20 years or more, I was applied for a
small business incentivis sbir like it was sba thing
and it was about detecting steganographic basically
steganography's idea. You hide data and other packets of data.
Oh wow. And it was an RFP for I believe it was the Air force.
And I didn't win the contract but I learned so much about
cryptography and mathematics and how to apply that Actually, in Net,
that opened up so many other doors for me, including
my wife. Actually, I met my wife when we were dating.
We were talking and she. I mentioned steganography and she knew
exactly what steganography was. And I'm like thinking in the back of my head, that's
the woman I want to marry, is the lady who knows who
steganography is. Because nobody knew. Not even techies know what steganography is. Most. Most
normal people don't know that. But you're right, encryption does scare off a lot of
folks, but it's so vital and. And we're getting close to time here. I
want to be respectful. I actually have another podcast called Impact Quantum where we do
talk about quantum computing and PQC post. Quantum computing comes up a
lot because it definitely. For those that really want
the two, an easy introduction to what that
means. The Y files. And we'll put this in the show note. The Y files,
which is an excellent YouTube channel, has a whole show based on. It's
a little dramatically punched up, but it has a whole thing of what would happen
when Q day happens. The idea that a quantum computer could break all conventional
encryption and the proverbial, you don't know what hits the fan.
It's an interesting thing. It's a little overly dramatic, but it's not
that far off from the truth. Just add AI plus
quantum computing together. Kaboom, man. There's your
next. Yeah, there's your next thing. So I put. I'll put the graphic
in the show notes, but in the comments, I did paste the slide of
Maslow's hierarchy of needs where the AI hierarchy of AI needs.
So feel free to use that. And where can folks find out more about you
and what you're up to? Yeah, so just go to TrustLogix
AI and we are introducing new products at
the speed of AI. But definitely. And then join us at
Snowflake Summit. Join us at Databrick Summit. We'll be there.
I'm hosting, folks. I'm actually sitting on my boat on the
bay. I'll be hosting customers on Monday and
Tuesday. And then also at Databricks, we take folks out.
We have fine wine and hors d' oeuvres, and then we talk about the latest
trends in AI and in data governance. Nice.
That sounds cool. Yeah. And with that, I'll let the outro music
play. That was great. That was great,
man. That was awesome.