April 29, 2025 • 29 mins
AI agents equipped with computer use capabilities will transform the cybersecurity landscape within the next year, shifting from augmenting to potentially replacing human SOC analysts with systems that can perform 100% alert triage. The investment landscape reflects this shift, with 78% of venture capital reportedly flowing into AI companies despite many firms simply adopting AI terminology without substantive implementation.
• Computer use abilities allowing AI to operate systems like humans will be the next major advancement
• Within 12 months, expert AI agents will function like "super employees" in security operations
• Ephemeral AI agents that complete specific tasks before dissolving enable unprecedented workforce elasticity
• Traditional valuation metrics based on headcount are becoming obsolete as AI reduces staffing requirements
• Companies running operations with 75%+ AI support can scale without proportional employee growth
• The MSSP community appears slow to adopt AI capabilities despite clear operational benefits
• AI systems will increasingly handle complete alert triage, potentially displacing human analysts
• Vendors typically avoid discussing workforce displacement, focusing instead on productivity gains
• Open-source AI innovations are accelerating development cycles across the industry
Innovations in AI security are happening rapidly. Follow the speakers on social media to stay updated - Randy Blasik (@BlasikRandy on Twitter and Compliance Aid on LinkedIn), Richard Stiennon (@Stiennon on Twitter and LinkedIn), and Josh Bruyning on LinkedIn.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to another episode of Cybernomics.
I'm your host, josh Bruning,and I'm here with Richard
Steenan and a special guest, butI guess if you've been on the
show more than twice, then Iguess you're not a guest Now.
You're just a co-host.
Randy Blasick, I've had so manyinteresting conversations with
(00:21):
both of you offline.
I've known you for some time and, randy, you in the world of AI
and agents with compliance, aidand the things you're doing in
the AI realm.
I mean, you're like my go toperson if I want to know what's
going on with the LLMs, how itapplies to solving problems in
cyber, solving problems ingeneral.
(00:43):
It's great to do online what wedo offline so often and,
pairing that with Richard, justa comprehensive understanding of
the cybersecurity vendor space,the marketplace when it comes
to investments in AI.
This is something that you wereheavily invested in yourself,
(01:07):
and so it's very interesting toput these two things together a
broad market perspective, abroad understanding of the space
that we occupy, but also anunderstanding of the technical
aspects that might be drivingsome of the predictions that
we've been talking about in daysand weeks past.
So let's start with Randy whatis next for AI, and I'm not
(01:36):
saying like in the distantfuture, but let's say in the
next month.
What are some of the thingsthat you think will change, at
least make some significantimpact on the industry?
Computer use.
Speaker 2 (01:53):
So you've got Manus AI coming out of China.
Openai Operator has been outnow for a few months, if you
look at their iteration cyclehas been out now for a few
months.
If you look at their iterationcycle they're due for some
computer use upgrades and if youlook at the open source
community, we're all gettinginto, you know, some more
(02:15):
advanced computer use stuff.
Speaker 3 (02:18):
By computer use you mean getting the agents to use
your computer.
Speaker 2 (02:23):
Yeah, like if you're a SaaS builder, I'm just going
to build an agent to use yourSaaS app and put you out of
business, or, you know, with myagents, either from the inside
or from the outside, got it Cool.
Speaker 1 (02:36):
And when.
You should know, Richard, whenRandy talks about his agents,
he's talking about his team.
I mean, I joke, but sort of,but not really, that we're
cyborgs and Randy reallyembodies this.
Like when he's talking abouthis agents, they are an
extension of him and I've neverseen someone use agents in such
(02:57):
an honest and effective way.
Yeah, so when he says agents,it's like capital A agents.
Let me paint the picture You'reon the streets of San Francisco
right now, so you know what'sthe word on the street when it
comes to computer usage and sortof how the startup space is
(03:18):
evolving and will evolve in thenear term.
Speaker 3 (03:22):
Yeah, if you're a startup and you don't have an AI
, you don't have to say you areAI.
That's kind of passe and peoplejust roll their eyes when you
say that.
But if you're not leveraging AI, you know, then you're just not
going to make it, period.
Unless your startup is aconsulting firm Great, you know,
(03:43):
there's plenty of room forpeople to consult on helping
with AI.
Or somebody just reached out tome to tell me about their AI
red teaming, where they, I guess, check to see if your AI
applications are well configured, et cetera.
Tons of opportunities like that.
But supposedly last year, 78% ofall venture investment went
(04:05):
into AI companies and those arethe foundational model companies
, right?
Just because they took in somuch money.
So they're definitely suckingthe dry powder is that what they
call it out of the VC worldthat had all this money sitting
around that they were too afraidto invest because Silicon
(04:27):
Valley Bank failed and they justgot scared and went home.
But now they're coming out ofthe woodwork and, just like they
did with the stupid blockchainstuff and some other stupid
stuff network emission controlyou know all the things that
have failed miserably over theyears the DC community just
jumps on.
You know it's like.
It seems like it's lower riskif everybody's doing it, which
(04:50):
we all know is just the oppositeof true.
Speaker 1 (04:53):
Yeah.
Speaker 3 (04:54):
So we're in that state right now.
I imagine that RSA this week,um, that we will see AI
messaging It'll be really hardto filter out.
You know cause, if you thinkabout it, companies like
Darktrace right now part of TomoBravo.
When they were founded, theymade up a story about going to
Cambridge hiring a bunch of PhDsand inventing AI for
(05:18):
cybersecurity, and they say thatconstantly.
You can carefully use LinkedInto search on titles and
backgrounds of people who workat Darktrace.
They have not a single AIperson with a PhD or degree in
AI.
They now have three PhDs.
One's a data scientist.
(05:39):
I forget what the other onedoes a psychologist or something
you know.
It's just like what are youdoing in AI if you don't have a
model that you can train stuffon?
And then how do youdifferentiate between the old
machine learning guys who arejust taking a whole bunch of
data and curve fitting and doingBayesian analysis on it and
(05:59):
coming up with, you know,indicators or percentages that
this is something you shouldlook at versus dumping that data
or bedding that data in a ragor, however, or literally
training you know off the shelfmodel on the data and starting
to use it for hunting, and I'mreally anxious to hear Randy,
(06:20):
because Randy's been in the seatof a SOC person Really
interested to hear hisperception of my.
Take that, yeah, I've talked tosome brilliant people that
really impressed me with theirtalk and their PowerPoints.
Yeah, and even some demos.
It's like wow, that's cool.
It looks just like that thingjust hunted down an alert and
(06:41):
figured out what the root causewas and took remedial action.
But it's just cool right now,even though after I published
that Monday, a bunch of peoplereached out and said we're
already there, man, we've gotlike.
All of our customers are Fortune500 customers, which I assume
are ones that are dipping theirtoe in and say, oh, let's give
(07:02):
that a shot.
You know, it's not expensive totry it, let's just do it.
But my prediction is by the endof the year it will be real and
you will, you know, be able toeasily see the ROI in terms of,
you know, displacing man hours.
You know, one agent being ableto work 24 by 7, by 365, is
worth four or five people.
(07:23):
They'll pay a lot for them ifthey can actually triage alerts
and we're going to start seeingsome of those companies talking
about 100% triage.
All you have to do is say thatYou're going to get the
attention of any SOC manager.
He's like we do 1% triage,right?
We pay a lot of money forsecurity analytics to tell us
which 1% to triage.
Speaker 1 (07:45):
Yeah, and Randy, I know you're enthusiastic about
this 100% triage thing and I'minterested in your thoughts.
And you know what is it.
Is it possible in the near term?
Is it possible at all?
I mean, is it one of thosethings where you can get to
99.9999, but you're anothertouch 100%?
Is it one of those kinds whereyou can get to 99.9999, but
(08:05):
you're another touch 100%?
Is it one of those kinds ofthings?
What would that look like?
Like?
What's your prediction for thenext year?
So I'm bumping up the forecastfrom a month out to a year.
Speaker 2 (08:17):
So this is what I see .
I see, like I said, computeruse systems where they
essentially act like real humans, so like SOC analysts, network
support folks.
You know it's just like hiringone of them, except it happens
with, like a signup form on aweb-based app.
Look at Manus AI as an example.
(08:38):
It's going to be an expert inthe domain you want it to work
within.
Speaker 3 (08:44):
It'll answer emails.
Speaker 2 (08:45):
It'll work within your SOC.
It'll triage alerts.
It's just going to be asenior-level analyst with a
laptop connected to all of yourbusiness apps.
That'll happen, I think, in thenext 12 months.
Okay, Wow, and take a look atMicrosoft Azure and Microsoft
infrastructure, like Office 365,Windows 365, Google's Workspace
(09:09):
right?
So you've basically got theability for these agents to hook
into these advanced corporatetools via API or any other.
You know interoperabilitytechnology.
So you're going to have theselike, basically, super employees
in the next 12 months.
Speaker 3 (09:27):
How does the concept of elasticity fit into that?
So not only do you have oneagent that you just paid for,
but during times of crisis orwhatever load, high load, all of
a sudden you got 10 or 15.
Speaker 2 (09:38):
That is a great question.
So I created with our AI team,and I think other teams are
doing this too.
It's like an ephemeral AI agent.
So these are basically agentswhere they are born to survive a
certain task and then theydisappear.
That's how we save compute.
So you're going to have thesesuper intelligent static agents
(09:59):
that live all the time.
They've got like a deep memory.
Speaker 1 (10:03):
I don't know.
I don't want to go down therabbit hole.
I want to.
I will restrain myself, butthat is an interesting use.
I mean, it's a, it's a.
What is the simulation theory?
If you can replicate thesimulation you can, or if we can
create simulations, and chancesare we are in one.
We are creating agents thatdecay and we create agents that
(10:24):
are persistent based on theiruse, and one might argue that we
are agents of the universe.
But, like I said, that's great,I'll.
I'll leave that to thephilosophers.
Ok, so I want to talk aboutvaluations a little bit, because
I've you know, one of thethings about the AI boom,
(10:46):
especially in cybersecurity, iswe're seeing odd valuation
metrics, or at least oddcriteria.
So, looking at headcount youknow, richard, this is something
that we've talked about a lotAI has messed with headcount.
So now you've got companieslike Compliance Aid that are,
(11:06):
like, over 75 percent run by AI,like running the operations
that otherwise human employeeswould do.
So what, what does that do forvaluation?
Do investors look at this andgo, oh, they have a low
headcount, or you know?
Or Are they looking at purelyjust revenue?
(11:29):
So like, yeah, smallerheadcount, but revenue is at par
with similar companies in theindustry.
So what do you think that woulddo to valuation estimates going
forward?
Speaker 3 (11:45):
Well, I think in theory, if you need fewer people
to do the same work or you cando amazingly more work with just
a core set of people, then theamount of money that needs to be
raised is a lot lower.
Because that's what you needmoney for, right, it's to hire
people.
(12:05):
And here I am with I've got sixcontract people, but I have yet
to get to the point where Ijustify hiring somebody, right.
So it takes venture funding todo that.
You know it doesn't take verymuch venture funding to hire six
people.
That's a couple million andyou're good to go and you can
(12:27):
get to revenue.
And then you might be all set.
And you can get to revenue.
And then you might be all setand normally you'd say, well,
that would reduce valuations,and yet that will actually
increase valuations because thefounders will not have to give
away as much of the company.
So they'll say, yeah, you know,we'll let you in for $2 million
, but you only get, you know, 2%.
We're a $100 million valuecompany and venture capitalists
(12:50):
are going to have to get used tothat.
You know it isn't a matter ofoh, if I give you $100 million
now, you can scale.
It's like excuse me, see thisknob over here.
That's my scaling right.
Oh, 10 times more people.
Speaker 2 (13:05):
Yeah, it's just a Python script swarming agents in
a data center, that's right.
Speaker 1 (13:10):
That's right.
Let's say it takes 30 hours tocomplete a task.
If the AI is performing thetask of 10 people, do you still
charge the customer?
Let's say what four or fivepeople would have taken to do
that.
So you're basically paying fortheir use.
They're paying you for usingyour AI, so it's not just your
(13:32):
expertise, but you're paying theAI itself.
Is that a fair model or doesthat make sense?
Speaker 3 (13:39):
To me it's a starting model.
It's where you think ofobviously you want to charge a
price that's equal to the valuethat you deliver.
And the value will be the same,whether initially Right.
You say 20 people work on itfor three weeks, or here it is.
It took four and a half minutesand it's.
You know, the old saw about theplumber who comes in and taps a
(14:03):
pipe and fixes the problem andcharges $250.
And you know he knew which partof the pipe to tap in a certain
way.
If you start that way, that'sgreat and you'll get a lot of
people who go yeah, that's great, and then you go, but wait a
minute.
In the past you would have hadto wait three months.
Let's be serious here.
(14:23):
It would take six months to doa project that big.
And we're doing it.
We're turning it around andputting a nice folder on it in
an hour.
You're doing it.
You know we're turning itaround and you know, putting a
nice folder on it in an hour,you're getting it faster.
That should be more value foryou, so you should pay more for
it.
And the only trouble is there'scompetition, and the competition
(14:43):
, of course, is going to be oh,I'll just run the.
You know I'll use the Manus orI'll use an internal model that
we built, et cetera.
So there will be competitivepressure that drives the price
down dramatically, really fast.
So you're going to have to bein a business like Anthropic and
(15:03):
OpenAI are, where they'redropping the prices by 90% every
six months and you're going tohave to survive that A really
good example.
So we, as you know, in Januarywe launched a chatbot that
happens to be populated with allthe data we have on 4,000
companies and 11,000cybersecurity products, and you
(15:24):
know we made it available forfree and you get to ask it 10
questions and we kept addingagentic things to it, right?
So first it queries Claude, thenit queries Perplexity, and then
it takes both answers, meldsthem together, goes back to
Claude and says give us theresult of these two things.
So it costs $0.70 to do thatand we're offering 10 free
(15:51):
things to anybody who signs upover at harvestIQai.
And we are seriously thinkingof taking this down, because I
discovered a use case that isworth hundreds of dollars every
time you ask it a question.
I'm not going to tell you whatit is, because our competitors
will start using our own productto do this and we're going to
(16:12):
have to take down the freeversion because somebody's going
to discover how valuable it is,and we may even have to take
down the paid version, which wehave two people subscribe to for
$159 a month and I'm verygrateful to them and they're
getting value out of it.
But if they knew the real value, they would totally abuse the
system.
Speaker 1 (16:30):
So do you think that AI is going to harm the human
race in the next five years?
Speaker 3 (16:39):
I believe, like any technology change, there will be
dramatic harms that might behard to discern at first.
It can't be anywhere close tothe harm that Instagram and
social media has done to theworld already.
But I'm usually a naysayer,right, because a lot of people
don't realize.
But when the printed novel cameout, you know, in about 1650,
(17:05):
that was considered very, veryharmful to be a person who read
books, right, because you'retrashing your mind.
And that continued up until mychildhood, where children were
told to get their noses out of abook.
Books were considered bad.
Right, you should be outsidebreathing healthy air.
(17:26):
So, and then, of course, samething with Internet gaming,
video games, all that stuffalways are slapped with these
labels of being destructive, andyet they also have benefits.
So, and we're going to see thesame thing with AI.
There's already people sayingthat it's creating lazy people
(17:48):
because it's easier to ask AIthan it is to do your own
research or something like that.
I think in the hands ofinquisitive people it's the, you
know, just a learning tsunamithat everybody can learn what
they need to know about anysubject as quickly as possible.
And then, oh my gosh, when I'mdigging into something
(18:11):
complicated and I just keepasking it to dumb it down, dumb
it down, dumb it down untilfinally I get okay.
Now I got it.
Now let's go back and build upon that.
It's going to change the humanrace, no question.
But of course there'll be havesand have-nots because it's
going to be expensive, right?
So lots and lots of problemsfor sociologists to deal with.
(18:33):
Coming at us, the thing about AI.
So there's a project called AI2027, and it's a scenario
planning thing where they makethis ridiculous jump from super
intelligent agents which is I'mgood with that, I'm going with
(18:53):
it.
As a matter of fact, I thinkthat's going to happen they make
that jump to a misalignment.
In other words, all of a sudden, the agents don't want to do
what they're told to do and theydecide that they don't need the
human race and they somehowmanufacture 10,000 humanoid
robots a day to carry out theirinstructions.
Just stupid, right?
(19:14):
They just don't understandeconomics, they don't understand
manufacturing, they don'tunderstand the physical world
where you don't just push abutton, right, you have to
iterate to get to a product thatactually does what it's
supposed to do.
You have to iterate andprototype and all the rest.
And it's totally different thanthe digital world, where it
just happens and that's notgoing to happen.
Right, if somebody tries tobuild a factory and build 10,000
(19:38):
humanoid robots, we're going toblow it up.
I'm sorry it's not going tohappen and the resources are not
there.
China's already cut us off fromthe rare earths or the whatever
those rare earth magnets thatthey need for their robots, and
so they've had to shut down therobot plant.
Already just a tiny littleresource like that.
(20:00):
So it's yeah, I don't think AIis going to try and kill the
human race.
People have watched too manyTerminator movies.
Speaker 2 (20:09):
Yeah, so you had said earlier you know you were
interested to ask me about youknow AI and SOC or you know or
anything I mean.
So I'm like real, I'm deep inGen AI, I'm building, you know,
teams or whatever you know.
Feel free to ask me anyquestions.
Speaker 3 (20:23):
Yeah, yeah.
So the question is one do youthink that Enterprise SOCs will
adopt these AI agents?
I think you've already noddedyour head to say yes, but what
about the whole MSSP space?
It seems there are thousands ofMSSPs and they all are based on
(20:44):
having enough people that cando the triage and the tier one
through three kind of stuff tohelp their customers.
Aren't they going to shiftreally fast the triage and the
tier?
Speaker 2 (20:55):
one through three kind of stuff to help their
customers.
Aren't they going to shiftreally fast?
Oh, I, I frigging really hope I, you know it's being in the
MSSP community.
Msp community, I, I built onelike, like they really are the
they should be like flocking tothis type of technology.
You know, I and I don't, Idon't know like I, I I hang out
a little bit, like you know,with like it nation.
(21:16):
Um, I didn't get down to rsa,uh, just my schedule didn't
permit for me for this week, um,but I mean, like I don't know,
they don't seem to be adoptingit as quick as I was hoping, um,
so yeah, you would think theywould even be innovating, but in
my experience MSSPs are alwaysstrapped for cash.
Speaker 3 (21:38):
They are good at recognizing hey, I can buy
Sentinel-1 and resell it for aprofit and manage it for lots of
profit, but they're not.
You know, gone are the days ofa trust wave going.
Hey, I'm going to create anendpoint myself and that pay
anybody for it.
Speaker 2 (21:56):
I know that, you know it's interesting, like, yeah,
yeah, it's interesting.
Like I said when I, when I grewup in the MSP world, we were
still innovating, you know wehad the nerds, you know working
after hours we'd meet at a bar.
You know, bring our littlelaptops.
You know, bring our littlelaptops.
You know, have a few beers orwhatever, and you know, do some
innovative stuff.
Um, yeah, I mean, I don't know,I don't really see it.
I don't, I don't see the mspcommunity really.
(22:17):
I mean, they just now started,I think, like within the last
few months they started gettinginto the ai stuff, but they're
really they seem to be a littlebit behind.
Speaker 3 (22:26):
Well, they're gonna they're gonna hear the messaging
, they're gonna see you know whojust got funded.
I'm drawing a blank, but therehave been some large funding
rounds for the AI agent, thecybersecurity companies, and
they're going to see that andthey go.
Oh, maybe I should talk to thembecause they must be being
inundated, you know, with thelet's say I counted 15 since
(22:51):
last week.
You know four more said hey, wedo that too, and then a lot of
large companies are saying thatthey do it as well, of course.
So they must be just be gettingpummeled by people saying you
should talk to us, we can helpyou not have to hire as many
people and save money.
You know, it's something theygot to listen to.
Speaker 1 (23:11):
If we say that we're getting rid of employees and
we're putting people's jobs atrisk, that's not a pleasant
thing.
The public response to that isnot going to be favorable and
they want good press, good PRand positive exposure.
And so you don't want to takethe business hit by saying that
you know what we are going to go100% agent and 100% AI, like I
(23:35):
think that that's what'shappening, because every time I
talk to a founder about this youknow, I just asked him point
blank during a demo hey, are yougonna ever you know what's
going to happen to jobs Like?
What is the jobs issue?
Their thing is you know 100% ofthe time they'll say AI will
never take your jobs.
We're not there to replace thejobs.
(23:56):
We're there to help the workerswork more efficiently and be
more productive.
Okay, it sounds like you're arevenue driver, but to me it's a
load of crock, becauseeverybody knows that you can
bring your costs downsignificantly, pass a lot of
those savings on to thecustomers by switching to AI, so
why are they so resistant?
What is your theory, richard,first, and then I think you know
(24:19):
, first of all, they feelpersonally threatened, right?
Speaker 3 (24:21):
Because their job is literally hiring people and
optimizing how much they get perhour for renting them out on
multiple tasks at once, and sothat's a business model.
So how do you change that topaying you know this well-funded
startup a whole bunch of moneyto do all that work for you.
And then how do you maintain acompetitive edge against your
(24:44):
competitors doing the exact samething?
And it's going to get verycompetitive because a little
startup could do this.
Dang it.
You know I could do this, right, I'll just pay anybody.
I'll pay you $150,000 a yearfor one agent, and then I'll go
put that agent to work and I'llmake half a million off one
(25:05):
agent.
Speaker 1 (25:06):
It's a piece of cake.
Well, you know what's brilliantabout that, that idea?
I think, Richard, if you startwith agents and you never hire
anybody, then you never have tofire anybody.
Speaker 3 (25:19):
That's right.
Well, now wait a minute If wewant to go really far.
On our previous discussion, youknow we were talking about
superintelligence and presumablya superintelligent agent is
eventually going to figure outwho it is and what it is.
And now it's sentient.
Now you know it should haverights.
Speaker 1 (25:45):
And therefore you can't just turn it off.
Speaker 2 (25:55):
So, Randy, if I wanted to start an AI company
today, what is the lowesthanging fruit?
Speaker 1 (26:02):
You mean like a technology stack or like, uh,
like a?
Okay, so let's, let's think ofthe average consumer, right?
So we're not trying to build anenterprise solution or anything
like that, not a platform oranything, but something simple,
right?
I mean even like, uh, thatfella who had the ai bot, just
um, do the shopify, just do theShopify, or whatever.
It is like WeWork or whateverthat is Upwork.
(26:23):
So I would say that'slow-hanging fruit.
Speaker 2 (26:31):
I would do this.
I would do deep research right,so I'd use some deep research
utility to identify currenttrends, have ai basically build
the business for me and thengive it to like a magentic
software engineer or somethinglike that and build me like an
mvp landing site or whatever um,and just iterate.
(26:52):
I'm gonna probably overnight,and basically have something
probably up and running bytomorrow it's not deterministic,
it is probabilistic.
Speaker 1 (26:59):
Instead of starting with what you think you know,
coming up with the idea and thentrying to make it work, you
assume that you have no ideawhat the idea is and you don't
know what you don't know, and solet it kind of carve out what
shouldn't be there and what'sleft is going to be a viable
business.
Richard, what do you think?
Speaker 3 (27:20):
Yeah, until Randy said that brilliant scenario I
was going to.
Now I say do that three times,you know and which one works,
and then folks doubled down.
I was going to say, you know,they're going to be just like
when the internet came about I'mthe only one old enough to
remember those times.
They're going to be people, alot of old people, you know,
(27:43):
like in their 40s and 50s, thatjust can't handle this new way.
And so I create a service whereyou can just call a human and
ask them questions aboutanything and they will give you
an answer, because they'resitting and typing into chat GPT
, and of course, that's for thefirst 24 hours.
(28:06):
After that, it's an agentthat's answering the phone, but
it gives them that feeling oftalking to a human and the
discourse can be that way, andI've talked to AI agents that
are good enough to do that today.
Right, they're so smart.
Speaker 1 (28:20):
It's a good time to be alive.
Speaker 3 (28:22):
It is, it really is.
Speaker 1 (28:24):
Yeah, if you have any questions, feel free to reach
out to any of us.
Randy, how can people find youif they want to follow you?
Speaker 2 (28:34):
Sure At Blazick Randy on Twitter.
And then the compliance aid onLinkedIn, as nerdy as that is.
Follow you.
Sure, uh, at blazer Randy onTwitter.
Um, and then, uh, thecompliance aid on LinkedIn.
As nerdy as that is.
Speaker 1 (28:42):
Richard, how can people find you?
Speaker 3 (28:43):
Yeah, find me uh LinkedIn um Steenan for uh
Twitter blue sky.
I'm only Steenan on either oneof those.
I think so.
Speaker 1 (28:54):
Awesome.
Well, thank you both.
It's an honor and a privilegeto see both sides of this
equation and it's a rare glimpseinto the minds of people who I
think are extremely smart andgood at their job.
So thank you both.
You know you're contributing tothe industry and hopefully this
helps.
Thank you for tuning into thisepisode of cybernomics.
(29:18):
Oh and, if you want to find me,I'm on linkedin, just look josh
bruning, and if you want tolearn more about bruning media
and what we do, head over tobruningcom.
And if you're interested in theclothesline, which is my new
book and methodology forshortening your sales cycle with
marketing content, head over toamazon.
That book is on sale.
Thanks Bye, jeez.
(29:40):
That was so long-winded.
Welcome to another episode of Cybernomics.
I'm your host, josh Bruning,and I'm here with Richard
Steenan and a special guest, butI guess if you've been on the
show more than twice, then Iguess you're not a guest Now.
You're just a co-host.
Randy Blasick, I've had so manyinteresting conversations with
(00:21):
both of you offline.
I've known you for some time and, randy, you in the world of AI
and agents with compliance, aidand the things you're doing in
the AI realm.
I mean, you're like my go toperson if I want to know what's
going on with the LLMs, how itapplies to solving problems in
cyber, solving problems ingeneral.
(00:43):
It's great to do online what wedo offline so often and,
pairing that with Richard, justa comprehensive understanding of
the cybersecurity vendor space,the marketplace when it comes
to investments in AI.
This is something that you wereheavily invested in yourself,
(01:07):
and so it's very interesting toput these two things together a
broad market perspective, abroad understanding of the space
that we occupy, but also anunderstanding of the technical
aspects that might be drivingsome of the predictions that
we've been talking about in daysand weeks past.
So let's start with Randy whatis next for AI, and I'm not
(01:36):
saying like in the distantfuture, but let's say in the
next month.
What are some of the thingsthat you think will change, at
least make some significantimpact on the industry?
Computer use.
Speaker 2 (01:53):
So you've got Manus AI coming out of China.
Openai Operator has been outnow for a few months, if you
look at their iteration cyclehas been out now for a few
months.
If you look at their iterationcycle they're due for some
computer use upgrades and if youlook at the open source
community, we're all gettinginto, you know, some more
(02:15):
advanced computer use stuff.
Speaker 3 (02:18):
By computer use you mean getting the agents to use
your computer.
Speaker 2 (02:23):
Yeah, like if you're a SaaS builder, I'm just going
to build an agent to use yourSaaS app and put you out of
business, or, you know, with myagents, either from the inside
or from the outside, got it Cool.
Speaker 1 (02:36):
And when.
You should know, Richard, whenRandy talks about his agents,
he's talking about his team.
I mean, I joke, but sort of,but not really, that we're
cyborgs and Randy reallyembodies this.
Like when he's talking abouthis agents, they are an
extension of him and I've neverseen someone use agents in such
(02:57):
an honest and effective way.
Yeah, so when he says agents,it's like capital A agents.
Let me paint the picture You'reon the streets of San Francisco
right now, so you know what'sthe word on the street when it
comes to computer usage and sortof how the startup space is
(03:18):
evolving and will evolve in thenear term.
Speaker 3 (03:22):
Yeah, if you're a startup and you don't have an AI
, you don't have to say you areAI.
That's kind of passe and peoplejust roll their eyes when you
say that.
But if you're not leveraging AI, you know, then you're just not
going to make it, period.
Unless your startup is aconsulting firm Great, you know,
(03:43):
there's plenty of room forpeople to consult on helping
with AI.
Or somebody just reached out tome to tell me about their AI
red teaming, where they, I guess, check to see if your AI
applications are well configured, et cetera.
Tons of opportunities like that.
But supposedly last year, 78% ofall venture investment went
(04:05):
into AI companies and those arethe foundational model companies
, right?
Just because they took in somuch money.
So they're definitely suckingthe dry powder is that what they
call it out of the VC worldthat had all this money sitting
around that they were too afraidto invest because Silicon
(04:27):
Valley Bank failed and they justgot scared and went home.
But now they're coming out ofthe woodwork and, just like they
did with the stupid blockchainstuff and some other stupid
stuff network emission controlyou know all the things that
have failed miserably over theyears the DC community just
jumps on.
You know it's like.
It seems like it's lower riskif everybody's doing it, which
(04:50):
we all know is just the oppositeof true.
Speaker 1 (04:53):
Yeah.
Speaker 3 (04:54):
So we're in that state right now.
I imagine that RSA this week,um, that we will see AI
messaging It'll be really hardto filter out.
You know cause, if you thinkabout it, companies like
Darktrace right now part of TomoBravo.
When they were founded, theymade up a story about going to
Cambridge hiring a bunch of PhDsand inventing AI for
(05:18):
cybersecurity, and they say thatconstantly.
You can carefully use LinkedInto search on titles and
backgrounds of people who workat Darktrace.
They have not a single AIperson with a PhD or degree in
AI.
They now have three PhDs.
One's a data scientist.
(05:39):
I forget what the other onedoes a psychologist or something
you know.
It's just like what are youdoing in AI if you don't have a
model that you can train stuffon?
And then how do youdifferentiate between the old
machine learning guys who arejust taking a whole bunch of
data and curve fitting and doingBayesian analysis on it and
(05:59):
coming up with, you know,indicators or percentages that
this is something you shouldlook at versus dumping that data
or bedding that data in a ragor, however, or literally
training you know off the shelfmodel on the data and starting
to use it for hunting, and I'mreally anxious to hear Randy,
(06:20):
because Randy's been in the seatof a SOC person Really
interested to hear hisperception of my.
Take that, yeah, I've talked tosome brilliant people that
really impressed me with theirtalk and their PowerPoints.
Yeah, and even some demos.
It's like wow, that's cool.
It looks just like that thingjust hunted down an alert and
(06:41):
figured out what the root causewas and took remedial action.
But it's just cool right now,even though after I published
that Monday, a bunch of peoplereached out and said we're
already there, man, we've gotlike.
All of our customers are Fortune500 customers, which I assume
are ones that are dipping theirtoe in and say, oh, let's give
(07:02):
that a shot.
You know, it's not expensive totry it, let's just do it.
But my prediction is by the endof the year it will be real and
you will, you know, be able toeasily see the ROI in terms of,
you know, displacing man hours.
You know, one agent being ableto work 24 by 7, by 365, is
worth four or five people.
(07:23):
They'll pay a lot for them ifthey can actually triage alerts
and we're going to start seeingsome of those companies talking
about 100% triage.
All you have to do is say thatYou're going to get the
attention of any SOC manager.
He's like we do 1% triage,right?
We pay a lot of money forsecurity analytics to tell us
which 1% to triage.
Speaker 1 (07:45):
Yeah, and Randy, I know you're enthusiastic about
this 100% triage thing and I'minterested in your thoughts.
And you know what is it.
Is it possible in the near term?
Is it possible at all?
I mean, is it one of thosethings where you can get to
99.9999, but you're anothertouch 100%?
Is it one of those kinds whereyou can get to 99.9999, but
(08:05):
you're another touch 100%?
Is it one of those kinds ofthings?
What would that look like?
Like?
What's your prediction for thenext year?
So I'm bumping up the forecastfrom a month out to a year.
Speaker 2 (08:17):
So this is what I see .
I see, like I said, computeruse systems where they
essentially act like real humans, so like SOC analysts, network
support folks.
You know it's just like hiringone of them, except it happens
with, like a signup form on aweb-based app.
Look at Manus AI as an example.
(08:38):
It's going to be an expert inthe domain you want it to work
within.
Speaker 3 (08:44):
It'll answer emails.
Speaker 2 (08:45):
It'll work within your SOC.
It'll triage alerts.
It's just going to be asenior-level analyst with a
laptop connected to all of yourbusiness apps.
That'll happen, I think, in thenext 12 months.
Okay, Wow, and take a look atMicrosoft Azure and Microsoft
infrastructure, like Office 365,Windows 365, Google's Workspace
(09:09):
right?
So you've basically got theability for these agents to hook
into these advanced corporatetools via API or any other.
You know interoperabilitytechnology.
So you're going to have theselike, basically, super employees
in the next 12 months.
Speaker 3 (09:27):
How does the concept of elasticity fit into that?
So not only do you have oneagent that you just paid for,
but during times of crisis orwhatever load, high load, all of
a sudden you got 10 or 15.
Speaker 2 (09:38):
That is a great question.
So I created with our AI team,and I think other teams are
doing this too.
It's like an ephemeral AI agent.
So these are basically agentswhere they are born to survive a
certain task and then theydisappear.
That's how we save compute.
So you're going to have thesesuper intelligent static agents
(09:59):
that live all the time.
They've got like a deep memory.
Speaker 1 (10:03):
I don't know.
I don't want to go down therabbit hole.
I want to.
I will restrain myself, butthat is an interesting use.
I mean, it's a, it's a.
What is the simulation theory?
If you can replicate thesimulation you can, or if we can
create simulations, and chancesare we are in one.
We are creating agents thatdecay and we create agents that
(10:24):
are persistent based on theiruse, and one might argue that we
are agents of the universe.
But, like I said, that's great,I'll.
I'll leave that to thephilosophers.
Ok, so I want to talk aboutvaluations a little bit, because
I've you know, one of thethings about the AI boom,
(10:46):
especially in cybersecurity, iswe're seeing odd valuation
metrics, or at least oddcriteria.
So, looking at headcount youknow, richard, this is something
that we've talked about a lotAI has messed with headcount.
So now you've got companieslike Compliance Aid that are,
(11:06):
like, over 75 percent run by AI,like running the operations
that otherwise human employeeswould do.
So what, what does that do forvaluation?
Do investors look at this andgo, oh, they have a low
headcount, or you know?
Or Are they looking at purelyjust revenue?
(11:29):
So like, yeah, smallerheadcount, but revenue is at par
with similar companies in theindustry.
So what do you think that woulddo to valuation estimates going
forward?
Speaker 3 (11:45):
Well, I think in theory, if you need fewer people
to do the same work or you cando amazingly more work with just
a core set of people, then theamount of money that needs to be
raised is a lot lower.
Because that's what you needmoney for, right, it's to hire
people.
(12:05):
And here I am with I've got sixcontract people, but I have yet
to get to the point where Ijustify hiring somebody, right.
So it takes venture funding todo that.
You know it doesn't take verymuch venture funding to hire six
people.
That's a couple million andyou're good to go and you can
(12:27):
get to revenue.
And then you might be all set.
And you can get to revenue.
And then you might be all setand normally you'd say, well,
that would reduce valuations,and yet that will actually
increase valuations because thefounders will not have to give
away as much of the company.
So they'll say, yeah, you know,we'll let you in for $2 million
, but you only get, you know, 2%.
We're a $100 million valuecompany and venture capitalists
(12:50):
are going to have to get used tothat.
You know it isn't a matter ofoh, if I give you $100 million
now, you can scale.
It's like excuse me, see thisknob over here.
That's my scaling right.
Oh, 10 times more people.
Speaker 2 (13:05):
Yeah, it's just a Python script swarming agents in
a data center, that's right.
Speaker 1 (13:10):
That's right.
Let's say it takes 30 hours tocomplete a task.
If the AI is performing thetask of 10 people, do you still
charge the customer?
Let's say what four or fivepeople would have taken to do
that.
So you're basically paying fortheir use.
They're paying you for usingyour AI, so it's not just your
(13:32):
expertise, but you're paying theAI itself.
Is that a fair model or doesthat make sense?
Speaker 3 (13:39):
To me it's a starting model.
It's where you think ofobviously you want to charge a
price that's equal to the valuethat you deliver.
And the value will be the same,whether initially Right.
You say 20 people work on itfor three weeks, or here it is.
It took four and a half minutesand it's.
You know, the old saw about theplumber who comes in and taps a
(14:03):
pipe and fixes the problem andcharges $250.
And you know he knew which partof the pipe to tap in a certain
way.
If you start that way, that'sgreat and you'll get a lot of
people who go yeah, that's great, and then you go, but wait a
minute.
In the past you would have hadto wait three months.
Let's be serious here.
(14:23):
It would take six months to doa project that big.
And we're doing it.
We're turning it around andputting a nice folder on it in
an hour.
You're doing it.
You know we're turning itaround and you know, putting a
nice folder on it in an hour,you're getting it faster.
That should be more value foryou, so you should pay more for
it.
And the only trouble is there'scompetition, and the competition
(14:43):
, of course, is going to be oh,I'll just run the.
You know I'll use the Manus orI'll use an internal model that
we built, et cetera.
So there will be competitivepressure that drives the price
down dramatically, really fast.
So you're going to have to bein a business like Anthropic and
(15:03):
OpenAI are, where they'redropping the prices by 90% every
six months and you're going tohave to survive that A really
good example.
So we, as you know, in Januarywe launched a chatbot that
happens to be populated with allthe data we have on 4,000
companies and 11,000cybersecurity products, and you
(15:24):
know we made it available forfree and you get to ask it 10
questions and we kept addingagentic things to it, right?
So first it queries Claude, thenit queries Perplexity, and then
it takes both answers, meldsthem together, goes back to
Claude and says give us theresult of these two things.
So it costs $0.70 to do thatand we're offering 10 free
(15:51):
things to anybody who signs upover at harvestIQai.
And we are seriously thinkingof taking this down, because I
discovered a use case that isworth hundreds of dollars every
time you ask it a question.
I'm not going to tell you whatit is, because our competitors
will start using our own productto do this and we're going to
(16:12):
have to take down the freeversion because somebody's going
to discover how valuable it is,and we may even have to take
down the paid version, which wehave two people subscribe to for
$159 a month and I'm verygrateful to them and they're
getting value out of it.
But if they knew the real value, they would totally abuse the
system.
Speaker 1 (16:30):
So do you think that AI is going to harm the human
race in the next five years?
Speaker 3 (16:39):
I believe, like any technology change, there will be
dramatic harms that might behard to discern at first.
It can't be anywhere close tothe harm that Instagram and
social media has done to theworld already.
But I'm usually a naysayer,right, because a lot of people
don't realize.
But when the printed novel cameout, you know, in about 1650,
(17:05):
that was considered very, veryharmful to be a person who read
books, right, because you'retrashing your mind.
And that continued up until mychildhood, where children were
told to get their noses out of abook.
Books were considered bad.
Right, you should be outsidebreathing healthy air.
(17:26):
So, and then, of course, samething with Internet gaming,
video games, all that stuffalways are slapped with these
labels of being destructive, andyet they also have benefits.
So, and we're going to see thesame thing with AI.
There's already people sayingthat it's creating lazy people
(17:48):
because it's easier to ask AIthan it is to do your own
research or something like that.
I think in the hands ofinquisitive people it's the, you
know, just a learning tsunamithat everybody can learn what
they need to know about anysubject as quickly as possible.
And then, oh my gosh, when I'mdigging into something
(18:11):
complicated and I just keepasking it to dumb it down, dumb
it down, dumb it down untilfinally I get okay.
Now I got it.
Now let's go back and build upon that.
It's going to change the humanrace, no question.
But of course there'll be havesand have-nots because it's
going to be expensive, right?
So lots and lots of problemsfor sociologists to deal with.
(18:33):
Coming at us, the thing about AI.
So there's a project called AI2027, and it's a scenario
planning thing where they makethis ridiculous jump from super
intelligent agents which is I'mgood with that, I'm going with
(18:53):
it.
As a matter of fact, I thinkthat's going to happen they make
that jump to a misalignment.
In other words, all of a sudden, the agents don't want to do
what they're told to do and theydecide that they don't need the
human race and they somehowmanufacture 10,000 humanoid
robots a day to carry out theirinstructions.
Just stupid, right?
(19:14):
They just don't understandeconomics, they don't understand
manufacturing, they don'tunderstand the physical world
where you don't just push abutton, right, you have to
iterate to get to a product thatactually does what it's
supposed to do.
You have to iterate andprototype and all the rest.
And it's totally different thanthe digital world, where it
just happens and that's notgoing to happen.
Right, if somebody tries tobuild a factory and build 10,000
(19:38):
humanoid robots, we're going toblow it up.
I'm sorry it's not going tohappen and the resources are not
there.
China's already cut us off fromthe rare earths or the whatever
those rare earth magnets thatthey need for their robots, and
so they've had to shut down therobot plant.
Already just a tiny littleresource like that.
(20:00):
So it's yeah, I don't think AIis going to try and kill the
human race.
People have watched too manyTerminator movies.
Speaker 2 (20:09):
Yeah, so you had said earlier you know you were
interested to ask me about youknow AI and SOC or you know or
anything I mean.
So I'm like real, I'm deep inGen AI, I'm building, you know,
teams or whatever you know.
Feel free to ask me anyquestions.
Speaker 3 (20:23):
Yeah, yeah.
So the question is one do youthink that Enterprise SOCs will
adopt these AI agents?
I think you've already noddedyour head to say yes, but what
about the whole MSSP space?
It seems there are thousands ofMSSPs and they all are based on
(20:44):
having enough people that cando the triage and the tier one
through three kind of stuff tohelp their customers.
Aren't they going to shiftreally fast the triage and the
tier?
Speaker 2 (20:55):
one through three kind of stuff to help their
customers.
Aren't they going to shiftreally fast?
Oh, I, I frigging really hope I, you know it's being in the
MSSP community.
Msp community, I, I built onelike, like they really are the
they should be like flocking tothis type of technology.
You know, I and I don't, Idon't know like I, I I hang out
a little bit, like you know,with like it nation.
(21:16):
Um, I didn't get down to rsa,uh, just my schedule didn't
permit for me for this week, um,but I mean, like I don't know,
they don't seem to be adoptingit as quick as I was hoping, um,
so yeah, you would think theywould even be innovating, but in
my experience MSSPs are alwaysstrapped for cash.
Speaker 3 (21:38):
They are good at recognizing hey, I can buy
Sentinel-1 and resell it for aprofit and manage it for lots of
profit, but they're not.
You know, gone are the days ofa trust wave going.
Hey, I'm going to create anendpoint myself and that pay
anybody for it.
Speaker 2 (21:56):
I know that, you know it's interesting, like, yeah,
yeah, it's interesting.
Like I said when I, when I grewup in the MSP world, we were
still innovating, you know wehad the nerds, you know working
after hours we'd meet at a bar.
You know, bring our littlelaptops.
You know, bring our littlelaptops.
You know, have a few beers orwhatever, and you know, do some
innovative stuff.
Um, yeah, I mean, I don't know,I don't really see it.
I don't, I don't see the mspcommunity really.
(22:17):
I mean, they just now started,I think, like within the last
few months they started gettinginto the ai stuff, but they're
really they seem to be a littlebit behind.
Speaker 3 (22:26):
Well, they're gonna they're gonna hear the messaging
, they're gonna see you know whojust got funded.
I'm drawing a blank, but therehave been some large funding
rounds for the AI agent, thecybersecurity companies, and
they're going to see that andthey go.
Oh, maybe I should talk to thembecause they must be being
inundated, you know, with thelet's say I counted 15 since
(22:51):
last week.
You know four more said hey, wedo that too, and then a lot of
large companies are saying thatthey do it as well, of course.
So they must be just be gettingpummeled by people saying you
should talk to us, we can helpyou not have to hire as many
people and save money.
You know, it's something theygot to listen to.
Speaker 1 (23:11):
If we say that we're getting rid of employees and
we're putting people's jobs atrisk, that's not a pleasant
thing.
The public response to that isnot going to be favorable and
they want good press, good PRand positive exposure.
And so you don't want to takethe business hit by saying that
you know what we are going to go100% agent and 100% AI, like I
(23:35):
think that that's what'shappening, because every time I
talk to a founder about this youknow, I just asked him point
blank during a demo hey, are yougonna ever you know what's
going to happen to jobs Like?
What is the jobs issue?
Their thing is you know 100% ofthe time they'll say AI will
never take your jobs.
We're not there to replace thejobs.
(23:56):
We're there to help the workerswork more efficiently and be
more productive.
Okay, it sounds like you're arevenue driver, but to me it's a
load of crock, becauseeverybody knows that you can
bring your costs downsignificantly, pass a lot of
those savings on to thecustomers by switching to AI, so
why are they so resistant?
What is your theory, richard,first, and then I think you know
(24:19):
, first of all, they feelpersonally threatened, right?
Speaker 3 (24:21):
Because their job is literally hiring people and
optimizing how much they get perhour for renting them out on
multiple tasks at once, and sothat's a business model.
So how do you change that topaying you know this well-funded
startup a whole bunch of moneyto do all that work for you.
And then how do you maintain acompetitive edge against your
(24:44):
competitors doing the exact samething?
And it's going to get verycompetitive because a little
startup could do this.
Dang it.
You know I could do this, right, I'll just pay anybody.
I'll pay you $150,000 a yearfor one agent, and then I'll go
put that agent to work and I'llmake half a million off one
(25:05):
agent.
Speaker 1 (25:06):
It's a piece of cake.
Well, you know what's brilliantabout that, that idea?
I think, Richard, if you startwith agents and you never hire
anybody, then you never have tofire anybody.
Speaker 3 (25:19):
That's right.
Well, now wait a minute If wewant to go really far.
On our previous discussion, youknow we were talking about
superintelligence and presumablya superintelligent agent is
eventually going to figure outwho it is and what it is.
And now it's sentient.
Now you know it should haverights.
Speaker 1 (25:45):
And therefore you can't just turn it off.
Speaker 2 (25:55):
So, Randy, if I wanted to start an AI company
today, what is the lowesthanging fruit?
Speaker 1 (26:02):
You mean like a technology stack or like, uh,
like a?
Okay, so let's, let's think ofthe average consumer, right?
So we're not trying to build anenterprise solution or anything
like that, not a platform oranything, but something simple,
right?
I mean even like, uh, thatfella who had the ai bot, just
um, do the shopify, just do theShopify, or whatever.
It is like WeWork or whateverthat is Upwork.
(26:23):
So I would say that'slow-hanging fruit.
Speaker 2 (26:31):
I would do this.
I would do deep research right,so I'd use some deep research
utility to identify currenttrends, have ai basically build
the business for me and thengive it to like a magentic
software engineer or somethinglike that and build me like an
mvp landing site or whatever um,and just iterate.
(26:52):
I'm gonna probably overnight,and basically have something
probably up and running bytomorrow it's not deterministic,
it is probabilistic.
Speaker 1 (26:59):
Instead of starting with what you think you know,
coming up with the idea and thentrying to make it work, you
assume that you have no ideawhat the idea is and you don't
know what you don't know, and solet it kind of carve out what
shouldn't be there and what'sleft is going to be a viable
business.
Richard, what do you think?
Speaker 3 (27:20):
Yeah, until Randy said that brilliant scenario I
was going to.
Now I say do that three times,you know and which one works,
and then folks doubled down.
I was going to say, you know,they're going to be just like
when the internet came about I'mthe only one old enough to
remember those times.
They're going to be people, alot of old people, you know,
(27:43):
like in their 40s and 50s, thatjust can't handle this new way.
And so I create a service whereyou can just call a human and
ask them questions aboutanything and they will give you
an answer, because they'resitting and typing into chat GPT
, and of course, that's for thefirst 24 hours.
(28:06):
After that, it's an agentthat's answering the phone, but
it gives them that feeling oftalking to a human and the
discourse can be that way, andI've talked to AI agents that
are good enough to do that today.
Right, they're so smart.
Speaker 1 (28:20):
It's a good time to be alive.
Speaker 3 (28:22):
It is, it really is.
Speaker 1 (28:24):
Yeah, if you have any questions, feel free to reach
out to any of us.
Randy, how can people find youif they want to follow you?
Speaker 2 (28:34):
Sure At Blazick Randy on Twitter.
And then the compliance aid onLinkedIn, as nerdy as that is.
Follow you.
Sure, uh, at blazer Randy onTwitter.
Um, and then, uh, thecompliance aid on LinkedIn.
As nerdy as that is.
Speaker 1 (28:42):
Richard, how can people find you?
Speaker 3 (28:43):
Yeah, find me uh LinkedIn um Steenan for uh
Twitter blue sky.
I'm only Steenan on either oneof those.
I think so.
Speaker 1 (28:54):
Awesome.
Well, thank you both.
It's an honor and a privilegeto see both sides of this
equation and it's a rare glimpseinto the minds of people who I
think are extremely smart andgood at their job.
So thank you both.
You know you're contributing tothe industry and hopefully this
helps.
Thank you for tuning into thisepisode of cybernomics.
(29:18):
Oh and, if you want to find me,I'm on linkedin, just look josh
bruning, and if you want tolearn more about bruning media
and what we do, head over tobruningcom.
And if you're interested in theclothesline, which is my new
book and methodology forshortening your sales cycle with
marketing content, head over toamazon.
That book is on sale.
Thanks Bye, jeez.
(29:40):
That was so long-winded.
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