S1: Unsupervised Learning is a podcast about trends and ideas in cybersecurity,
S1: national security, AI, technology and society, and how best to
S1: upgrade ourselves to be ready for what's coming. All right, Harry,
S1: welcome to Unsupervised Learning.
S2: Hey, great to be here.
S1: Yeah. So, uh, I understand you're doing some cool stuff with, uh,
S1: AI and vulnerability management and stuff like that. Can you
S1: tell me what you're working on?
S2: Yeah, sure. So just over a year ago, I co-founded
S2: with a couple of others, a company called maze. Um,
S2: we just came out of stealth, maybe like, two months
S2: ago now. Uh, so, so really quite recent, but super
S2: experienced team. And what we've been doing is building basically
S2: a series of AI agents that can deeply interrogate and
S2: understand the vulnerability. Um, so kind of can go off
S2: and do the kind of analysis that, like, really experienced
S2: security engineer might be able to do into a vulnerability,
S2: do it completely automatically. And by doing that, we can
S2: do it over like hundreds of thousands or millions of
S2: vulnerabilities all at once. And therefore, we can get people
S2: out of this like constant hell that we found ourselves in,
S2: in our old jobs. And I know plenty of others
S2: have been where you're just constantly firefighting, like an endless
S2: backlog of vulnerabilities with kind of no, no hope in sight. Um, yeah.
S2: Hopefully we're starting to help people out of that, out
S2: of that mess. Um, so yeah.
S1: Okay. Interesting. So yeah, I've got some thoughts around this.
S1: So are you, um, are you focused on the vulnerability
S1: or are you focused on, like the context of the org?
S1: Like where do you think you're going to get the most, like, um,
S1: signal or benefit, uh, when it comes to the actual remediation? Because, um,
S1: I've done a whole bunch of management in my career
S1: as well, and it seems like the problem is always remediation, uh,
S1: as opposed to, like, the prioritization of the vulns. What
S1: are your thoughts?
S2: Yeah. So one of the interesting things that we've we've
S2: found along the way. And I've got going into this
S2: was like the volume of vulnerabilities is so ridiculous. And
S2: it's like, as people know, like it's climbing crazy like
S2: year on year. At the moment, the volume is so high.
S2: The problem doesn't make sense because the volume is just
S2: so high. So people end up with all these different
S2: approaches to it, prioritization and scoring. And can we use
S2: SPSS and Kev and all these different approaches. And the
S2: premise that we've come at it from is if you
S2: go and chat to and I'm sure maybe you are
S2: one of these people once upon a time like go
S2: chat to a team that's dealing with this day in
S2: day out and say, okay, if you were to go
S2: look into like, start with the top of your list.
S2: If you were to go into look into it for
S2: like 2 or 3 hours in the context of your environment,
S2: what would you find? Right? And then theoretically, if you
S2: could do that over every single one, what would you
S2: find and how big would that list be relative to
S2: your current list? Right. And invariably, maybe you disagree with this,
S2: but like most of the time when we ask people
S2: that question, they're like the answers range from like it
S2: would be 80% bigger to like the most extreme one
S2: ever was like 99.99% bigger. I think I've seen one
S2: true positive ever, which I think was a bit bold
S2: to be honest.
S1: But um, yeah, yeah.
S2: But the the premise is basically like if you spend
S2: time with them and if you try and actually deeply
S2: understand what the attacker would need to have to do
S2: to exploit it and what your environment is actually like,
S2: a huge proportion of the time the vulnerability isn't there.
S2: Like it is not in any way, shape or form exploitable.
S2: And so when we've been building all these prioritization criteria
S2: over the years, we've been like shuffling this pack of
S2: cards where most of the pack of cards should never
S2: have been in the first place. Right? We should have
S2: just been removing, you know, 48 of the 52 cards
S2: or whatever, and focusing on those four rather than trying
S2: to figure out how to sort them 1 to 52. Um,
S2: so that's a lot of the premise of why we
S2: think this world can go, which is if you can
S2: build tools that are intelligent enough to do that really
S2: deep technical analysis, you can actually remove most of the
S2: deck of cards rather than trying to figure out how
S2: to prioritize it all. Because I agree with you. It
S2: doesn't matter if we can prioritize them. If we can't remediate,
S2: it's literally irrelevant that we've got a prioritized list if
S2: we can't actually act on them. But the only way
S2: that you can figure out how to use your limited
S2: remediation resources to act on them is to figure out
S2: which ones you can throw away and which ones you
S2: actually need to keep, and maybe which ones you need
S2: to like, fix like today. Um, so that's one side
S2: of it. We also think there's a bunch of use
S2: cases for AI to actually help with remediation. So just
S2: take like everything from I figured out I want to
S2: fix something to it is fixed and see how much
S2: of that you can get AI to help you with.
S2: I think like running all the way to fully automating it.
S2: I think we're a little bit despite what some people
S2: are hoping at the moment. I think we're still like
S2: a little bit away from it. Like just AI going
S2: in and changing our environments day in, day out. But like,
S2: I think we can do we can take people a
S2: lot further than they are today. Um, so when you
S2: stick all that together end to end, you get like much,
S2: much smaller list and then you get much faster remediation
S2: and then you start being able to work on the
S2: stuff that matters, um, much, much quicker, uh, we think.
S1: Yeah. Yeah, that makes sense. I feel like as you're
S1: talking there, I mean, there's there's multiple steps, right? There's
S1: understanding the bone deeply. And then, um, I guess there's
S1: the threat Intel component as well. And you've seen like, CVS,
S1: like try to try to capture all these in different
S1: fields of the Volm. So there's like an active threat
S1: Intel for like how many people know about this? How
S1: easy is it to exploit? Then there's the Volm details itself.
S1: Like how bad is it? Like what is the attack
S1: surface that it actually impacts? And then there's the thing
S1: of like, okay, do we have that here at the company?
S1: Is that installed? Where is it installed? Um, what versions?
S1: And how does that relate to the vulnerable version. Um,
S1: and then given all of that now what is the prioritization?
S1: And I feel like those are all like, uh, individual components. Um,
S1: and then the thing I've been thinking a lot about
S1: is like, okay, but where are the developers? Um, if
S1: you find the vuln in like some readout somewhere in
S1: some report, and it's in this particular piece of code,
S1: what app is that code part of? Um, who actually, uh,
S1: is responsible for that app? And then, um, when you
S1: go to create a patch or a fix or whatever, um,
S1: how do we get that to the right person in
S1: the form that requires the least amount of effort for them?
S1: So I feel like those are all the pieces, and
S1: it's almost like you can have multiple companies working on
S1: each one of those pieces. Um, but somebody who could do, like,
S1: all of them pretty well, that's going to be a
S1: massive win.
S2: Yeah. So that that's, that's that's our concept as well,
S2: which is basically if you think of all these different
S2: moving pieces, you kind of want a series of agents
S2: like AI agents, not not the old school type tackling
S2: each of those component pieces. Right. So like you mentioned there,
S2: like who owns the thing? That's a great use case
S2: for AI and particularly agents because like there is information
S2: out there to help you solve that problem? Yes, but
S2: for a human, it's just like it's a, it's a
S2: like a it's like one of those long tail problems where, like,
S2: you could go find the answer. It's going to take
S2: you often a long time to like go figure it out.
S2: And then going and figuring it out for every single
S2: ball and every single day becomes really annoying. But for AI,
S2: that's actually pretty. Like if you've got the right data sources,
S2: it's actually quite, quite a tolerable problem. Um, so then
S2: you imagine, like putting an agent on all of those
S2: different places, like you mentioned, prioritization. Well, before you before
S2: threat Intel becomes relevant at all, you have to figure
S2: out whether it can be exploited or not. And that's
S2: got to be your first agent, because if it can't
S2: be exploited, it's irrelevant whether it's got threat Intel associated
S2: with it or whether the business context is is bad,
S2: or whether the outcome could be bad or how to
S2: fix it, like everything else becomes irrelevant because you just
S2: need to stop there. So you, like, start there with
S2: that agent and then build out like agent by agent
S2: for each part of that flow. And as you said,
S2: like figuring out who owns it, um, what action they
S2: need to take, what would be the most efficient action.
S2: And then you can go all the way through to
S2: like starting to generate those actions for them. Right? Which
S2: is like, yes, I don't think we're quite there with
S2: just taking the action. But if we've done enough work
S2: intelligently to figure out what the problem is, how it's fixed,
S2: what the ramifications could be, then we can start to
S2: generate actions for people and saying like, look, hey Sarah,
S2: I found this vulnerability. I proved it could be exploited.
S2: I proved it could be bad. I actually figured out
S2: what the right thing to do would be. I generated
S2: it for you. Do you want to do it? Yes
S2: or no? That's a hell of a lot better experience than, like,
S2: here's ten findings, of which one of them's probably maybe
S2: bad good luck. Which is kind of like today's situation oftentimes.
S1: Yeah.
S2: Um, so yeah, that's the idea.
S1: Yeah. No, I really love that. I mean, if you're
S1: inside of their tool, let's say it's, it's, um, GitHub
S1: or whatever it is, and it's just like, here's a
S1: PR that I've submitted. You can see the diff right here.
S1: And like this is what she does every day, all
S1: day anyway in this exact tool. So she's like yes,
S1: I accept. Boom. That's way, way easier than the thing
S1: that just doesn't work. Which is Witches. They received a
S1: report which had like 480 things in it. And and
S1: now it's her job to figure out if any of
S1: those apply to her. And then when she looks inside,
S1: the vulnerability description is just like this wall of text,
S1: which came from like a generic vulnerability description. And so
S1: she's left trying to figure out like, okay, like what
S1: exactly is this saying? And guess what? That's why management
S1: like hasn't been doing so well over the last like
S1: 25 years.
S2: Yeah. Yeah. Well the worst thing is though like for
S2: Sarah there a lot of places now have got to
S2: the point where they like one of the easiest applications
S2: of AI was throw a list of existing findings in
S2: and like get some suggested actions out because it was
S2: just like literally like single call. You could like throw
S2: a finding in, give you some code, give you something.
S2: And that's where a lot of like our auto fix
S2: suggestions kind of come from today. The problem is that
S2: because they started there, rather than starting right at the
S2: root of the problem, which is like most of the
S2: findings don't mean anything. Yeah. We end up with, let's
S2: say we have a list. Your list of 480 findings
S2: turns into 480 auto fixes. And so we've got a
S2: lot of work getting generated that doesn't need to be
S2: there in the first place. And also, most of them
S2: lack a lot of the context about, like, how this
S2: thing exploited what would be the most appropriate fix. So
S2: we've kind of like started at the end in a
S2: lot of cases now. And we need to go, I
S2: think like back to the start, get back to the
S2: root of the problem, figure out how bad these things
S2: like figure out what like if they can be exploited,
S2: how bad they are, what the context is around them,
S2: and then feed all of that into our remediation efforts.
S2: And suddenly Sarah gets not only like a shorter list,
S2: but like a way more intelligent list of, of of
S2: actions you could take. And then, as you said, she
S2: gets away from, you know, just getting that like blank
S2: report with a vague description to you just need to
S2: do this thing now. Okay, great. Like click uh, and
S2: that's hopefully like, honestly it's kind of why we started
S2: the company is we used to lead like the PM
S2: and engineering teams at various places. And it just it
S2: just annoyed us because because the teams were spending so
S2: much so long like, uh, spinning their wheels around the problem.
S2: And it was just annoying. So we just thought like,
S2: this can't be right. Um, and so, Yeah, here we are.
S1: Yeah. That's it from my experience. That's absolutely the best
S1: place to have a startup is like, the founders are
S1: familiar with the pain of the problem, right? And it's
S1: like they just wish that they had this in the
S1: past for their problem. And, like, it's just a really
S1: pure way to start a company. Um, so what can
S1: you say about the, um, the current state of agents, um,
S1: with all the hype and what, like what works? Uh,
S1: and as much as you can share about about your, like,
S1: approach to the agents, like how many are there? Are they,
S1: are they working on different things all at the same time?
S1: Are they coming back and like unifying into one context,
S1: that kind of stuff?
S2: Yeah. Good question. I was actually just I was just
S2: talking to a CSO about this earlier today, and we
S2: have quite a funny chat about it of where people's
S2: expectations sometimes of what agents can do are and like
S2: the reality and some of the like. They sometimes they
S2: look so impressive when you first start using them that
S2: you kind of imagine that they're capable of just tackling
S2: like all sorts of tasks with limited guidance. Right? You're
S2: just a bit like imagine, like you're working with like
S2: a really senior colleague. You can give them a really
S2: vague instruction and they'll just like figure it out and
S2: probably get a good result. The agents can sometimes feel
S2: a bit like that. So people end up being like, cool,
S2: we're going to build this platform and agents are going
S2: to do all these like 100 different things, right? And
S2: I see some security companies starting to, like, fall into
S2: this trap a little bit where they're like, we use
S2: agents now. We use agents for like this and this
S2: and this and this and this. And having worked with
S2: them for the last over a year now, my perception
S2: of them is they're much more like very knowledgeable, like 16,
S2: 17 year olds, right? In that like they're knowledgeable, they're
S2: capable of getting a task done. But they need a
S2: ton of like clear instructions, guidance, guardrails like training back
S2: and forth with a manager again and again and again
S2: and again. And then you can get them a bit
S2: like teaching a grad how to do a specific job.
S2: You can take your like 17 year old and turn
S2: them into someone who is pretty capable at that job
S2: day in, day out, but they still need to be
S2: doing it in like a relatively narrow, confined space. And
S2: they still need a lot of guidance and training and oversight.
S2: And that, I think, is where we currently are with
S2: a lot of agents. But that doesn't mean for security,
S2: because we have so many tightly well-defined problems, lots of
S2: data and lots of ways to give guardrails to these things.
S2: If you spend enough time honing them, they can just
S2: start doing incredible stuff. But it's really not a case
S2: of like, cool, I'll just throw this problem into Claude
S2: and just like, see what it comes out with. Because
S2: a lot of a that's gonna be very expensive a
S2: lot of time if you do it without any optimization,
S2: B you're gonna end up with like, such an unpredictable
S2: result that you're going to go off the idea entirely. Um,
S2: and I run into teams like that all the time
S2: where they're like, oh, yeah, I tried this for like
S2: a day, and it didn't quite work. It's like, well,
S2: you need to spend so long, like getting them there.
S2: So that's kind of my perception of it. They I
S2: don't know what you're seeing. I'm sure you're playing around
S2: with them in your own world, but like, they can
S2: do incredible things. It just takes work basically would be
S2: the summary.
S1: Yeah, yeah, very much agree with that. Yeah. I just
S1: got done talking to, uh, Matthew Brown, uh, who led
S1: the AI sec competition. Are you following that whole thing?
S2: Yes. Yeah, vaguely followed it.
S1: Yeah. The this, uh, DARPA competition and it's open source project,
S1: and basically, they'll all release their things. I'm not sure, um,
S1: if anyone's messed with them yet, but the idea was
S1: to just go and find vulnerabilities on GitHub, and then
S1: you have to be able to fix them. So that's
S1: this multi-year competition from DARPA. So I was talking to
S1: him about this agent design thing, and he was like, yeah,
S1: the first, most important thing is like break the problems
S1: into categories. And the category is, um, should you even
S1: be using AI at all for this? Right. So, um,
S1: and try to do as much with regular deterministic code
S1: is possible and like it because too much. Intelligence is
S1: the thing. Too much creativity is a thing. Um, and
S1: especially if you ask a model, does this look vulnerable
S1: to you? They will try to find a way to
S1: say yes. Right. So it's like it's this weird balance
S1: between deterministic, uh, deterministic code and then using intelligence where
S1: you have to, for example, does this look like a cat.
S1: You can't use deterministic code for that. You have to
S1: use ML or AI or whatever for that. But like
S1: this whole system design thing is really fascinating to me.
S2: Yeah. Your example of like, does this look vulnerable to
S2: you is a great example of where, like, the agents
S2: aren't at that level of competency, it's too broad and
S2: too vague for them. Like they like a human may
S2: not even be able to answer that. Like, you line
S2: up ten humans and they might give you a bit
S2: different answers anyway, let alone what agents would give you.
S2: So yeah, you're right. I think like the nature of
S2: it is how do you distill it down to smaller
S2: and smaller and smaller questions and like make it impossible
S2: to get it wrong, given the knowledge that it has
S2: and like the ability that it has? And again, much like,
S2: like imagine you're like leading a team of like new grads,
S2: like you're not going to just ask them vague stuff
S2: at the start of the day and see how they
S2: get on. Like a week later. You're going to give
S2: them all very like tight, refined tasks. You got to
S2: keep checking in on them. And then you're probably going
S2: to like, you know, aggregate their work at the end
S2: and then you're going to come out with something useful, hopefully. Um,
S2: and I think that's much more like where we are.
S1: Yeah. So, so what is, um, the product that you have?
S1: What does it do? Like, how does the work, like,
S1: what sources are you pulling from? Are you aggregating other sources?
S1: Are you finding yourself, uh, like, what is the workflow
S1: look like for somebody using it?
S2: Yeah. So in essence, it's really just a way of
S2: going from like, I have a potential risk. I need
S2: to investigate it and figure out how bad it is.
S2: I need I need help fixing it. Like that's the
S2: really the workflow that it does at the moment. It
S2: does that all with cloud CVS. It's basically like cloud infrastructure, um, VMs, containers, etc.
S2: pull in the vulnerabilities that are found by scanners. Investigate
S2: every single one of them. Come away with that you
S2: know 10% as long list. And then within that list
S2: prioritize the very, very, very small number that are going
S2: to lead to something bad happening and then help people
S2: fix them. Um, so that's the flow at the moment.
S2: Like cloud vulns in um, like human level triage, I
S2: guess in the middle and then like, fixes come out
S2: the other end. And as you can imagine, then as
S2: we build out that platform, it gives us more and
S2: more potential to throw more and more different types of
S2: data at that same platform. But for now, very kind
S2: of focused on on just cloud CVS.
S1: Yeah, that makes sense. And so, um, pretty much the
S1: top products that are out there, like when they're emitting vulns,
S1: you can just consume from them, you can connect to
S1: them and get their phones, uh, a list essentially.
S2: Exactly. Yeah. Because it's always been a very integrated kind
S2: of like area of VM was, you know, probably probably. No.
S2: And so, um, so yeah, I think people are very
S2: used to kind of like aggregating data from scanners and
S2: bringing it around and stuff like that. This is a
S2: a big twist on that, because rather than aggregating it
S2: and putting some kind of score on top of it
S2: or something, you're aggregating it and then letting AI kind
S2: of like, investigate everything like a human would have done,
S2: and then coming away with a very different answer on
S2: the other side. Um, but yeah, we basically pulled from API's, uh,
S2: pulled from the cloud environment, gather all the context, pull
S2: from other systems if we need to, sometimes to like
S2: gather more context to help us with our assessments and then, um,
S2: you know, create actions at the other end. But it's
S2: meant to as I said, the analogy is just always like,
S2: what would what would, like Daniel four years ago or
S2: something have done, like trying to solve this internally? Like,
S2: what would you have gone in if you had the time? Like,
S2: what would you have gone and looked at? What data
S2: would you have gathered? How would you put it all together?
S2: And then how do we do that automatically?
S1: Basically, yeah, I really love that framing of AI. Like, um,
S1: I try to discourage people, especially who are negative about AI,
S1: from thinking of it like as some special tech and
S1: just imagining it as extra eyes and hands. And to
S1: your point right now, 16 and 17 year old eyes
S1: and hands. Right. So it's like.
S2: Yeah, yeah.
S1: Um, what what would you have done if you had
S1: 10,000 people to put on every given day to go
S1: and look? And it's like, well, I would go and
S1: collect them and I would go and analyze them. I
S1: would dig into the details and all the things that
S1: you just said. That's what we would do be doing manually.
S1: So that's what we should try to get the agents
S1: to do. It's like it's not complex. It's like not
S1: special tech. It's just you've got extra eyes and hands.
S1: What do you do with them?
S2: Yeah, exactly. And they don't get bored. Um, and they
S2: can use as many of them as you want within
S2: within reason. Um, and that's why I think a lot
S2: of people get wrong sometimes is like they're trying to
S2: they try and like. Yeah, they try and just think like, okay.
S2: Like there is like human jobs today. And there is
S2: what AI can do. And like it's just a one
S2: for one fight. But it's not. It's more like, it's
S2: more like, what is all the long tail of stuff
S2: that like four years ago, I couldn't write software to
S2: help me with. But now I can use software to
S2: help me with and like, what's that long tail of
S2: stuff that I'm never, ever going to get time to do,
S2: but would be useful if I do enough of it
S2: and I can aggregate it all up like that's where
S2: the value is today. And this insecurity, like security, is
S2: absolutely full of use cases like that where like theoretically
S2: we would like to go and look at a bunch
S2: of stuff, read a bunch of different data points, pull
S2: it all together, come up with conclusions, etc. but we
S2: just don't have the time. So yeah, I think hopefully,
S2: hopefully people are starting to figure that out, which is
S2: like it doesn't have to be just like a 1
S2: to 1 with what a human is doing. It's more
S2: like a yeah, what's what's the list of stuff you
S2: would do if your day was 100 times longer or something?
S1: Yeah, yeah. That's right. And yeah, I've been thinking about this, uh,
S1: limitations of creativity. Um, like, I call it a type
S1: three limitation, um, because there's two others. But the limitation
S1: is basically not realizing because we grew up in the past, obviously,
S1: that's the way time moves is forward. And it's like, Um,
S1: we just have blocked out a million different things that
S1: we could be doing in life. Life and at work
S1: or whatever. And so I kind of think of, like
S1: how many logs are actually streaming in to, into a
S1: given organization, right? Let's say terabytes per day, depending on
S1: the size of the company. And like the natural assumption
S1: over the last 20 years is, well, we could look at,
S1: you know, 0.01%. So let's try to find that 0.01%.
S1: And so we we without even knowing it, we have
S1: these invisible barriers on us. And what AI is like
S1: forcing me and like, you know, us in general to
S1: do is like, think outside of that. Think what could
S1: you do potentially if in that case, it wouldn't be
S1: like if I could double my team because it would
S1: still be 0.01%. But if you had a million agents
S1: that could read logs or whatever, and maybe some of
S1: them can help you whittle down how many logs are
S1: being generated or whatever, but it's like I find it
S1: strange that we have these artificial barriers on ourselves about
S1: what is possible, based on the fact that we grew
S1: up in tech in the past.
S2: Yeah, I see that all the time, but that's a
S2: great way of phrasing it. I think one one good
S2: way of breaking out of that, which I think you
S2: can use outside of security. But it's interesting in security
S2: is fast forward like five years, right? Yeah. And AI
S2: is way is way progressed. And I'm sure we're going
S2: to go through like peaks and troughs with AI during
S2: that time, you know, of, of of success and failures
S2: and stuff like that. But over five years you'd expect
S2: it to get pretty far and it's spread pretty wide
S2: and change a lot of how we do all that stuff.
S2: You got to think in that scenario. What is competition
S2: from people using AI more heavily than you are going
S2: to change about your behavior by then? Right. And so
S2: in security, the competition there is not necessarily other companies.
S2: It's actually attackers. And I'm always like, remiss to like,
S2: say too much of this because you don't want to
S2: feel like I'm scaremongering or anything like that. But if
S2: you think about what we've what we're now seeing, like
S2: you and I are working with AI agents and stuff
S2: like that, and we're seeing what they can do and
S2: how they're starting to scale into like pretty complex security tasks.
S2: Just like imagine that on the flip side. And so
S2: if teams are kind of like slower to to think
S2: creatively and think out the box of like how much
S2: they could get out of this in the short term,
S2: they're going to be forced to in the long term
S2: because eventually attackers are not like they're not going to
S2: like hang around. They're just going to be like ruthless
S2: in terms of figuring out how to do it. And
S2: they're gonna, um, and they're going to start forcing our
S2: hand to actually think, okay, well, it's not it's not
S2: just a nice to have anymore to, like, be able
S2: to go and tackle those 10,000 other, like, things that
S2: you might do if you had the time. Like it
S2: starts to become a must have, because suddenly the stuff
S2: that used to be easy to defend against starts to
S2: become hard to defend against, and all our behaviors have
S2: to change. I think actually applies across a bunch of industries. Like,
S2: you can apply that to all sorts of different products,
S2: which is like if you're struggling to think creatively about
S2: where this could all go, think five years forward. Think
S2: about what the competitive landscape around you looks like. If
S2: everyone else is really heavily using AI. And then work
S2: backwards to like what you probably need to do. And
S2: I think a lot of security is probably like that today,
S2: although it's going to take a bit of time for
S2: it to all shake out.
S1: Yeah, I very much agree with that. Um, the way
S1: I characterize it for the future is essentially, um, in,
S1: in the, uh, the head of DeepMind thinks this way
S1: as well, is like this, um, it's all about building
S1: world models of the things you care about. So in
S1: the case of of what we're doing, it's like understanding the, um,
S1: the it stack perfectly understanding the business, perfectly understanding the
S1: people there and the developers and the projects they're working
S1: on and their spend. And just like having a perfect
S1: picture of that company, or in the case of attackers,
S1: which is guaranteed to be using the same tech against you,
S1: they have a world model of a world model of
S1: their target. And we are the target. So we better
S1: have a better world model of ourselves than they have
S1: of us. Because in a way, it's just dueling banjos
S1: of their AI system against our AI system. Who has
S1: the most up to date data now? Um, and, and
S1: I basically say that attackers are going to win first
S1: because they could just start shipping with this quick. Right?
S1: And right now, everyone's like trying to figure out what's
S1: going on. So attackers are going to move first. But
S1: ideally and for somebody like Google already, they are so
S1: organized that they should have more up to date internal
S1: information coming out of these platforms to feed to their
S1: agents to keep that context more up to date. Um,
S1: but I very much agree with your your characterization here.
S1: It's like, look, just imagine that your attacker knows everything
S1: about you and they can sense changes in your environment.
S1: So you added this new company because there was a
S1: merger and acquisition which they learned about from from Crunchbase. Okay.
S1: So now they're going to profile that entire company, and
S1: they're going to assume that what Vulns are there are
S1: going to now be your vulns for a period of time.
S1: So they're going to start attacking those things. And it's like, well,
S1: how fast are you making that adjustment? Because they're going
S1: to make it pretty fast. And it really is this
S1: that's the game. That's the competition is who has a
S1: better system.
S2: Yeah. And it's not it's not like superhuman stuff I
S2: don't think. We may occasionally see some pretty, pretty wild attacks,
S2: but it's just going to be the stuff that we
S2: kind of think is kind of hard and therefore kind
S2: of rare today. Right? It's just going to happen way
S2: more frequently, I think is the most sensible thing that
S2: like your example there of like that's kind of rare
S2: for an attacker to have the sense and the timing
S2: to be like, okay, we're now going to go after
S2: you because we've got this acquisition, but that just becomes
S2: 10 to 100 times cheaper to do in the New World,
S2: and therefore theoretically becomes a lot more common. Um, and
S2: we saw this I used to work in, uh, in
S2: fishing and pre like lem era. We saw some similar
S2: types of effects where basically there was this period of
S2: time where people went from, they figured out that like,
S2: you know, the Nigerian prince email didn't work anymore, like
S2: the mass mail phishing email didn't work anymore. And so
S2: they started working on more targeted stuff. And that worked.
S2: And then they realized it worked. And so they built
S2: a load of phishing kits like phishing as a service
S2: and all this kind of stuff, and they made it
S2: really cheap for each other to do it. And suddenly
S2: we saw this insane spike where it went from like
S2: business email compromise and similar types of emails being like
S2: kind of there, but not that common to suddenly they
S2: were unbelievably common because the attackers made it cheap for themselves.
S2: And so like as soon as attackers make stuff cheap
S2: for themselves, you see the volume go up. I feel
S2: like we're kind of nearing the precipice of that starting
S2: to starting to happen. I don't know whether it'll happen
S2: like this year or next year or something, but as
S2: you said, they'll be pretty ruthless with it. They won't
S2: they won't hang around and chat about it. Uh, they'll, uh,
S2: they'll just get to work and it starts working.
S1: Yeah. Yeah. And going back to your previous point that
S1: you made, um, that we were talking about with, like,
S1: the extra eyes and hands, um, and how this is
S1: just kind of like it. It's not superhuman stuff. It's
S1: stuff that you could do with more scale if you
S1: had more people. Um, and since you're talking about fishing,
S1: one of my greatest examples of this is like, what
S1: if you could just create a perfect dossier on every
S1: employee at the target? So, um, and I've already got
S1: a tech stack that does this, actually. So I could
S1: just give someone's name and it will build me, like
S1: a six page CIA background thing and like, including, like,
S1: likely personality analysis or whatever. Well, I could then feed
S1: that to a thing that writes spearfishing. So so here's
S1: the question if if you had um, if you used
S1: to be an attacker outfit with like a 19 people
S1: and like four of them were really smart or whatever.
S1: You have 19 people and you're barely able to. You
S1: have to focus on a very specific vertical. 1 or 2, uh, attack, um,
S1: you know, targets at a time. And, like, you're really effective,
S1: but you can only do so much as opposed to saying, hey,
S1: these 250 companies are the ones I want to go after. Um,
S1: create dossiers on all of them, then go find all
S1: their social media posts. Uh, find any time they're complaining
S1: or talking about the internal tech stack, or they mention
S1: an acquisition, or they do anything and use that to
S1: customize your spearfishing. How many people do you have to hire?
S1: Like this is all 100% possible. This is not special tech.
S1: It just requires so hundreds or thousands of people because
S1: do it every hour. Do it every day. Right? Yeah.
S1: And so so now it's just like you're just you
S1: just need more skill. And to your point about, um,
S1: Doing it internally. It's the same exact thing. You. You
S1: just need more eyes and hands to do this. And
S1: I'm just fascinated by the fact that, um, I mean,
S1: one way to characterize this is just imagine all your
S1: attackers who had 20 employees now have 20,000 employees. That
S1: that is your problem.
S2: Yeah. Yeah, that is quite literally. And and as I said,
S2: it's not like they're now doing stuff that they never
S2: did before. They're just doing it at they can just
S2: afford to do it a way bigger scale like your
S2: fishing example. The worst bit about that is that they
S2: don't even need to stop at writing the emails today.
S2: Like they can actually just build agents that can take
S2: your like dossier of them, right? Understand some stuff about
S2: them and then kick off a flow of actions that's
S2: actually gonna be way more effective than that single email, right?
S2: You know, uh, gently warm up the email recipient calls,
S2: whatsapps LinkedIn messages. Um, uh, real like real sounding voice calls. Like,
S2: if they really want to get into it. Fake, fake,
S2: fake web pages like that is all now in the
S2: remit of stuff that used to take a single person
S2: dedicated on that task, right? Like days at a time
S2: to get all that stuff done repeatedly to if you
S2: can at least give agents like a certain level of
S2: guidance around how to do it. That could be, you know,
S2: maybe not that cheap yet, but like reasonably cheap relative
S2: to what it used to be. And so, yeah, I
S2: think your analogy of what imagine you're attackers are now
S2: 10 to 100 times in size each, each outfit, which
S2: they are like a lot of them are just businesses, right? Um,
S2: and so they're going to use AI in the same
S2: way we are. Like make us more efficient. Um, so yeah,
S2: I think security as an industry has to work backwards
S2: from there. Like imagine that that's the the current state
S2: or the future state and then work backwards from there.
S2: And what does that change about our current perceptions? Because
S2: I think a lot of our, a lot of our
S2: current ways of dealing with problems are, well, this will
S2: kind of be fine in today's world, right? It's like,
S2: you know, like I can deal with this many like
S2: back to initial conversation. I can deal with this many
S2: open vulnerabilities or I can deal with this slower way
S2: of responding to something, or I can deal with this
S2: slower a speed to get myself back online in the
S2: worst case scenario, whatever it might be. It's like, oh,
S2: we can deal with that. That's probably within the bounds
S2: of okay. But if all the attackers get 10 to
S2: 100 times bigger, in your words, like, um, by by
S2: getting more leverage, then which of those things are still
S2: okay and which are not okay? And then how do
S2: we start adapting what we do today in response to
S2: what that's going to look like? Um, and as I said,
S2: I don't know how quickly all that's going to happen, but, um,
S2: it's very hard to make a good argument why it's
S2: not why it's not going to happen.
S1: Yeah. Totally agree. And I guess one of the most
S1: tangible ways of thinking about this is like, how how
S1: large can you tolerate for a window of vulnerability? Uh, right.
S1: Like maybe previously it was like, you know, ten years
S1: ago or whatever, it was like a week or whatever.
S1: And let's say it was half a week, let's say
S1: it's a day. And I think we start to move
S1: towards a world and, and who knows how fast. But
S1: you start to move to a world where hours and
S1: minutes really matter. Um, where if you have an exposed
S1: S3 bucket before it. I mean, it takes time for
S1: the tech and very few people who were automating that
S1: stuff to actually find that exposed bucket or whatever. I
S1: think that goes down to minutes. You know, eventually, potentially
S1: even seconds. So yeah, I, I find the whole thing, uh,
S1: really fascinating. Um, what what are you guys doing? That's
S1: that's exciting right now, um, you're looking to put out
S1: soon or you're excited about you just released?
S2: Yeah. I mean, we're we're like, um, we're we're so
S2: new still that we're just getting out there into the world, really. So, like, we're, um,
S2: we're I'd say we're like, hitting the point now where
S2: we are close to making some pretty big announcements about
S2: about where we are with the product and stuff like that. Um,
S2: but yeah, I think what we are excited about in
S2: particular at the moment is we've kind of like cracked
S2: a lot of the triage side of the problem of like,
S2: how do I understand which vulnerabilities matter? Where we're excited
S2: a lot at the moment is like, how do we
S2: how do we start helping people more and more with
S2: the remediation side? As I said, I think for now
S2: that's more about like cutting down all the human work
S2: to a smaller window as possible. But if we do
S2: theoretically need to get to a point of minutes, then
S2: you need to cut out humans entirely eventually. Um, but
S2: I think you've got to go like step by step
S2: by step. But yeah, we are we're super excited about
S2: some of the stuff that we're seeing so far in
S2: that world because, yeah, it's a super complex problem, obviously,
S2: and it's something you can get very wrong. Right. If
S2: we're like taking down people's prod environments on a daily
S2: basis or something. But, um, done right, like it can
S2: actually take us from this. We're just like shuffling big
S2: lists of red around, which is kind of what it
S2: feels like sometimes last few years, uh, to a point of, okay,
S2: we actually feel like we're making tangible progress day in
S2: and day out without a ton of effort. And then
S2: maybe all those engineers and SREs and all the other
S2: people involved can suddenly go and spend 5% more of
S2: their time or something like that, to shipping product, and
S2: then hopefully everyone wins at that point. So yeah, we're
S2: really excited about getting more into that side. and also
S2: just excited about having the the initial, uh, you know,
S2: version of the product now out there in the world and,
S2: you know, people starting to use it.
S1: No. It's awesome. And where can people learn more about it?
S2: Um, they can catch up with me online, my LinkedIn
S2: or Substack. Um, feel free to, like, reach out to
S2: me there. We also have a website where you can
S2: kind of catch up a bit more on what we're
S2: up to.
S1: Well very cool Harry, thanks for the chat. Very, uh,
S1: very cool conversation.
S2: I really enjoyed it.
S1: All right. Take care. Unsupervised learning is produced on Hindenburg
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S1: podcast is available on the Unsupervised Learning YouTube channel, and
S1: the text version with full links and notes is available
S1: at Daniel. Com newsletter. We'll see you next time.