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. So I
S1: want to talk today about how to think about AI
S1: and cybersecurity, and specifically how to think about and what
S1: to build regarding AI for cybersecurity. There are a million
S1: different directions you could go, and you only have so
S1: much time and so many resources. So I want to
S1: give a possible direction here. So I want to frame
S1: everything today by asking and answering two questions. One, how
S1: does software and security change when we add agents, and
S1: how should we prioritize our efforts on adding AI and
S1: agents to our cybersecurity program? So these are the main
S1: ideas I want to talk about that. I'll kind of
S1: bring us around to answering those questions, and these ideas
S1: build on each other. So we're going to take one
S1: at a time. The first one is the concept of
S1: intelligence pipelines, which is a way to visualize workflows within
S1: a business. Second one is Theory of Constraints, which talks
S1: about how systems struggle to do things at scale and
S1: like where the blocker is. The third one is something
S1: I call AI state management, which is how I see
S1: I actually replacing most software. And the final one builds
S1: on all of those. And that's the AI security attack
S1: and defense framework. So let's start with something I call
S1: intelligence pipelines. So I started thinking about intelligence pipelines in
S1: the context of how AI will replace human workers. So
S1: these are explainable visualized workflows that show how business processes
S1: require human level intelligence. So imagine like you have someone
S1: named Mark and they work at a company called claim. Right.
S1: And this is what his day looks like, right. He
S1: does all these different tasks that you see in this diagram.
S1: The brain icons are where his human action is required.
S1: And let's say this is a world without AI agents.
S1: So this is where this is human work. This is
S1: things that only humans can do. And Mark is a
S1: really good employee because he could do 124 of these
S1: claims per week, and he has an 87% quality score.
S1: And it's really hard to find people like Mark. So
S1: he makes a lot of money. They give him nice
S1: perks because they don't want Mark to leave, because 124
S1: claims a week 87% quality. So that's insurance. This one's
S1: for a medical company. This is called Badspot. And Badspot
S1: is a company that reviews moles in person using licensed dermatologists. Now,
S1: Kim is one of the dermatologists here, and she sees
S1: 212 patients a week, has 92% accuracy. And again, it's
S1: really hard to find employees as good as Kim. And
S1: the key point here is that the only reason we
S1: have jobs at all is because of these blue icons, right?
S1: These blue brain icons are the specialty. They are the
S1: geniuses of like general human intelligence that somebody can do
S1: what Mark could do and Kim can do. Right? Is
S1: it a new spot? Analyze the mole. Was it dangerous before?
S1: You have to do these manual checks. This is human work.
S1: This is why we all have jobs, right? Otherwise, this
S1: would have just been like a script or automation. We've
S1: had programming and automation for years and years and years, decades.
S1: But there are certain things that only humans can do,
S1: and that's what these blue icons are. So I just
S1: want to stress that again, the only reason we have
S1: jobs is because somebody is paying us to be one
S1: of these blue icons. We as humans are inside of
S1: a workflow that looks like this. And I'm trying to
S1: get you to think of your job in terms of
S1: these workflows, because this is exactly how AI is going
S1: to think about your job, right when it comes to
S1: optimize it. Air quotes optimize. We don't normally think of
S1: our jobs or our pipelines or our workflows in terms
S1: of a visual flow like this. This is exactly how
S1: McKinsey is going to think about it, or KPMG or
S1: any random consultancy that comes in to optimize air quotes.
S1: Your company and your department and your team and your workflows.
S1: They're going to say, okay, tell me exactly what it
S1: is that you actually do. And they're going to produce
S1: some sort of visual. It's going to look something like
S1: this for your job. Your team's job, your department. And
S1: actually a collection of them will be your company. So
S1: you have to start thinking about this. That's why this
S1: is the first concept here. So this one's for a
S1: military intelligence company. So you have to look at satellite
S1: photos and do a bunch of analysis and then create
S1: narratives around them and write the report. So just like
S1: the other workflows, you see the different pieces and you
S1: see why they require human right? This is intelligence. You know, analysis.
S1: And Amir is the best at his job because he
S1: could do 12 of these assessments per week, and his
S1: accuracy is really high. It's 84%. This is an attacker
S1: organization based out of Eastern Europe. Let's say this is
S1: their primary attack workflow. Again, this is a company called
S1: cyber Attacks or whatever. So they find targets. They run
S1: recon tools. Again, these are things that manual security testers
S1: will have been doing for quite some time. And again,
S1: you can't just script everything right. You can script a
S1: lot of this stuff, but a lot of it requires
S1: the blue icon of human intelligence and human thought. And
S1: that's again why we are employed. So you've got to
S1: find and filter targets because they can only do so
S1: many assessments. Right. They got to run a bunch of
S1: recon tools. They attempt to exploit what they find, and
S1: then they try to do various tasks afterwards, like expand, uh,
S1: you know, get initial access, sell the initial access, you know, uh,
S1: gain persistence laterally, move things like that. So the core
S1: idea here is that human work can be broken down
S1: into workflows like this. Humans don't normally see things this way,
S1: but I can guarantee you this is how companies coming
S1: to replace you will see things. And unfortunately that's going
S1: to include your management. Right. That's going to include the
S1: C team and the board. They're going to be like,
S1: why can't we just do this with AI. So there
S1: are going to be thinking about how to make this
S1: kind of diagram for the work that you do, for
S1: the work that we all do. And by the way,
S1: like a year ago, uh, AI consulting. Yeah, this is 2024,
S1: I believe might have been 2023. I'm pretty sure it's 2024.
S1: McKinsey's consulting for AI was already 40% of their business, 40%.
S1: This is according to New York Times. So this thing
S1: is coming fast for us. So that was the first concept.
S1: Next one is Theory of Constraints, which a lot of
S1: you probably have already heard of. And it relates directly
S1: to the intelligence pipelines we just talked about. So the
S1: Theory of Constraints basically says that you should stop trying
S1: to optimize everything simultaneously, because not all problems are actually
S1: hurting you the same amount. It says the biggest thing
S1: hurting you is the slowest point in your overall workflow.
S1: Or as Goldratt puts it, your overall output is equal
S1: to the output of your worst piece, and that's what
S1: you should address first. My friend Joel works at OpenAI,
S1: and he works on a team that tries to figure
S1: out how to make AI benefit defenders as much or
S1: more than attackers, and we do a couple of 3
S1: or 4 hour walks a month. And one of these
S1: walks many, many months ago, he gave me like religion
S1: on this. In his mind, everything comes down to what
S1: Constrains attackers the most right now as well as defenders.
S1: And how is AI going to change or unblock those constraints?
S1: And his quote is attackers aren't constrained by a lack
S1: of access. They're drowning in access. Instead, it's the human
S1: labor currently required to exploit that access. That's the limit.
S1: Remove that and we are effed. And again, that's my
S1: friend Joel Parish. So think about that. We don't have
S1: a target problem and we don't have an access problem.
S1: We are stuck at the exploit phase. Right. And if
S1: you think back to those pipelines and workflows, you can
S1: kind of like put a little red mark around one
S1: of these brains, one of these blue brains, and you
S1: can actually make one of them red or something and say,
S1: this is the one that's really hurting us. Now start
S1: to think about AI. Now start to think about AI
S1: agents and saying, can we spin up 10 or 100
S1: or 1000 or 1 a million agents to help with
S1: this one particular spot, which, based on the theory of constraints,
S1: massively speeds up the entire pipeline because that was the blocker,
S1: that was the constraint. So thinking back to our pipelines
S1: and again, you want to think about what your pipeline
S1: looks like, right. For your productivity, your security workflows. Think
S1: about where they're currently constrained and how your constraints compare
S1: to your attacker constraints. Core idea here is to ask
S1: how AI will affect those constraints for both you and
S1: your adversaries. And once those constraints get unblocked, where does
S1: it get blocked next? Right. And how can we use
S1: agents to consecutively just keep unblocking or even apply the agents?
S1: I mean, kind of breaking away from the concept of
S1: theory of constraints, but you could kind of find all
S1: the blue icons and just say, hey, let's scale the
S1: crap out of this and let's try to increase our
S1: quality scores at the same time. So we were talking
S1: about 124 assessments previously. We're talking about 84% quality level. Well,
S1: if we actually have those metrics we actually have a
S1: workflow like this. We actually have a diagram. Well it
S1: just becomes a numbers game. Okay. Can we do a
S1: thousand assessments instead of 100. Can we take the 84%
S1: quality level to 85% or is it only 79%. But
S1: we're doing a thousand, so it's still worth it, right?
S1: Those are the types of questions people are going to
S1: be asking when they start doing this. So before we
S1: go into the next piece, I want to quickly cover
S1: my definition of agents because we're going to talk about
S1: agents a decent amount. So lots of different definitions out there.
S1: And I think it's good to level set before proceeding.
S1: So I think it's an AI system component that's capable
S1: of autonomously taking multiple steps towards a goal that previously
S1: would have required a human right. And if you break
S1: that down, it's a component, right? It's not all of AI.
S1: It's a piece of AI. It's autonomously pursuing multiple steps.
S1: So autonomously. So it's given a goal and it's autonomously
S1: chasing that goal by taking multiple steps on its own.
S1: That's the autonomous part. That's the goal part. And the
S1: last part is kind of the most important steps that
S1: could only be done by human previously. Right. So this
S1: means not scripting, not automation, not basic programming. Because if
S1: that were the case, it would already be scripted, right?
S1: So one or more of these steps that it's taking
S1: autonomously towards the goal can only have been done by
S1: human previously. And I think my overall favorite definition of
S1: AI is actually technology that does something cognitive that previously
S1: could have only been done by humans. So it's kind
S1: of in that mind frame. The next idea I want
S1: to talk about is a frame of thinking I've been
S1: thinking about for like the last six months or so,
S1: which I call AI state management. So the idea is,
S1: AI's ultimate form, or one of its ultimate forms, is
S1: to collect and understand the current state of a system
S1: to capture and articulate the desired state. You know, we
S1: know what the current one is. Now. We want to
S1: know how we wish it looked, and then use however
S1: many pieces of intelligence or agents, or thinking or reasoning
S1: or whatever you want to call it, which is basically
S1: AI to help us go from the former to the latter. Again,
S1: collect and understand the current state capture and articulate the
S1: desired state, and then use intelligence, automated intelligence AI. I
S1: didn't think that was actually an acronym, but, um, that
S1: was accidental, but. Yeah. Automated intelligence. Artificial intelligence is what
S1: it really means. But you're using all those tools available,
S1: reasoning agents, context, all of that to go from the
S1: state that you're in to the state that you want
S1: to go into, right. The state that you wish you were.
S1: This is an extremely powerful universal use case for AI
S1: as it gets more and more advanced, especially as it
S1: gets larger and larger context. Because even a small company,
S1: you can't fit everything into the context we currently have.
S1: The latest model that just came out has a 10
S1: million token context, but even that is not big enough
S1: to hold, you know, the full state of an IT system,
S1: the full state of the business, the full state of
S1: all employee activity. Like that's a lot of stuff. Plus
S1: you have to update it right every five minutes, every
S1: ten minutes, every 30s every hour. However often are you
S1: going to do. It takes a lot of context. This
S1: is the ultimate idea, the main concept here. Current state.
S1: Desired state. How do you transition? So in order to
S1: do this, you need to have a massive amount of
S1: data as I just talked about. And that state has
S1: to be updated as often as possible, maybe easier for
S1: a smaller company still. Actually not possible for a smaller
S1: company today, but soon it will be. But for larger enterprises,
S1: extremely non-trivial. We're talking about if you wanted a really
S1: high resolution state, I mean, that would have to be terabytes,
S1: at least gigabytes depending how much you summarize. But what
S1: you build is your current state, your desired state, and
S1: then you ask questions continuously as your main form of
S1: managing the program. And you finally get to recommendations from
S1: the system. It's really powerful and it's not really theoretical.
S1: I actually built a system like this in 23. So
S1: the contents of the context are of course everything. This
S1: is everything about the system. But the advantage is you
S1: can feed it your goals, your logs and your projects
S1: and your budget and your team. And what are people
S1: currently working on? What is the current status? All of
S1: this stuff, you know, all those stand up meetings or
S1: all those planning meetings that all kind of goes away?
S1: It's just data inside of this context. And I want
S1: to take a look at how it all fits together.
S1: So like I said, I built a system like this
S1: back in 23, very early after I guess ChatGPT came out.
S1: I started building this system, and it's a working system
S1: that uses this using a fictional company called alma. So
S1: I started off with tons of context about this fictional
S1: company history, mission, goals, projects, risks. Team members budget, all that.
S1: And then I said, okay, what questions do I actually
S1: want to ask about this? Because I do. My background is, uh,
S1: information security. So cyber security. And, uh, I've been doing
S1: a lot of security consulting, a lot of security assessment,
S1: a lot of security program management. And I have essentially
S1: been using this system before I came out, because it
S1: doesn't really require AI. It just requires really good questions
S1: and a really good understanding of the company. And I
S1: was doing that manually without AI. And now with AI,
S1: it's completely ridiculous. So let me show you some of it.
S1: So here's one of the most common questions that we
S1: get when we're managing a program. What is the list
S1: of projects that we're working on? And here's the answer.
S1: Notice the fact that I just gave it an echo. Right.
S1: This is a command line version of the system. But
S1: give me a list of the projects we're working on,
S1: along with a ten word summary. Okay. And now here's
S1: all of our projects. This is the type of thing
S1: that would take quite a while to get an answer on.
S1: If you're a manager or your director or the VP
S1: or whatever, and you're like, hey, what are we currently
S1: working on? Not too many places, unfortunately, have like an
S1: intranet that you can go to and just like get
S1: the current list. Turns out that things old, there's actually
S1: four versions of it. There's competing versions of it because
S1: some other team is doing the same thing. Actually, it
S1: lives in this Google doc. Oh wait, we got rid
S1: of that Google doc. That one's deprecated. Now go check
S1: this new one. Oh, it's also in confluence. Oh did
S1: you check Jira. Like it's a total mess. Here you
S1: are asking the single unified AI context and it gives
S1: you the answer. Here's another common one. What are our
S1: metrics that are related to our projects? How are they related?
S1: This is the type of thing. This is a giant
S1: blue icon. This is mental work that a human on
S1: your team must do to try to figure this out.
S1: This thing just instantly mapped it. Just like that. Instantly
S1: told you we are working towards fixing these metrics or
S1: improving these metrics. And that's why we're doing these projects
S1: like that. Here's another one. We start with the risks,
S1: and we want to see how those risks relate to
S1: our projects. Right. So we have our risk register in here.
S1: And we could see why we're working on particular projects
S1: because of these risks. And we have a mapping for that.
S1: So now we have a breakdown of why we're working
S1: on what. And we can ask questions like are we
S1: focusing on the right things with our efforts? Here I'm
S1: asking about remediating critical vulnerabilities on crown jewel systems. Look
S1: how clean this narrative is, right? Here's our progress on
S1: remediating critical vulnerabilities on crown jewel systems. Cool. This is
S1: the type of thing you can just copy and paste
S1: this and send it on to a leader or whoever
S1: you need to send it on to, assuming they have
S1: the access to see it. And yeah, shows a consistent
S1: reduction in the time taken to remediate critical vulnerabilities on
S1: crown jewel systems, improving from 21 days in October 22nd
S1: to less than six days by March 2024. That's a
S1: cool narrative and you've got the data right there. This
S1: is total critical remediation, and this one we're calling a
S1: pattern to actually output the graphable data. And this is
S1: for something called fabric, which I'll put a link to
S1: this video right here. Should be over my head or something.
S1: So this one says create a TRC graph. So now
S1: look at that. If you recognize that that's actually CSV.
S1: Guess what you could do with CSV. Boom. Now you
S1: have graph data you can add to any tool to
S1: get an instant visualization of progress over time, to put
S1: it in a presentation or whatever that just flew out
S1: of the eye automatically in a couple of seconds, right?
S1: So those are pretty cool. That's like level one. Cool.
S1: Let's move on to level two of pretty cool. And
S1: I can give you a million more examples because you
S1: can literally just ask anything of the system, and as
S1: long as it's somewhere in the context, it's going to
S1: make all the connections itself, right. But those don't truly
S1: show the power of telos file structure, which, uh, again,
S1: that's another project you could check out here, uh, which
S1: I put together a while back. But if you want
S1: to see the actual power of this, level two is
S1: where it starts. So let's look at at a more
S1: advanced example of like a what if question. So I
S1: tell the system that Nadya is leaving, uh, which she isn't,
S1: but I tell it to help me readjust things based
S1: on project priorities. And here's what it gives me. And
S1: keep in mind, the more detail I have in the
S1: team section about skill sets and experience, the better this gets.
S1: But look at this. Nadya is leaving the team and
S1: I need to give her projects to someone and or
S1: get additional help. Use your expertise as a risk professional
S1: to help me reassign her work and or get additional
S1: help keeping in mind our risks and priorities, especially our
S1: critical projects like that's a little bit unnecessary. Like, you
S1: can actually not give it that context and it's still
S1: going to figure it out. Look at this. Reassign this
S1: project to this other person. Assign this one to this
S1: other person. Hire a contractor to do the WAF install.
S1: And it's got reasoning for this. Keep in mind what
S1: we're asking is a complete and absolute cybersecurity expert. Okay.
S1: So it knows the different skill sets. It knows the
S1: different requirements. So it's giving us advice on what to outsource,
S1: what to do later. Just completely defer and what to
S1: prioritize in which person to give it to. That's insanely helpful.
S1: Insanely helpful. So what about adding new stuff to the context?
S1: How do you update the program? Well, because AI is
S1: seeing the whole picture, you could just add it to
S1: the bottom of the context. And if you're using Rag,
S1: you just add an additional entry. If you're using, you know,
S1: a single file or you're using CAG or you're using
S1: whatever you're using, you can simply append to it. You
S1: don't have to go in and clean and keep the
S1: thing perfectly, you know, upkept and maintained, because when it
S1: reads the update, it'll realize that that supersedes the previous thing.
S1: As long as you have like a timestamp, which we
S1: do here. So this is how you can update progress,
S1: for example, on a KPI, you know, July 2024 Criticals
S1: are now being fixed in nine days. So what I
S1: added here are a few lines with new metrics updates,
S1: bottom items here. And guess what? When I run my
S1: previous question, here's the graph that it produces. Look at that.
S1: It added new items to the graph to the CSV output,
S1: because we added them to the context. And the I
S1: just figured out okay, we're going to update the graph.
S1: So now let's see how powerful the system is for
S1: knocking out very time consuming everyday tasks for any security program.
S1: I mean, like so much of security is actually just
S1: doing this stuff, responding to auditors. Here, I'm telling it,
S1: I need a narrative on critical vuln remediation times for
S1: an external auditor. Not only wrote the narrative, but added
S1: in our actual numbers, it just wrote it with all
S1: the numbers already intertwined. This is the type of thing
S1: that can derail somebody in the security program for hours,
S1: or a couple of days to go and find all
S1: this data, see, pull from the different things or whatever.
S1: Like this just takes massive time away from security teams
S1: to be able to do this. So think about how
S1: much time an AI like this actually saves. And here's
S1: one for the executive team. And notice how it changes
S1: the tone according to who you're actually making the report for.
S1: And the more detail you put in the context file
S1: around sensitivities, reporting preferences, or in the reporting pattern you
S1: use for each audience, the better this output is actually
S1: going to be. So you can set these up for
S1: as many groups as you regularly report to, right? Internal team.
S1: The board. Auditors, internal, external, whoever. So those are pretty cool.
S1: Level three. Now let's look at some insanely helpful functions
S1: that will save even more time. So by a show
S1: of hands, who likes security questionnaires? Okay, let the record
S1: show nobody. This is absolutely insane. This is another section
S1: of the tlos file, the context file that I created
S1: for this fictional company. Okay. It's a set of updates
S1: to Alma's IT infrastructure and security controls. And just like
S1: everything else, look how casually these are written. This is
S1: like a logbook migrated from Google Workspace to Office 365,
S1: with MFA enabled for all users. It's not in a schema,
S1: it's not in a format. It's got a basic time
S1: stamp there. Rolled out Sentinel one on 50% of corporate
S1: laptops in August of 2020. That's all you need to say, right?
S1: And you see how I've got a history here. Okay.
S1: All the way back to July 2019. Basically, we're a
S1: soup sandwich. Back then, admin accounts still not required to
S1: use Toofar company laptops. No, MDM everyone is admin. It's it's,
S1: you know, mass chaos pandemonium. And then as the updates
S1: start going down through the months, it starts to get
S1: better and better. And this is just it's just a logbook.
S1: So now the question is we're being asked by a
S1: customer what percentage of endpoints are protected by endpoint protection.
S1: How often are you asked stuff like this in a
S1: security questionnaire all the time. And they'll ask it in
S1: a different way each time. So you can't have like
S1: a static question and static answer if you're in vendor management.
S1: You've been down that road before. So here's the answer
S1: written in a form we can copy and paste directly
S1: to the customer. All came back from alma, the AI system.
S1: Here's another one asking how much our infrastructure is US based.
S1: I asked to give me an answer for both cloud
S1: and on prem, and to give the response in an
S1: email gives me the 100% answer that comes from our
S1: Tlose context file. So we just recently got this where
S1: all of our stuff, all of our cloud environments, all
S1: our different AWS regions, all of our, you know, on
S1: prem stuff, it's all US based. That was an update
S1: that at the bottom of the Telos file, keep in
S1: mind the context file up here could have been the
S1: worst answer because that's the old stuff. But now that
S1: we have that updated, the answer is now 100% and
S1: we have it perfectly in an email, we can just
S1: send that to somebody. And ultimately we're heading towards being
S1: able to ask questions like these. How should we move
S1: from our current to desired state? Going back to that idea,
S1: we talked about what should be in our desired state, right.
S1: That that's a, you know, big brain question. Big brain
S1: question what should be in our desired state that you
S1: actually don't see in there? How about this? How will
S1: attackers exploit our desired state? Right. Natural. Follow on. How
S1: should we change that desired state to make it less exploitable?
S1: All looking at the same unified context. So the insane
S1: thing about this is realizing that most software is kind
S1: of the same in cyber. We're pulling in data from somewhere.
S1: We're trying to pull signal out of that data, and
S1: we try to do something with it. Right. But it's
S1: exactly the same with productivity apps or B2B processing or whatever.
S1: It's all the same. So the core idea here is
S1: that when the AI understands where we are and where
S1: we want to go, it can help us get there.
S1: And that's where this entire program management thing that I
S1: just showed you that I just demoed, that's real AI system,
S1: like it's sitting right here. I can ask you questions.
S1: And obviously my context file for this thing was a
S1: few hundred lines, right? I think it eventually got to
S1: a few thousand lines. But as the AI systems scale right,
S1: we've already got millions of tokens available. This is just
S1: going to get easier and easier to do at larger
S1: and larger scales. So we're going to be able to
S1: do the same kind of stuff even for a company.
S1: So this next idea is kind of a culmination of
S1: all of this in a way to prep for attackers
S1: in a more tactical way. Like what exactly are they
S1: going to launch at us, and how can we get
S1: ready for that? So the whole concept starts with asking
S1: one question what do attackers wish that they could do
S1: to us? What can they do now? What can they
S1: do in like six months from now? And what can
S1: they do in 18 months or 24 months or 36 months?
S1: And obviously, you can't know exactly the answers to those
S1: because you don't know what text exists at that time.
S1: You don't know how advanced the AI is going to be,
S1: how good agents are going to be. Right. But we
S1: can start with a giant list of attacker capabilities, some
S1: of which they have already, some of which they're about
S1: to get, some of which are coming soon and some
S1: of which are distant in the future. But if we
S1: start with what we know, the ideal is with what
S1: would be devastating to us. I'll give you an example
S1: of devastating to us. They can instantly identify all open
S1: services on the entire United States at any given time,
S1: and can launch an attack against all of those services simultaneously.
S1: Any of those attacks that get inside and are successful,
S1: they can pivot within 30s to compromise other systems internally.
S1: They can instantly crawl around and find all the different, uh,
S1: stuff like an advanced human red team. They could figure
S1: out what the business does, uh, what their most sensitive, uh,
S1: talking points are. Their most sensitive, like worst case scenarios
S1: are and it could right then the exploit where and
S1: the ransomware and the ransom notes and the verbiage and everything.
S1: And then it could actually call with AI and start
S1: negotiating the ransom because it already has the stuff. It's
S1: all zipped up. Oh, and by the way, they're already
S1: selling to all the different information brokers at the same time.
S1: And this took them four minutes. Okay. And they could
S1: do that to an entire country. That is science fiction
S1: right now. That is science fiction because we're talking about
S1: millions upon millions of IP addresses, web locations, APIs, websites,
S1: attack surface components, basically. Um, so IPS services, uh. Net
S1: ranges like think of how much work you have to
S1: do to actually go and do that in a short
S1: amount of time. Think of how much infrastructure you would
S1: have to have. So in this list, these capabilities are
S1: further down the list. So the idea of like, okay,
S1: look at a very small startup with ten people and
S1: find their infrastructure, find their subdomains, determine what open services
S1: are available, see if any open services are for sensitive services.
S1: Get back a list of those within ten minutes. Okay,
S1: that might be possible today. That might be possible in
S1: less than ten minutes. Uh, fairly soon. It depends on
S1: the capability of the attacker, but it's a type of
S1: thing that's within the realm of possibility. Further down the
S1: list is doing that for a larger company or doing
S1: it in five minutes instead of ten minutes. So now
S1: what you start to see is the better the tech gets,
S1: the more realistic these things become. You start moving down
S1: the list of capabilities until you start moving towards the
S1: sci fi worst case scenario I gave you in the beginning.
S1: So we basically start with this giant list of attacker capabilities,
S1: some of which they already have and most of which
S1: are not possible yet. For each one we collect a
S1: bunch of information for it, some metadata that will help
S1: us like figure out how it might be used against us,
S1: or how difficult it could be, like cost or talent,
S1: or scalability or constraints or whatever. Because this is what's
S1: going to determine how, when the tech improves, which parts
S1: of these are going to change. And here's what makes
S1: this thing so powerful. When I talk to companies who
S1: are thinking about cybersecurity and AI, the number one question
S1: I hear is what should we build? What should we build? Like, okay,
S1: I hear about agents, so I build agents. What do
S1: I build agents to do? What should they defend? Should
S1: they monitor like they don't know where to start? So
S1: this framework is a really good answer to that. It's
S1: basically saying, here's what's going to be thrown at you
S1: because here's what the attacker wants to do. So what
S1: are you doing to be ready for each of these?
S1: So you basically take this attacker capabilities. That's why it's
S1: Acad right. It's for defense as well. You use this
S1: attacker capabilities map to build your defense right. The same
S1: way that they're using AI to power this list of
S1: capabilities and move down the capabilities list. You could be
S1: doing exactly the same for defense. So what we end
S1: up with is an AI cyber infrastructure that basically looks
S1: like the attackers. We are constantly assessing ourselves. Basically, we
S1: are executing everything on this attacker capabilities list as a
S1: unified engine. It's a continuous kind of like a continuous
S1: red team type situation. Or in the current, you know, parlance,
S1: it's essentially a attack surface management or external attack surface management.
S1: But really that starts to merge with automated pen testing.
S1: Automated red team attack. Surface management. Like all these things
S1: start to merge into this AI powered execution of these
S1: attacker capabilities. And it's just continuous. And the same thing
S1: that the attackers are doing is what the defender has
S1: to be doing to themselves so they can get there first.
S1: As we get this capability up and running, the entire
S1: game becomes two things making sure we're adding new techniques
S1: to look for, right? So we're making this thing smarter.
S1: We're making it more knowledgeable, right. New techniques, new attack
S1: surface new services to watch out for, you know, new
S1: ways of finding information, whatever new threat Intel and making
S1: sure we find our issues. The second one here, making
S1: sure we find our issues and fix them faster than
S1: our attackers. Right. So this is a race. This is
S1: a giant game where they are running the Acad system.
S1: We are running our own Acad system, this automated continuous
S1: red teaming system that knows everything about our company. Right?
S1: Going back to the context thing. They're going to have
S1: this this giant context that knows everything about us too, right?
S1: But they have a disadvantage. They should have a disadvantage
S1: that they don't know all of our internal configs, hopefully. Right.
S1: So we should be able to move faster than them.
S1: But we have to start by building the same thing
S1: that we know they are building. So both attackers and
S1: defenders basically end up building a world model of our company.
S1: What we do, what we care about, what matters to us,
S1: what our weaknesses are, what's most valuable that we have
S1: to different types of attackers, and what we must avoid
S1: at all costs. Then we are looking at the capabilities
S1: map to see what new capabilities are coming online as
S1: a result of the advancing AI, we could say, okay, well,
S1: we don't have to defend against that yet. For example,
S1: we don't have to defend against an attacker that can
S1: see every single change and attack it within five seconds.
S1: We do not have to defend against that right now.
S1: That would cost tens of millions of dollars. We would
S1: have to hire a massive staff. We would have to
S1: massively scale our automation. It would be an investment that
S1: would sink the company, most likely because the company needs
S1: to be building products. They can't spend that much on security.
S1: So that's like a thing we don't have to worry
S1: about yet, because we know the attacker doesn't have it
S1: yet either. That's why this map is so important, because
S1: we could say as the tech starts improving, we could say,
S1: you are here, right? You are here on this level
S1: of like what is possible in the world given the
S1: current state of automation, given the current state of AI, right?
S1: So we're looking at the capabilities map to see the
S1: new capabilities coming online as a result of advancing AI.
S1: And we could say, okay, yeah, this is where we
S1: need to be currently. This is where we need to
S1: be thinking about coming up soon, because we know that's
S1: what our attackers are about to get. So it's not
S1: just what you need to build, but it also helps
S1: you inform you know when to make what investment of
S1: what size in what area. So I'm still talking to
S1: a few people about how much of this I should
S1: actually release, but I'm pretty sure attackers are going to
S1: figure this out anyway. So I think the priority needs
S1: to be on enabling defenders. We've got over 60 capabilities
S1: so far for attackers and around 40 so far for defenders,
S1: because even though the defenders should be using the attack
S1: map as well, there are also defender capabilities. And this
S1: is something Jason Haddox and I talk about a lot.
S1: And I think his idea was to start with a
S1: defender side basically like what is the what are the
S1: core things like automated SoC and uh, you know, incident
S1: management and stuff like that. What are those things that
S1: we could use current AI to just improve? Um, so
S1: I think we're we're thinking about the combination of the
S1: two together. And, uh, yeah, we've got 60 attacker, 40
S1: defender right now. And, uh, looking to release this within, uh,
S1: 30 to 60 days, uh, depending on a few outstanding conversations.
S1: So what I love about it is it can give
S1: us tremendous focus. We can base our efforts on what
S1: we expect to face in the real world. And based
S1: on the combination of what AI is possible and what
S1: the attacker wishes they can execute. So what I love
S1: about this is how it can just give us tremendous
S1: focus as defenders, right? We can base our efforts on
S1: what we can expect to face in the actual world
S1: from our attackers. And based on this combination of what
S1: AI is possible and what the attacker wishes that they
S1: could do to us. So I want to end by
S1: answering the two questions we started with. How does software
S1: and cybersecurity change? When we add AI and specifically agents,
S1: it changes everything. It replaces human intelligence in workflows, right?
S1: And it does so at a scale that addresses the
S1: theory of constraints problem for attackers, making them far more capable.
S1: That's what's going to allow them to move through these
S1: stages of the attacker capabilities map. Right. It is solving
S1: this the various pieces of the theory of constraints. Second
S1: question how should we prioritize our efforts around adding AI
S1: agents to our cybersecurity program? Start by building your AI
S1: state management system. Now I'm calling this like a UCC,
S1: a unified company context. And there's a million companies working
S1: on this, right? Microsoft has their own version. You know,
S1: Databricks is working on theirs. Splunk is probably working on theirs.
S1: I imagine the game is about to become bringing all
S1: company data into a thing that I can see and
S1: hold in its brain all at once. I'm calling it
S1: UCC Unified Company Context. But who knows what Gartner is
S1: going to call it? Like, everyone's just going to like,
S1: figure this out, uh, fairly soon. So you've got to
S1: start building this thing now because your attackers are going
S1: to be building a UCC for you, which all their
S1: attacker tools are going to then use to attack you
S1: on a continuous basis. If you weren't vulnerable this morning
S1: at 9 a.m., maybe you will be at noon. It's
S1: going to check again when something they learn that you
S1: just acquired a new company, you have a new attack surface. Oh,
S1: maybe they'll now attack you now because of that. Maybe
S1: they learn things through your bug bounty. Maybe. Whatever it is,
S1: the context and the situation on the ground continues to change.
S1: So they're going to have this world model of you.
S1: You need to have a better one. That's the trick
S1: of this. And the second thing you want to be
S1: doing with this context that you have is figuring out
S1: what your desired state is and what your current state is.
S1: And this is going to allow you to say things like,
S1: what should I fix first? Vulnerability prioritization. I've been in
S1: the vulnerability management space forever, and it's so frustrating to
S1: try to figure out how to prioritize a vulnerability. And
S1: people are like trying to put that information inside the
S1: vulnerability itself with like a CVE rating or something. You
S1: can't get the prioritization from the vulnerability itself. It has
S1: to come from the context of where it's affecting something, right?
S1: The company itself. So this UCC is going to be
S1: the context for doing vulnerability management So you're talking about
S1: current state to desired state. You're going to say, I
S1: don't want any critical vulnerabilities in my crown jewel systems.
S1: That's one of my items in my desired state. Now
S1: when you ask the question, what can I do to
S1: make that real? It will then say, well, based on
S1: the fact that you have these vulnerabilities and these things
S1: in your risk register, and the fact that we know
S1: all these dev teams and we know what dev teams
S1: are using, what GitHub repos, and we know what code
S1: changes go in and we know who owns what applications.
S1: This is the holy grail. This is the thing that
S1: management has never had which you will have due to
S1: unified company context. You'll then be able to find the
S1: actual vulnerability that can be fixed by the actual developer
S1: in the actual code repo, and you can submit your
S1: own PR and just have them approve the PR right.
S1: This is the type of thing that you could do
S1: with this unified company context. So the next piece is
S1: start working on your set of defender capabilities. Based on.
S1: Now you have your own context. You understand the attacker
S1: context that they're going to be moving through as this
S1: tech tree improves. And now you can start improving your system.
S1: You can start assessing yourself continuously using these same exact
S1: techniques and using your own internal UCC. And if you
S1: do that, I think you're going to be in extraordinary shape.
S1: That's what I want to share today, and we'll see
S1: you in the next one. Unsupervised learning is produced on
S1: Hindenburg Pro using an SM seven B microphone. A video
S1: version of the podcast is available on the Unsupervised Learning
S1: YouTube channel, and the text version with full links and
S1: notes is available at Daniel Missler newsletter. We'll see you
S1: next time.