Speaker 1: Welcome to the deep dive.
Speaker 2: We're here to cut through the noise, pull out the
Speaker 2: insights that matter really just for you today, we are
Speaker 2: diving deep into something moving incredibly fast, this escalating digital
Speaker 2: arms race. Ian cyber threats are just increasing. They're getting well,
Speaker 2: shockingly sophisticated. You've got nation state actors, advanced machine learning.
Speaker 2: They're acting as real force multipliers for the attackers. So
Speaker 2: our deep dive today it's about reinforcement learning RL. It's
Speaker 2: this cutting edge part of AI, and it's not just
Speaker 2: theory anymore. It's becoming a really powerful practical tool. It's
Speaker 2: fundamentally changing cybersecurity, especially in well critical area of penetration testing.
Speaker 2: Our mission really is take you on a journey. We'll
Speaker 2: look at the core ideas of RL, how it's actually
Speaker 2: being used in cyber ops, highlight the challenges, the clever
Speaker 2: solutions popping out, and then look at the real world
Speaker 2: uses in this future of AI combating AI. The goal
Speaker 2: is simple, give you a shortcut to being genuinely well
Speaker 2: in formed, maybe offer some surprising insights practical takeaways. Okay,
Speaker 2: so with that laid out, let's unpack this traditional penetration testing.
Speaker 1: It's absolutely vital for securing our digital world, right, but
Speaker 1: it's also, let's be honest, often seems like a slow,
Speaker 1: manual and incredibly complex undertaking. Is that fair?
Speaker 3: Oh? Absolutely, that's right at the heart of the challenge
Speaker 3: pen testing. Yeah, it is where these highly technical red
Speaker 3: teams simulate real attacks, trying to find the holes in
Speaker 3: an organization's defenses. And it's crucial. I mean identifying weaknesses,
Speaker 3: prioritizing where you spend your security budget, tuning defenses, meeting
Speaker 3: compliance like PCI or a pally for the execs, it's
Speaker 3: risk management, reputation for DEFF teams, it's baking security in
Speaker 3: from the start, super important stuff. But what's fascinating here
Speaker 3: and a bit problematic, is how this critical process, which
Speaker 3: is really labor intensive to struggles. It struggles with the
Speaker 3: sheer volume of data in modern networks. You know, despite
Speaker 3: having brilliant human testers, the results are often found through
Speaker 3: well manual, tedious means. It's just an overabundance of information, logs,
Speaker 3: network endpoints. It's overwhelming. Even when you use automated tools
Speaker 3: made sense of it all, that's still a huge challenge
Speaker 3: for the analyst. So it really begs the question, doesn't it.
Speaker 3: How can we possibly scale human expertise to keep up
Speaker 3: with this constantly growing threat landscape?
Speaker 1: Right, and that sounds like the perfect entry point for AI,
Speaker 1: specifically reinforcement learning as this force multiplayer you mentioned. So
Speaker 1: how does RL actually step in? What lets it chew
Speaker 1: through these mountains of data that swamp human teams?
Speaker 3: Well, AI capabilities they've just improved so dramatically. RL models
Speaker 3: can now sift through, I mean, mountains of data that
Speaker 3: maybe was ignored before. They find patterns, anomalies, these sort
Speaker 3: of graph linked epiphanies. It just massively accelerates the ability
Speaker 3: to spot and stop bad actors. It really is about
Speaker 3: using AI to combat AI, or at least AI to
Speaker 3: combat the complexity that modern systems and threats bring.
Speaker 1: Okay, let's break down RL itself. The taxi driver analogy
Speaker 1: is pretty classic, right, helps make it concrete. Imagine you're
Speaker 1: a taxi driver. Your goal maximize fares in a city,
Speaker 1: that whole city, the traffic, passengers, time of day, that's the.
Speaker 3: Environment exactly, and your states are your current situation, like
Speaker 3: where your taxi is right now, the time, the weather.
Speaker 3: In cyber terms, that translates to things like the network
Speaker 3: can fig maybe a host status. Is it up as
Speaker 3: we scanned it? What access level do we have?
Speaker 1: Then you have actions, the choices the driver makes. Go downtown,
Speaker 1: wait at the station in cyber that's your scans, your exploits,
Speaker 1: trying to get higher privileges on a machine you've popped.
Speaker 3: And absolutely crucial for learning. The reward. That's the feedback
Speaker 3: for the driver. It's the fare, simple enough. In cybersecurity simulations,
Speaker 3: it's often framed as costs or penalties for certain actions,
Speaker 3: maybe a big lump sum reward for hitting a key
Speaker 3: objective like getting domain admin.
Speaker 1: But here's a really elegant part. I think Markov decision
Speaker 1: processes MDPs. Instead of the driver needing to remember every
Speaker 1: single fare they've ever collected to decide where to go next,
Speaker 1: which would be crazy, MDPs simplify things. They focus on
Speaker 1: the present moment, the here and now. This lets the agent,
Speaker 1: our driver or our cyber agent, make quick, informed decisions
Speaker 1: based on the current state, not the entire history. It's
Speaker 1: about what matters right now, makes.
Speaker 3: Sense it does, it makes the problem tractable, and finally
Speaker 3: you have the objective function. This is the mathematical goal.
Speaker 3: The agent tries to maximize total fair maybe and often
Speaker 3: it's a discounted sum of future rewards, meaning rewards you
Speaker 3: might get way down the line are seen as less
Speaker 3: valuable than rewards you can get right now. It reflects
Speaker 3: that real world trade off, immediate gains often feel more important.
Speaker 1: So here's where it gets really interesting. Imagine teaching a
Speaker 1: computer to think like a hacker by letting it continuously
Speaker 1: interact with a simulated network. That's essentially what reinforcement learning
Speaker 1: allows us to do in cybersecurity, and you mentioned combining
Speaker 1: this with neural networks that gets us into deep reinforcement
Speaker 1: learning DRL.
Speaker 3: Exactly. DRL uses those neural nets to handle well incredibly
Speaker 3: complex inputs and figure out sophisticated strategies or policies and
Speaker 3: specific algorithms. You mentioned PPO proximal Policy optimization. That one,
Speaker 3: along with others like DQN or A to C has
Speaker 3: been really key. PPO especially brought a lot more stability
Speaker 3: and efficiency to the training process. It lets us actually
Speaker 3: apply these powerful learning methods to really large complex network
Speaker 3: simulations without things going completely off the rails.
Speaker 1: Okay, so the theory sounds powerful, but how do we
Speaker 1: actually connect this theoretical AI agent to a real, messy,
Speaker 1: live network. That sounds like a huge leap.
Speaker 3: It is a huge leap, and that's where this grounding
Speaker 3: problem comes in. You have to make sure the AI
Speaker 3: is understanding its representation of reality is actually tied accurately
Speaker 3: to the system it's interacting with.
Speaker 1: Right, how do you bridge that gap between the clean
Speaker 1: model and the well the chaos of a real corporate network.
Speaker 3: The key approach involves a high level architecture. It's often
Speaker 3: called something like the layered reference model or LRMSHRAG. Think
Speaker 3: of it like building layers of maps for the AI,
Speaker 3: each one adding more detail. First, you take info from
Speaker 3: the real network and abstract it into an attack graph.
Speaker 3: This graph then becomes the foundation for the mark decision process,
Speaker 3: the MDP. That's the environment the URL agent actually learns inside.
Speaker 1: Okay, an attack graph is the base map.
Speaker 3: Exactly, but the crucial part is layering more context onto
Speaker 3: that basic MDP. First, there's a terrain MDP. This layer
Speaker 3: adds concepts of cyber terrain, so firewalls become obstacles. Maybe
Speaker 3: an intrusion detection system and IDs has a fuel to fire.
Speaker 3: It borrows from military ideas. Actually, intelligence preparation of the battlefield,
Speaker 3: understanding the environment to predict moves, so.
Speaker 1: Mapping the cyber landscape strategically makes sense.
Speaker 3: Then you add an adversary MDP. This layer tailors the
Speaker 3: environment to specific types of attackers, maybe using node attack
Speaker 3: templates or reflecting the capabilities of your own red team.
Speaker 1: So modeling different kinds of threats precisely.
Speaker 3: And finally, a task MDP. This refines the whole setup
Speaker 3: for specific goals. Are you doing crown jewel analysis trying
Speaker 3: to find exfiltration paths? The task shapes the environment and rewards,
Speaker 3: and importantly, as networks change or tasks change, these agents
Speaker 3: don't always have to start from scratch. They can use
Speaker 3: transfer learning to share knowledge between tasks, or even metal
Speaker 3: learning basically learning how to learn more efficiently to adapt quickly.
Speaker 3: So this whole layered approach, that's how we connect the
Speaker 3: theory to the practice. It gives a structure needed to
Speaker 3: make these AI driven cyber operations actually feasible on real networks.
Speaker 1: Okay, but even with that structure, there must be massive
Speaker 1: practical challenges. You mentioned scaling earlier. Real companies have networks
Speaker 1: with what tens of thousands.
Speaker 3: Of machine oh, easily tens of thousands of hosts is
Speaker 3: not uncommon in large enterprises, and that scale is a
Speaker 3: huge problem for ROL models. If your model doesn't scale well,
Speaker 3: it becomes incredibly computationally expensive. Training takes forever, or maybe
Speaker 3: just won't converge, meaning it never settles on a good strategy,
Speaker 3: or worse, the reward signal just keeps bouncing around wildly,
Speaker 3: never improving. The simulation becomes useless, slower than just using
Speaker 3: a human.
Speaker 1: Team, and the attack grafts themselves must explode.
Speaker 3: In size exponentially. Traditional attack graft generation just blows up
Speaker 3: as you add hosts. You end up with these unbelievably vast,
Speaker 3: complex decision spaces for the RIL agent to explore. It's
Speaker 3: like going from tic tac toe to I don't know,
Speaker 3: forty chess with millions of pieces.
Speaker 1: Wow. Okay, So how on earth do you make that
Speaker 1: manageable for an AI? How do you simplify the choices?
Speaker 3: That's where action space simplification comes in. You have to
Speaker 3: make the problem tractable. Strategies include things like reducing the dimensions,
Speaker 3: maybe focusing only on the most relevant actions at any
Speaker 3: given point, or combining similar actions into more general ones
Speaker 3: using hierarchical action spaces is another key idea to teach
Speaker 3: the agent high level goals first, like gain access to
Speaker 3: subnet X, before it learns the specific low level steps.
Speaker 3: It's about smart.
Speaker 1: Abstraction makes sense, giving it a better way to think
Speaker 1: about its options. What about the realism challenge, especially with rewards,
Speaker 1: You need the AI to value things like a real attacker. Right.
Speaker 1: You mentioned CVSS scores earlier. The zero to ten vulnerability
Speaker 1: rating Okay, you said, has.
Speaker 3: Limits, big limits in this cond text. CBSS is standardized,
Speaker 3: which is good, but it focuses purely on technical severity.
Speaker 3: It often lacks crucial context, like what's the actual business
Speaker 3: value of the data on that server? Or they're compensating
Speaker 3: security controls already in place. It's also static, it doesn't change,
Speaker 3: and it doesn't really capture human factors.
Speaker 1: So a critical vulnerability on a test server isn't the
Speaker 1: same risk as a medium one on the main financial
Speaker 1: database exactly.
Speaker 3: CBSS doesn't capture that nuance. It's not really a measure
Speaker 3: of risk, just technical severity, and it definitely doesn't generalize
Speaker 3: well to evaluating an entire attack path with multiple steps.
Speaker 1: So how do you inject that realism that context.
Speaker 3: Well, real attackers think holistically, don't they. They weigh factors
Speaker 3: beyond just the technical vulnerability. They look at the cyberterran
Speaker 3: firewalls IDs detection potential. So the reward system needs to
Speaker 3: mimic that. We need to build in that contextual awareness.
Speaker 3: One way is using these service based penalties we talked
Speaker 3: about them, assigning different negative rewards or costs based on
Speaker 3: the type of service being attacked, Like attacking authentication services
Speaker 3: might get a my nine to six penalty hitting data
Speaker 3: services man at four, maybe security or common services man
Speaker 3: of two. The exact numbers are relative tune for the simulation,
Speaker 3: but they reflect the proportional risk to the organization, higher
Speaker 3: penalty for hitting more critical services.
Speaker 1: Got it, So penalties reflecting business impact. Essentially. Now bringing
Speaker 1: this all together is scaling the realism. What's the approach
Speaker 1: that's really making this work in practice? The workhorce solution.
Speaker 3: A really promising combination that's emerged is known as double
Speaker 3: agent plus PPO or DAPPO. It starts with the double
Speaker 3: agent architecture the DAA. Instead of one monolithic AI trying
Speaker 3: to figure everything out, you have two specialized agents working together.
Speaker 3: There's an exploration agent whose job is to decide which
Speaker 3: host to target next, and then there's an exploitation agent
Speaker 3: that decides which specific action or exploit to use on
Speaker 3: that chosen host.
Speaker 1: Ah, So like a team, one doing recon in target selection,
Speaker 1: the other handling the actual at execution.
Speaker 3: Precisely, this decomposition makes the whole learning problem much more tractable.
Speaker 3: Each agent has a smaller, more focused learning space, and importantly,
Speaker 3: it's quite conceptually sound from an attacker's perspective. Real attackers
Speaker 3: often think in terms of where do I go next?
Speaker 3: And then what do I do once I'm there?
Speaker 1: Okay, that makes intuitive sense. Splitting the problem HEALTHS and
Speaker 1: the PPO part proxim policy optimization.
Speaker 3: That's the other key piece. Applying PPO to both of
Speaker 3: these agents provides the stability and efficiency we talked about earlier.
Speaker 3: PPO is just much better than some older algorithms like
Speaker 3: say A to C, especially for complex problems. It gives
Speaker 3: you stability, robustness, and sample efficiency, less data needed to learn,
Speaker 3: less likely to get stuck, and this combination the double
Speaker 3: agent architecture powered by PPO is what has really enabled
Speaker 3: these systems to scale effectively to networks of thousands of nodes.
Speaker 3: It keeps the learning stable even in huge environments.
Speaker 1: So essentially, instead of one AI trying to do everything,
Speaker 1: we're giving it a specialized team and a really smart,
Speaker 1: stable way to learn. Allows it to tackle networks far
Speaker 1: larger than before. It's like having that reconnaissance expert and
Speaker 1: an exploit expert working together, powered by the best learning methods.
Speaker 3: That's a great way to put it.
Speaker 1: Okay, So these aren't just lab experiments. You're saying, this
Speaker 1: dappo approach, and these layered models are actually being used
Speaker 1: now for real cybersecurity tasks.
Speaker 3: Yes, absolutely, we're seeing RL applied in several practical ways.
Speaker 3: One key area is crown Jewels analysis or CJARL. Here,
Speaker 3: RL models are trained specifically to find the most effective,
Speaker 3: often the stealthiest paths to compromise an organization's highest value assets.
Speaker 1: They're crown jewels, so finding the quickest way to the
Speaker 1: most important.
Speaker 3: Stuff, not just the quickest, but often the path of
Speaker 3: least resistance or least detection. The insights you get provide
Speaker 3: a really nuanced understanding of attackers methods of discreetly navigating
Speaker 3: through networks. It can reveal attack pads you simply want
Speaker 3: and have thought.
Speaker 1: Of manually exposing those hidden routes. What else?
Speaker 3: Another big one is discovering exfiltration paths. The focus here shifts.
Speaker 3: It's not about getting in anymore, but about how attackers
Speaker 3: get sensitive data out after a breach while trying to
Speaker 3: minimize detection ah getaway plan exactly. The model has to
Speaker 3: consider things like protocol and payload considerations. Agents might learn,
Speaker 3: for example, to use specific protocols like tunneling exful traffic
Speaker 3: through domain name systems DNS because DNS traffic often looks
Speaker 3: benign and isn't heavily scrutinized. Very They can also learn
Speaker 3: to use strategic pauses to avoid detection, mimicking low and
Speaker 3: slow techniques, or maybe they learn to stick to just
Speaker 3: one protocol consistently to better blend in with benign or
Speaker 3: otherwise unmonitored traffic. It's about modeling that stealthy data theft.
Speaker 1: That's fascinating and it keeps going. Oh yes.
Speaker 3: Another application is discovering command and control.
Speaker 1: Channels C two channels right the phone helme mechanism for malware.
Speaker 3: Precisely, these are the pathways that malware, once it's inside
Speaker 3: and undetected, uses to get instructions from its operator and
Speaker 3: send back stolen data or status updates. It has to
Speaker 3: execute nefarious tasks under direction. RL agents can learn how
Speaker 3: to establish and maintain these channels, figuring out how to
Speaker 3: navigle through firewalls, again using strategic pauses sleep actions to
Speaker 3: lie low and avoid detection. They might even learn optimal
Speaker 3: data upload speeds may be consistently choosing fast upload options
Speaker 3: overslow if the coast seems clear, balancing speed against the
Speaker 3: risk of setting off alarms. It reveals how persistent threats
Speaker 3: maintain their foothold.
Speaker 1: Incredible, So mapping out not just the break in, but
Speaker 1: the long term occupation and data theft too.
Speaker 3: Exactly and perhaps one of the most advanced applications is
Speaker 3: exposing surveillance detection routes or SDRs. This is like super
Speaker 3: advanced reconnaissance. The goal is to find paths an attacker
Speaker 3: could use to gain maximum surveillance exposure, learn as much
Speaker 3: as possible about the network while simultaneously minimizing opportunities of
Speaker 3: being detected. The ultimate stealth recon.
Speaker 1: Maximum info, minimum footprint. How does that work?
Speaker 3: One really interesting technique used here is a warm up
Speaker 3: phase before the RL agent starts actually learning and updating
Speaker 3: its strategy based on rewards. It first explores areas of
Speaker 3: the network deemed safe to explore without changing its internal weights.
Speaker 3: It just gathers initial information cautiously.
Speaker 1: Like a human operator, carefully mapping out the surroundings before
Speaker 1: making any risky moves.
Speaker 3: Exactly like that, it mimics that initial caution. This warm
Speaker 3: up sets the stage for more efficient and targeted learning
Speaker 3: later on.
Speaker 1: And does this also show different attacker styles?
Speaker 3: Yes, very clearly. By adjusting the penalty scales how much
Speaker 3: the agent is punished for potentially being detected, you can
Speaker 3: simulate different adversary behaviors in different levels of risk aversion.
Speaker 3: For instance, with a low penalty scale, say a value
Speaker 3: of one, the agent acts more like a smash and
Speaker 3: grab operator or maybe a less experienced attacker. It might
Speaker 3: perform noisy scams, not caring as much about stealth.
Speaker 1: Okay, the loud attacker, right.
Speaker 3: But if you crank up the penalty scale maybe two
Speaker 3: to eleven, the agent starts behaving very differently. It acts
Speaker 3: more like highly competent actors like nation state actors or
Speaker 3: apts advanced persistent threats. It displays highly risk averse behavior
Speaker 3: chooses the most direct paths that minimize exposure, tries to
Speaker 3: minimize its overall footprint. It becomes incredibly stealthy.
Speaker 1: So you can model specific threat actors from script kitties
Speaker 1: to spies just by tuning the AI's aversion to risk.
Speaker 3: That's the idea. It allows defenders to anticipate the specific tactics, techniques,
Speaker 3: and procedures the TTPs associated with different adversary profiles.
Speaker 1: These aren't just theoretical models. They're literally showing us how
Speaker 1: attackers might move through a network, whether they're looking for
Speaker 1: most valuable data or trying to stay hidden. It's like
Speaker 1: having a crystal ball for cyber defense, revealing attacker TTPs
Speaker 1: even before they strike. It's quite remarkable.
Speaker 3: It really shifts the perspective for defenders.
Speaker 1: So, looking ahead, what does this all mean for the future.
Speaker 1: We're in this AI versus AI situation or heading deeper
Speaker 1: into it. What are the next frontiers beyond these simulation applications.
Speaker 3: Well, the applications are expanding rapidly. We're seeing AI, including
Speaker 3: oral principles, move more into active threat detection, shifting away
Speaker 3: from just relying on known signatures of malware towards behavioral
Speaker 3: based detection using sophisticated AML to spot anomalies, unusual patterns
Speaker 3: of activity that might indicate a novel, never before seen threat.
Speaker 3: Protecting against the unknown unknowns, So spotting.
Speaker 1: Bad behavior even if you don't recognize a specific tool exactly.
Speaker 3: And related to that is specific ransomware detection. We can
Speaker 3: simulate the entire ransomware life cycle, the initial spread, installation, staging,
Speaker 3: data encryption, and also simulate defenses like honeypots.
Speaker 1: Ah those decoy systems designed at trap attackers.
Speaker 3: Right, AI can help optimize honeypop placement and analyze the
Speaker 3: behavior of attackers who fall into them.
Speaker 1: What about offense? Can AI actually create new attacks?
Speaker 3: That's one of the really disruptive possibilities, the potential for
Speaker 3: AI models to perhaps invent new atomic level vulnerabilities, maybe
Speaker 3: by fuzzing or analyzing code and novel ways automating penetration testing,
Speaker 3: not just by orchestrating known exploits, but by discovering entirely
Speaker 3: new ones at a granular level. That's a big step.
Speaker 1: Wow, Okay, that's significant. What else on the.
Speaker 3: Horizon asset discovery and classification. Imagine AI models that can
Speaker 3: infer the role of a server or the type of
Speaker 3: data holes. Ah, there's likely PII in here just from
Speaker 3: analyzing network traffic or scan results even with limited initial.
Speaker 1: Information, making sense of the network automatically.
Speaker 3: And attribution assisting human analysts in identifying and assigning responsibility
Speaker 3: to threat actors. There's research into using metric learning, essentially
Speaker 3: comparing patterns seen in live network data flows end points
Speaker 3: against a library of synthetic attack paths generated by URL
Speaker 3: agents trained to mimic different known actors. This could potentially
Speaker 3: allow for zero ROO attribution identifying a new campaign launched
Speaker 3: by a known group even if the specific tools.
Speaker 1: Are new, Identifying the actor behind a novel attack almost immediately.
Speaker 1: That would be huge for.
Speaker 3: Response game changing and finally, defensive modeling. Moving beyond static
Speaker 3: pre programmed response is like if you see this block
Speaker 3: that IP towards truly AI driven defenses that can dynamically
Speaker 3: analyze an ongoing attack and choose the optimal countermeasures in
Speaker 3: real time, adapting as the attack evolves active intelligent defense.
Speaker 1: This really paints a picture of an accelerating arms race.
Speaker 1: We're going to see true AI attacks. Aren't we not
Speaker 1: just humans using AI tools, but AI directing the attack?
Speaker 3: It seems inevitable. Malicious actors will likely use RL and
Speaker 3: other mL techniques to automate complex attack patterns, including the
Speaker 3: initial scanning and enumeration feases which are often tedious and
Speaker 3: think about social engineering. AI could be used for honing, refining,
Speaker 3: and using more efficiently these attacks, crafting hyper personalized phishing emails,
Speaker 3: maybe even generating real, realistic, relevant, customized synthetic media voice
Speaker 3: video for spearfishing, or disinformation.
Speaker 1: Deep figs for hacking. That's UNSOI it is.
Speaker 3: And attackers could use mL defensively too, in a sense
Speaker 3: observe how defenses like IDs or anti virus react to
Speaker 3: their probes and then use that feedback to craft malware
Speaker 3: or just simply hone their techniques to avoid detection, learning
Speaker 3: to bypass our security controls, so.
Speaker 1: The AI learns how to be invisible to our AI defenses.
Speaker 3: That's the adversarial dynamic, and a key challenge for a
Speaker 3: defensive AI is generalization. How do you get an RL
Speaker 3: model trained on one network simulation to perform well on
Speaker 3: a completely different real world network it's never seen before.
Speaker 3: That's where techniques like metal learning learning how to learn
Speaker 3: how to adapt quickly to new environments become absolutely critical,
Speaker 3: And this raises a fascinating, maybe provocative thought. Cybersecurity might
Speaker 3: be uniquely suited for AI evolution. How so think about it.
Speaker 3: It's perhaps the one domain of AI application that presents
Speaker 3: the can conditions for true evolution. Why Because it's AI
Speaker 3: existing in its natural environment. It's constantly interacting with other software,
Speaker 3: with hardware, with networks, and crucially with other AIS, both
Speaker 3: friendly and adversarial. It's a dynamic, competitive ecosystem. It might
Speaker 3: be the first place where human intelligence is really forced
Speaker 3: to turn the keys over to an AI that truly
Speaker 3: surpasses us, simply because the speed and complexity demand it.
Speaker 1: That's a huge point an environment driving AI evolution. Because
Speaker 1: the stakes are so high and the interaction so constant, it.
Speaker 3: Really raises an important question, doesn't it. As these AIS
Speaker 3: become more capable, especially in a competitive space like cyber
Speaker 3: how do we ensure we design them responsibly? The arms
Speaker 3: race dynamic likely means we will build them to be
Speaker 3: as effective as possible, even if their intelligence isn't human like.
Speaker 3: We need them to serve our defensive purposes.
Speaker 1: A profound challenge layered on top of the technical ones.
Speaker 1: So let's try to wrap this up. This deep dive,
Speaker 1: I think has really shown how reinforcement learning isn't just
Speaker 1: tweaking cybersecurity, it's fundamentally transforming it. We're moving away from
Speaker 1: these cumbersome, often slow, manual processes towards dynamic AI driven insights,
Speaker 1: insights that can mimic, an dissipate, and hopefully counter even
Speaker 1: the most sophisticated adversaries out there. This ongoing AI versus
Speaker 1: AI arms rights. While it has huge implications for everyone
Speaker 1: really understanding these evolving capabilities, it's not just for the
Speaker 1: cyberpros anymore. It's relevant for anyone who lives or works
Speaker 1: in our digital world. So hopefully we've left you, our listener,
Speaker 1: with a sense of the incredible potential here, but also
Speaker 1: maybe the scale of the challenge and this constant, fascinating
Speaker 1: evolution happening right now in our digital defenses.