Katie: OK, let's dive in. If you're working in the digital world, trying to keep things secure, it feels like you're in this constant relentless battle, doesn't it?
James: Oh, absolutely. It's like an uphill climb, constantly. The attackers just seem to keep getting faster.
Katie: And it feels like there are these two massive forces making it even harder right now. First, there's this huge persistent shortage of skilled people. finding defenders is tough.
James: It really is. The demand worldwide just swamps the supply. If you're looking for talent, it's, well, it's difficult.
Katie: Right. You just can't hire enough people fast enough. And then the second thing, it's the sheer volume, the absolute flood of security vulnerabilities being found every single day.
James: Uh-huh. Trying to keep up manually, it's just overwhelming.
Katie: So you've got fewer people and they're basically drowning in alerts and potential fires to put out.
James: Exactly. Scratched incredibly thin. That's the reality for so many teams.
Katie: But here's where it really changes gear. And honestly, it gets pretty scary. Attackers aren't just sitting back. They're now using AI, artificial intelligence, to turn those new vulnerabilities into working exploits, like incredibly fast.
James: Yeah, we're talking days, sometimes even hours.
Katie: From a public announcement of a flaw to a tool that can actually attack systems.
James: Damn.
Katie: in hours.
James: That's the potential. Yes. And that speed, that's the critical thing here. It just collapses that window defenders used to have. Time to test a patch, figure out a workaround. That window is shrinking dramatically.
Katie: OK, so that's what we're really digging into today. How can organizations possibly fight back at that kind of machine speed? We have to move past this old model, right? The people versus machines idea on defense.
James: We do. It has to become more like machine versus machine.
Katie: Yeah, a necessary evolution. And it's not just about the usual patching routines. We need to explore these innovative automated ways to handle and protect against these, well, AI-driven threats.
James: It really requires a fundamental shift. In banking, and definitely in the technology we use, you have to match the speed and scale of the attackers.
Katie: Let's talk about that first problem. You mentioned the talent gap. Just how big is the shortage we're facing?
James: The numbers are.
Katie: Yeah.
James: Well, they're pretty staggering. Projections are showing that by the end of 2025, so not far off, We could be looking at close to five million unfilled cybersecurity jobs worldwide.
Katie: Five million.
James: Five million roles. Just sitting empty, meaning critical work isn't getting done.
Katie: Wow. That's hard to even wrap your head around. So what does that mean for the security teams that are actually in place right now?
James: It means they're completely swamped, overwhelmed. Your existing security folks, your IT staff, they're already juggling the day-to-day stuff, right? Keeping things running, managing users, dealing with normal issues. Keeping the lights on. Exactly. And then on top of that, you've got this relentless fire hose of high-priority security alerts, new vulnerabilities, all demanding immediate attention. It just leads to burnout, mistakes happen, and sometimes critical things just fall through the cracks.
Katie: And even if you're lucky enough to hire someone new, someone skilled, they can't just walk in on day one and fix everything, can they?
James: Oh, absolutely not. Every place is different. You've got your specific systems, maybe some old legacy platforms still kicking around, custom apps, different security tools already in place.
Katie: Right, the whole unique ecosystem.
James: Precisely. So a new hire, doesn't matter how skilled they are, they need time, real time, to learn the nuances of your environment, understand your specific risks, get plugged into the workflows. There's always a ramp up period.
Katie: And the threats don't pause while someone's getting up to speed.
James: Not at all. They just keep coming.
Katie: OK, so we've got staff shortages, overwhelmed teams, new people needing time. Now, let's layer on that second problem, this explosion, in the sheer number of threats.
James: Yeah, the volume of new vulnerabilities being disclosed, it's just escalating like crazy. I mean, think back maybe five, six years ago, finding 10,000 new vulnerabilities in a whole year, that felt like a lot.
Katie: Right, 10,000 a year was the big number we worried about.
James: It was. Now, 40,000 new vulnerabilities in a year is becoming kind of the new normal.
Katie: 40,000. Yeah.
James: And look, monthly, we're regularly seeing months with 4,000, sometimes even 5,000 new issues published, every single month.
Katie: thousands of potential new weaknesses popping up constantly, that scale just feels impossible for human teams to track, let alone prioritize and actually fix quickly.
James: It is virtually impossible manually. And this is where that AI connection becomes so, so critical.
Katie: Right, the speed factor.
James: Exactly. The volume is one challenge. But the speed at which those vulnerabilities get weaponized, that's the real game changer. Bad actors are using AI to basically automate building exploit code.
Katie: How does that work?
James: They can take the technical details released about a vulnerability, feed it to an AI model, and the AI can analyze it and generate working attack code. Sometimes in days, sometimes, like we said, even hours after the flaws made public.
Katie: And wasn't there a real-world test that showed this isn't just theory?
James: That's right. A researcher did exactly that. Took the published details of a vulnerability, gave it to an AI, and boom, the AI-generated exploit code. And it worked.
Katie: It actually worked on live systems.
James: It did. It proved that AI can dramatically speed up exploit development. And it lowers the bar, too. You don't necessarily need elite coding skills anymore to weaponize some of these flaws quickly.
Katie: So what happens if we project that forward? What kind of threat environment are we looking at?
James: Well, it paints a pretty concerning picture. You could have a single person, maybe not even a highly skilled traditional hacker, orchestrating attacks on a massive scale. Imagine them controlling fleets of AI-powered hacking bots. These bots could be constantly scanning the internet, looking for systems with these newly announced vulnerabilities, or even just common misconfigurations, and when they find a target. The AI could potentially generate a tailored exploit right then and there and compromise thousands of systems, really rapidly, with very little human oversight needed for each individual attack.
Katie: Wow, like automated attack swarms. That makes the traditional way of doing security feel, well, really outdated, fragile even.
James: It does. If attackers can weaponize something in minutes or hours, waiting weeks, maybe even months to patch, That's just not going to cut it anymore.
Katie: That's the core problem, isn't it? The speed mismatch. Many organizations probably aim for, what, patching critical stuff within maybe two weeks?
James: Yeah. And two weeks used to be considered pretty good, a big improvement over older timelines that might have stretched out for months.
Katie: But two weeks is an eternity if an AI is already exploiting that vulnerability just hours after it came out.
James: Exactly. That time to exploit is shrinking. It's heading towards almost instant. So the time to remediate, the time you take to fix or block that vulnerability, it has to compress just dramatically.
Katie: It has to keep pace.
James: It has to move towards automation, towards machine speed.
Katie: Which brings us back to that idea of machine versus machine defense. It's not just a cool concept anymore. It sounds like a flat-out necessity.
James: It really is. To defend against attacks that operate at machine speed and scale, your defenses need to operate at that same speed and scale. And that means you need real time detection of these issues, vulnerabilities, exposures paired with immediate automated responses.
Katie: So what's the practical goal here? What are we trying to achieve with this automated approach?
James: Well, there are two main goals, really. Ambitious, but essential. First, you want to shrink your mean time to detection, your MTTD. How long it takes you to even find the problem down as close to zero as possible, like instant detection.
Katie: OK, find it immediately. What's the second goal?
James: Just as important. drastically cut down the manual work piling up on your security and IT teams. Some analyses suggest this kind of automated machine speed approach could potentially reduce that manual effort by like 90%. 90%.
Katie: That's huge.
James: It is. Think about what that frees up, those scarce human experts we talked about. They can finally focus on the things that really need human intelligence. You know, deep analysis, threat hunting, strategy, handling complex incidents.
Katie: instead of just chasing down patches and alerts all day.
James: Exactly. Let the machines handle the speed and volume. Let humans focus on the things only humans can do well.
Katie: You mentioned earlier this isn't just about patching. When we talk about true remediation at machine speed, what else does that involve?
James: Yeah, true remediation acknowledges that the real world is messy, right? It's not always just click here to install the patch. Organizations have really complex IT setups.
Katie: Like what?
James: Well, you've got legacy systems maybe, things that can't easily be updated. You've got mission-critical apps that absolutely cannot go offline for patching without causing major business disruption. Or maybe you have internal processes, change management boards that need multiple approvals, which takes time.
Katie: And sometimes a patch might exist, but deploying it is risky itself, or just not practical fast enough.
James: Exactly. Or sometimes there isn't even a patch available yet from the vendor, especially for older systems or certain types of devices.
Katie: So just relying on the official vendor patch isn't always enough, especially when you need to react in minutes or hours?
James: Definitely not. An effective machine speed defense needs a whole toolkit, a range of options. Applying the official patch, once it's available and tested, that's one crucial tool, absolutely. Often the best way to fix the underlying code flaw.
Katie: Okay, patching is option one. What else is in the toolkit?
James: Another really critical option is making automated configuration changes.
Katie: How does that work?
James: It means automatically adjusting settings on the affected system or maybe a network device or even in your security tools to basically block the path the attacker uses. You're not changing the vulnerable code itself.
Katie: Ah, okay. Like what kind of settings?
James: You might automatically disable a specific service that the exploit targets. or close a network port it uses, or maybe enforce stronger authentication rules, things like that.
Katie: Smart. You neutralize the attack vector without touching the potentially fragile application code. What's the third piece?
James: That's often called patchless protection, or sometimes virtual patching. This uses other security controls, like firewalls, intrusion prevention systems, IPS, maybe specific endpoint security software to create a kind of protective shield around the vulnerable system. The control sits there and identifies and blocks the exploit attempt before it can even reach the vulnerable part of the system. So the threat is neutralized, even though the underlying vulnerable code hasn't actually been changed yet.
Katie: So the system might still technically have the flaw, but it's effectively barricaded. No one can get to it.
James: Exactly. And the real power comes when you have all three options, patching, configuration changes, and this patchless protection available and ideally automated.
Katie: Why is having all three so important?
James: It gives organizations flexibility. You can choose the best fix and the fastest fix for any given situation. You can balance the security urgency against operational realities like downtime, business impact, and the tools you already have.
Katie: That makes sense. That ability to pick the right tool for the job really quickly seems crucial for complex environments. It also sounds like this needs to work with the tools organizations already have invested.
James: Oh, absolutely. That's non-negotiable. You can't just tell organizations, hey, throw out everything you own and buy this completely new thing. That's just not practical.
Katie: So it needs to integrate.
James: Yes, a better together approach is key. The solution needs to plug in smoothly with the vulnerability scanners people already use and trust, the patch management systems they might have, the workflow or ticketing systems they rely on.
Katie: How does that integration help specifically?
James: Well, you can keep using your trusted scanners, for instance. The platform can just ingest those scan results, maybe enrich them, help prioritize better. If you have patch management tools that can help automate those processes, make them faster, rather than forcing you onto something totally separate.
Katie: So it enhances what you have.
James: Exactly. Hooks into your existing ticketing systems, your change management workflows. It's about augmenting your current capabilities creating a sort of unified control plane for remediation across different tools, rather than replacing everything.
Katie: It's like adding a layer of intelligent automation on top of your existing security foundation.
James: That's a good way to put it. And taking this unified approach also helps drive a shift in focus, moving beyond just vulnerability management to something broader, often called exposure management.
Katie: Exposure management. OK. How is that different from managing vulnerabilities?
James: Well, vulnerability management usually focuses on CVEs, those common vulnerabilities and exposures, specific flaws in software code, usually needing a patch from the vendor. But attackers don't only go after CVEs. They also exploit misconfigurations. Think about a server accidentally left with default admin passwords, or a sensitive management interface exposed directly to the internet when it shouldn't be, or maybe a service running with way more privileges than it needs.
Katie: Oh, okay. Those aren't strictly bugs in the code, but they're definitely gaping holes an attacker could walk right through.
James: Absolutely. So an exposure is a much broader idea. It's basically anything an attacker can leverage to get in or cause harm.
Katie: So that includes CVEs?
James: Yes. Code vulnerabilities are part of it, but also misconfigurations, weak credentials, insecure network protocols being used, maybe even compliance failures that create risk. It's a whole potential attack surface.
Katie: So exposure management is about finding all the possible ways an attacker might get a foothold, casting a wider net.
James: Exactly. You're still doing vulnerability scanning, but you're also doing configuration audits, checking systems against security-based practices like the CIS benchmarks, maybe setting up custom checks based on your own specific risks. The goal is to get that comprehensive view of all the potential weak spots.
Katie: And once you find these exposures, whether it's a CVE needing a patch or a misconfiguration needing a setting change, that same flexible automated remediation framework we talked about kicks in.
James: Precisely. Whether it is a CVE, a risky open port, a service that needs disabling, the platform identifies the exposure and suggests or automates the most appropriate fix from that toolkit patching config change virtual patch.
Katie: So it's a more proactive, holistic way to look at defense, addressing weaknesses before they get weaponized, especially by these faster AI-driven attacks.
James: That's the idea. Preemptive defense based on a full understanding of your exposure.
Katie: This whole picture, integrating tools, managing all exposures, having these automated, flexible responses, sounds really powerful. What's the next step? What's the ultimate horizon here for this machine versus machine idea?
James: The next logical step and kind of the ultimate goal for many is moving towards autonomous remediation.
Katie: Autonomous, meaning the system fixes things by itself.
James: Essentially, yes, where the AI and automation platform isn't just suggesting fixes or helping humans push buttons, but it's actually making and implementing certain remediation decisions on its own based on rules and risk parameters that you defined beforehand.
Katie: OK, wow, that feels like, well, a pretty big leap of faith for a lot of organizations today. Letting an AI make changes automatically that could potentially break something.
James: It absolutely is a big step. And frankly, very few organizations are fully comfortable right now letting AI just autonomously patch things or change configs across their whole environment. There's definitely a period needed to build confidence, test things out, set up really clear guardrails.
Katie: But you see this as the direction things have to go.
James: I think inevitably yes, because the attackers are heading there. If machines are attacking you at scale and speed, ultimately machines have to be doing the defending at that same scale and speed. The human role changes.
Katie: Changes how?
James: Humans become the supervisors, the policy setters, the strategists, the people who refine and oversee the autonomous systems, rather than being the ones manually turning every screw.
Katie: Can you paint a picture of what that autonomous defense might look like in action?
James: Sure. Imagine the platform detects a new critical exposure. Maybe it's a vulnerability, and threat intel shows an AI generated exploit is already out in the wild. Or maybe it's a critical misconfiguration that just popped up. Instantly, the platform analyzes the affected system, considers the options, You might see, OK, we could apply the vendor patch, but here's the assessed risk of that breaking something based on past data. Or we could make a config change, like disabling a risky protocol. Here's the likely business impact of that. Or we could apply a virtual patch at the network level. Here's its assessed coverage and safety.
Katie: So it weighs the options automatically.
James: Right. And over time, as humans in the organization approve certain actions, maybe adjust suggestions or overwrite others, the AI learns. It learns your organization's specific risk tolerance for different types of systems, different types of exposures.
Katie: Ah, so it gets tailored to your specific needs and risk appetite.
James: Exactly. And eventually, based on very clear business rules you've defined, like maybe for any critical exposure on a system tagged as high business impact, automatically apply the safest virtual patch immediately. The AI can then autonomously choose and implement that best option instantly upon detection.
Katie: That really shrinks that time-to-remediation window we talked about almost to zero. What about those really tricky systems, the ones you mentioned that just can't be patched easily or at all?
James: Yeah, that's where this AI-driven orchestration becomes incredibly valuable. Think about older IoT devices or maybe legacy hardware, industrial control systems where the vendor just doesn't provide updates anymore. They're effectively unpatchable.
Katie: Right. Those are persistent risks.
James: But autonomous defense can pivot. The AI could see the unpatchable vulnerability and automatically trigger other controls. Maybe it initiates network segmentation changes to isolate that device. Maybe it updates rules on an IPS further upstream, or applies specific host-based controls on the device itself if possible.
Katie: So it finds other ways to mitigate the risk, even if the root flaw remains.
James: Precisely. It finds ways to neutralize the threat vector or contain the risk, ensuring those unpatchable systems don't just sit there as permanent open doors for attackers, especially attackers now amplified by AI.
Katie: Wow. Okay. This has been quite the deep dive. We've really covered the escalating challenge, that collision of the talent shortage with this flood of AI weaponized threats. And it really lays out why we need this evolution to a machine versus machine defense.
James: Yeah, the old way, the manual human speed defense. It just can't keep pace anymore. Automation and AI, they aren't just nice to haves now. They're becoming foundational requirements for effective security.
Katie: And the key pieces seem to be that automated real-time detection, having those multiple flexible remediation options, patching, config changes, patchless protection.
James: Making sure it integrates smoothly with the tools you already have, leveraging those investments.
Katie: And broadening the focus beyond just CVEs to that comprehensive exposure management, covering misconfigurations, everything.
James: and ultimately having that clear path towards autonomous remediation where machines can intelligently orchestrate the defense against other machines.
Katie: And the payoff for shifting to this model seems huge. You drastically reduce that crushing manual workload on your teams. You can actually stay ahead of these modern, fast threats. And maybe most importantly, you free up those valuable, scarce human experts.
James: Exactly. Let them focus on the strategic, complex, creative work that really requires human ingenuity.
Katie: It's empowering the humans by letting the machines handle the sheer speed and scale, the grunt work of defense.
James: That's it, exactly.
Katie: So thinking about this accelerating pace of AI, both for attack and defense, it really brings us to a fundamental question for you listening. How prepared are you? How prepared is your organization to really make this shift, to start letting machines fight machines? What's your first step going to be towards building a more autonomous, more preemptive defense?
