新的顶级捕食者：CrowdStrike CEO George Kurtz 谈用 AI 智能体武装的攻击者

简要描述Brief Description

全新的顶级掠食者：CrowdStrike 首席执行官乔治·库尔茨谈配备 AI 智能体的对手The New Apex Predator: CrowdStrike CEO George Kurtz on Adversaries Armed With AI Agents

目录Table of Contents

• 智能体领域格局
  Agentic Landscape
• 预告片
  Trailer
• 乔治·库尔茨简介
  George Kurtz Introduction
• 人工智能与对手
  AI and Adversaries
• 开放权重模型与对抗性攻击
  Open Weight Models and Adversarial Attacks
• 对手地理分布
  Adversary Geography
• 上下文中的攻击者能力
  Attacker Capability in Context
• Hugging Face 要点
  Hugging Face Takeaways
• CrowdStrike 前沿安全模型
  CrowdStrike Frontier Security Models
• CrowdStrike 主要产品与服务
  CrowdStrike Key Offerings
• CrowdStrike 的模型有何不同
  What Differentiates CrowdStrike’s Models
• 近期安全领域态势
  Near Term State of Security World
• 模型蒸馏现状
  State of Model Distillation
• 开放权重模型的重要性与成本
  Open Weight Model Importance and Cost
• 评估现实中的智能体威胁
  Assessing Real Agent Threat
• AI 领域值得关注的方面
  Areas to Watch in AI
• 支持防守主导地位的理由
  Case for Defense Dominance
• 高管建议
  Executive Advice
• 变化创造机遇
  Shifts Create Opportunity
• 结论
  Conclusion

智能体格局Agentic Landscape

一个民族国家几年前才会拥有的能力，如今一个行动者基本上就能拥有。这极大地改变了整体威胁环境。人们必须牢记的是，现在已经不是恶意软件的问题了，而是提示词。他们基本上可以设计提示词，生成完全独特的特征，表明他们将朝哪个方向行动、如何收集信息、会转向哪里。大家都来找 CrowdStrike 等公司，说：“那么，怎样才能确保 AI 安全，让我们能更快地使用 AI？”如今，对抗 AI 的唯一方法就是用更好的 AI。防御者了解自己的系统。防御者拥有……nation state would have just a few years ago, essentially a activist would have today. That has dramatically changed the overall threat environment. What people have to keep in mind is it's not malware anymore. It's prompts. They can basically engineer prompts that would create a totally unique signature of what direction they're going to go in, how they gather information, where they pivot. Everyone's coming to Crowd Strike and others going, "Well, how do you make AI secure so we can go faster in AI?" The only way to fight AI today is with better AI. The defender knows their own systems. The defender has the

预告片Trailer

架构图。防御方掌握代码。你如何把所有这些整合起来，大幅提高智胜攻击型 AI 的概率？architectural diagram. The defender has the code. How do you put all of that into something that you can dramatically increase your probability of outsmarting the offensive AI?

George Kurtz 简介George Kurtz Introduction

听众朋友们，欢迎回到《No Priors》。今天，阿拉和我邀请到了乔治·库尔茨。发生安全事件时，他是全世界第一个会被找来的人，是传奇的网络安全防御专家，也是 Found 的创始人兼首席执行官，后来又创立了 CrowdStrike。我们将聊聊网络安全领域正在发生什么、如何用 AI 对抗 AI、他们的内部研究项目 Safe Mind，以及互联网在下一个时代前景如何。欢迎回来，乔治。非常感谢你来参加节目。Hi listeners, welcome back to No Priors. Today Allah and I are here with George Curts. George is the world's first call when there's an incident, a legendary cyber defender, the founder and CEO of Found and then CrowdStrike. We talk about what's happening in cyber security, fighting AI with AI, their internal research effort, Safe Mind, and whether the odds are good for the internet in the next era. Welcome back, George. Thanks so much for doing this.

主持人：很高兴来到这里。Host: It's a pleasure to be here.

嘉宾：所以，呃，科技正处于一个巨变时期，智能体正在接管企业。呃，CrowdStrike 正处于这一趋势的前沿。嗯，你能……Guest: So, uh, it is a sea change period in technology and agents are taking over the enterprise. Uh, CrowdStrike is on the forefront of that. Um, what can you

人工智能与对手AI and Adversaries

跟我们说说你现在在真实环境中看到了什么吧？我想你之前提到过，外面的智能体数量多出两到三个数量级，它们正在人们的……环境中采取行动，而且它们也意识到了这一点。tell us about what you're you're seeing in in the wild right now? I think you have said that uh there are you know two three magnitudes more agents out there taking action in in people's um estates and they're aware of.

主持人：我们在企业领域看到的情况令人惊叹。如果你追踪前沿实验室的发展轨迹，只看它们自身的营收，就能看出如今又涌现出多少智能体，不管是 Claude Code 还是 Codeex。嗯，它们正在整个环境中不断涌现。显然，市面上还有许多其他类型的智能体，但我们再次把它们当作一个参照。你知道，我们看到的是，它们无处不在。而这些企业面临的部分问题是，它们根本不知道这些智能体都出现在哪里。影子 AI 已经成为许多公司的一个大问题。嗯，我知道我们接下来会讨论这些智能体在做什么，以及发生的各种不良情况，但总体来说，让组织了解 AI 在哪里被使用、谁在做什么，更重要的是，他们实际是以什么身份在运行，这一直是行业面临的一大挑战，也是我们在 CrowdStrike 的重点工作之一——努力找到解决方案，帮助客户破解这个难题。Host: It's been incredible in the enterprise of what we've seen. If you follow the trajectory of the the Frontier Labs and you just look at their own revenue, you can see just how many more agents are out there, whether it's Claude Code or Codeex. Uh they're popping up all over the uh all over the environment. Obviously, there's many other type of agents that are out there, but again, just using that as a proxy and um you know, what we've seen is that they're they're popping up everywhere. And part of the problem in in these enterprises is they have no idea where they're popping up. Shadow AI has become a huge issue for many companies. Um, I know we're going to talk about what the agents are doing and all the bad sort of things that are happening, but just in general, having organizations understand where their AI is taking place and who's doing what and more importantly what identity are they actually operating under uh has been a big challenge in the industry and a big focus for us at Crowd Strike and and trying to crack the code for our customers.

嘉宾：你会如何描述你看到 AI 正在为对手做些什么？你之前把它描述为压平对手金字塔。Guest: How would you describe what uh you're seeing AI doing for adversaries? You've described it as flattening the the adversary pyramid before.

主持人：当我想到这些对手时，我把它称为“痛苦金字塔”。想象一下金字塔的底部，底座就是我们所说的黑客行动主义者。他们的技术水平最低，但对手数量最多。然后是金字塔中间这一层的网络犯罪分子，你知道，他们技术更复杂一些，人数也比黑客行动主义者少一些。金字塔顶端则是国家级行为体，对吧？也就是说，相对而言，他们技术最复杂，但数量最少。不过，这种格局已经被彻底颠覆了，因为我们现在看到的新“顶级掠食者”其实是智能体国家，不是民族国家，而是智能体国家。如果我们想想目前存在的那些模型，嗯，它们……Host: When I think about the the adversaries, I I call it the pyramid of pain. So, if you just imagine the bottom part of the pyramid, the base of the pyramid is really what we call activists. So, you've got the least level of sophistication but the greatest number of adversaries. Then in the middle band of the pyramid, you've got e-crime, you know, more sophisticated um and a few less than all the activists. And then at the top of the pyramid, you've got nation state actors, right? So the most sophisticated but the least number relatively speaking. Well, that has been totally just blown up because really now what we're seeing is the new apex predator is the agent state, not the nation state, it's the agent state. And what the agent state has done, if we think about the the models that are out there um that are

开放权重模型与对抗性攻击Open Weight Models and Adversarial Attacks

不受约束，因此没有真正的防护措施。它本质上是把所有这些非常复杂的主题、技术和成本都纳入其中，然后把整座金字塔夷平。也就是说，几年前一个民族国家才具备的能力，如今一个活动人士基本上也能拥有。这彻底改变了整体威胁环境。那么，我们看到了什么？我们看到发现的漏洞越来越多，攻击速度也越来越快。而攻击一旦开始，就会接连不断。你看得出来。其实现在很容易判断一次攻击是不是由 AI 发起的，因为你能看到它们迭代得有多快。不过，到某个时候，他们会让攻击变得缓慢而隐蔽，那时就更难判断哪些是 AI 发起的，哪些不是。bladed, so there there's no real guard rails. It's essentially taken all of these very complex topics and sort of techniques and cost and now flatten the entire pyramid. So the same level of sort of capability that a nation state would have just a few years ago, essentially a activist would have today. And that has dramatically changed the overall threat environment. So what are we seeing? We're seeing more vulnerabilities found and we're seeing faster attacks. And when the attacks come, it's just, you know, it's just one after another. You can see it. It's actually right now it's pretty clear whether it's an AI attack. Um because you can see how fast they're actually iterating. But at some point they're going to introduce that being low and slow and it's going to be more difficult to figure out what's AI and what isn't.

但这正是我们看到的情况。我认为这是组织正在面临的真正挑战之一：攻击接连不断，复杂程度也大幅提升。归根结底，如果你给一个智能体足够的时间去执行任务，而它要么必须闯进去，要么就会失败，它就会想方设法找到进入的办法，而且很多时候还真能得手。But that's what we're seeing and I think that's one of the real challenges that organizations are facing is just the barrage of attacks and the level of sophistication has dramatically increased and ultimately if you know if you give an agent a task with enough time and you know the agent need either needs to get in or it dies. It's going to try to figure out a way to get in and a lot of times it's going to have success.

嘉宾：你有没有看到这些对抗性攻击的实施方式正在发生变化？我知道我们之前聊过一点开放源代码模型和闭源模型，或者开放权重模型。你有没有发现，大多数攻击都在使用开放权重模型？还是说，人们使用的模型和能力参差不齐，有些来自最先进的实验室？比如说，人们现在实际用什么来发动这些攻击？Guest: Are you seeing a shift in terms of how these adversarial attacks are being made? I know we were talking earlier um we talked a little about open versus closed source models or openw weight models. Are you seeing a shift where most of it is openweight models? Is it a mixture in terms of people using state-of-the-art labs and capabilities? Like what what are people actually using to do these attacks at this point?

主持人：我们在开放权重模型上看到了很多这种情况，对吧？如果你看看，我是说，任何前沿模型最新一代的安全护栏。嗯，坦白说，你几乎连一份涉及网络安全的 PowerPoint 都做不出来。它真的会拒绝你，还会把你降级到更低级的模型。所以我们看到的是……Host: We're seeing a lot of it on the openweight models, right? If you look at I mean just the guardrails on on the latest generation of any of the frontier models. Um to be candid, you can you can barely create a PowerPoint that has cyber security in it. I mean it literally rejects you and bumps you down to a lower model. So what we've seen is

对手地理分布Adversary Geography

你知道，再说一遍，如果你看看 GLM，看看 Quinn，看看市面上各种各样的模型，它们在处理网络安全问题方面都非常有效。许多模型要么经过了能力剥离，要么就是专门为网络安全打造的，可能用于红队测试。这些就是你可以直接运行的那类攻击——我是说，你可以在……我家里有两台 Spark DGX，你知道，我把所有模型都跑起来了，想做什么都行。想创建勒索软件？没问题。想创建一套勒索软件工具包？没问题。想部署、想反复迭代？这些全都能做到。你知道，这离直接在自己的笔记本电脑上运行这一切只有一步之遥。再想想 Mac 如今的情况。you know again if if you look at um GLM if you look at you know Quinn if you look at any of the sort of models that are out there uh they've been very effective in being able to um deal with cyber security and many of the the models are either abladed or specifically built just for cyber might be for red teaming and and those are the type of attacks where you can actually just run these I mean you can run them on. I've got two Spark DGXs at home and you know I've got all the models running and you know whatever I want to do I can do. You want to create ransomware no problem. You want to create a ransomware kit, no issue. Uh you want to deploy, you want to iterate, I mean it's all there for you and you know that is like one step removed from just running it all on your your laptop and we think about where Mac is today.

即将推出的新款 Mac，呃，还有 Mac Studio，都会是非常出色的 AI 机器。因此，技术前沿将是那些可以不受护栏限制而运行的东西。前沿模型在某些方面也挺有用，比如你需要 PowerShell 脚本或类似东西的时候；不过，现在很多事情当然都可以用开放权重模型来做。Even the new Macs uh that are coming out right the studios they're going to be incredible AI and machines. So the tip of the spear is going to be what you can run without the guardrails and the frontier models are sort of helpful in in terms of if you needed PowerShell scripts or those sort of things, but certainly you can do a lot of it with the openweight models right now.

嘉宾：有没有常见的地理分布？比如说，攻击是不是大多来自某一个地区？嗯，攻击类型有没有什么共同点？主要是勒索软件吗？还是别的什么？是钓鱼活动吗？我有点好奇，随着你们看到 AI 工具的普及，攻击类型的构成有没有发生变化？我们不妨换个角度来看这个问题，因为我们是从攻击者的视角来看的。你知道，对方是民族国家行为体吗？是网络犯罪，还是民族国家行为体？我们给这些攻击者起了各种名字，比如“熊”和“熊猫”，分别指来自俄罗斯的，或者来自伊朗的；还有“小猫”之类的。所以，呃，我们见过很多攻击。呃，我们看到朝鲜在这类攻击中非常活跃。呃，他们得，怎么说呢，想办法把钱弄进他们的经济体系里。Guest: And is there a common ge geography like are mostly attacks coming from a single region? Um are the is there a common attack type? Is it mainly ransomware? Is it something else? Is it fishing campaigns? I'm a little bit curious like has the mix shifted as you've kind of seen the adoption of AI tools? We sort of look at it maybe just reframe the question a little bit because we look at it from an adversary perspective. You know, is it a nation state? Is it ecrime nation state? We sort of, you know, we've come up with these names of bears and pandas depending if they're Russia or, you know, it could be from Iran, kittens and other things. So, uh, but we've seen a lot of attacks. Uh, we see North Korea being very busy, uh, with these sort of attacks. Uh, they they've got to, shall we say, get money into their economy.

所以他们一直忙着，呃，你知道，窃取信息，呃，其中很多都跟勒索软件和收钱有关，不过，嗯，是的，我认为总体来说……So they've been very busy on uh you know stealing information uh a lot of it around ransomware and getting paid but um yeah I think overall

攻击者在此情境下的能力Attacker Capability in Context

我们看到的情况是，呃，我们显然看到来自中国的大量活动都在使用 AI；我们也在网络犯罪领域看到了这种情况。嗯，当有人进入一个系统后，我们会追踪“突破时间”，也就是他们进入你的系统后，实际需要多久才能突破并转移到另一个系统。通常，他们最先进入的系统并不是最有价值的目标。当然也有可能是，但通常不是。真正的核心资产可能在别的地方。过去这往往需要几天，或者几个小时，而现在只需几分钟。平均是 29 分钟，但我们见过最快的情况只用了 27 秒。基本上，现在我们关注的是推理速度。所以他们一进入机器，你就能看到他们以极快的速度不断尝试。大家需要记住的是，这已经不再是恶意软件了。what we've seen is a lot of uh we've seen obviously out of China a lot of activity using AI and we've also seen it in the e-crime world where um when someone gets onto a system we track breakout time that is if they got onto your system how long would it take them to actually break out and pivot and move to a different system generally the system they first land on is not the most interesting. It could be but it's not the most interesting. The crown jewels might be somewhere else. So on a that used to be days and you know hours and now it's minutes. On average it's 29 minutes but we've seen it as quick as 27 seconds. And essentially you know now what we're looking at is the speed of inference. So as soon as they get on the machine you can just see iterating very quickly. And what people have to keep in mind is it's not malware anymore.

这是提示词。所以他们基本上可以设计出一些提示词，形成独一无二的特征，体现他们将朝哪个方向行动、如何收集信息、会在哪里转向。这就让我们更难弄清楚这一切。不过，这就是我们目前看到的情况，而且无论是民族国家行为者还是网络犯罪行为者，情况都一样。It's prompts. So they can basically engineer prompts that would uniquely create create a totally unique signature of what direction they're going to go in, how they gather information, where they pivot. Um, which makes it, you know, more challenging to figure all that out. But that that's what we're seeing right now. And it it's across the board from the nation state and e-crime actors for sure.

主持人：嗯，您自 CrowdStrike 创立以来一直是安全领域的领军人物。您会如何看待当前这个阶段——攻击者的能力不断增强，防御者的能力或许也在提升？我们现在是否处于一个对手更占优势的世界？您总体上是如何看待这个问题的？Host: You have been um a leader in security since you know Found Crowd the founding of Crowd Strike. Um would you say that there like how do you put the current period of um increase in capability like attacker capability and perhaps defender capability in context right are we in a more adversary dominant world right now like how do you think about the problem overall

嘉宾：嗯，我觉得现在是进入科技行业最令人兴奋的时期之一。我在这个行业已经干了三十多年了，你知道，我常说的一句话是，安全的发展轨迹与技术曲线的斜率相呼应。我刚开始做安全时，用的是一台思科路由器，可能勉强算得上有个网站，对吧？那时候其实还没有什么防火墙，只有一些工具包，比如 TIS 工具包，还有其他一些东西。我这么说大概暴露年龄了，不过你知道，那时候可没多少东西。现在想想我们今天所处环境的复杂程度——云，以及人工智能相关的各种其他技术——我就不一一列举了，但今天的情况比那时候复杂得多。所以我们需要所有这些小公司，也包括大公司，来保护这些技术。Guest: well I think it's one of the most exciting times to be in technology I mean really I've been doing this 30 plus years and you know what I always like to say is that security parallels the slope of the technology curve when When I first got into security, it was a Cisco router and like maybe a website barely, right? There really weren't firewalls. There were these toolkits, TIS toolkit and these other things. Probably dating myself, but um you know, there wasn't much there. And now if you think about the complexity of where we are today with with cloud, with um all of the other uh technologies around AI, I'm not going to go through all of them, but it's a lot more complex today than it was way back then. So you need all these little companies and including big companies that are able to secure all these technologies.

所以，当我们思考当下所处的阶段时，它确实是最令人兴奋的阶段，但也创造了最大的机会，因为对手通常会寻找漏洞或薄弱环节。他们会找到一些新技术，而这些技术可能没有完善的技术防护；他们也会利用 npm 包之类的东西，对吧？你很清楚，在过去那种环境下，美国企业里没人会随便下载东西，然后就在自己的电脑上运行。所有东西都必须经过审查和扫描，诸如此类。So when we think about where we are today, it actually is the most exciting but it also creates the biggest opportunity because the adversaries generally find the gaps or the seams. So they'll find new technologies that maybe that they don't have good tech protection uh that they're taking advantage of things like npm packages. Right? In the old world, as you as you very well know, it was no one in corporate America would just download anything and run it on their computer. It had to be vetted and scanned and all these sort of things.

Hugging Face 要点Hugging Face Takeaways

现在我们放出一个智能体。我们赋予它一个本质上就是我们自己的身份，甚至更糟。然后放出它，并告诉它想做什么就做什么。顺便说一句，想下载什么软件包就下载什么。显然，这些正是会造成巨大风险敞口的事情。所以，我们正在逐渐摸索着应对……And now we unleash an agent. We give it an identity which is essentially our own or worse. And then we unleash it and we say do whatever you want to do. And by the way, download whatever package you want to download. And obviously these are the kind of things that cause a tremendous amount of exposure. So, we're we're sort of getting to grips

主持人：而且，生产代码在编写过程中根本没人看。Host: and nobody's reading any of the production code as it gets.

嘉宾：不，不是这样。我的意思是，你知道，你在现实中看到这些事情，有时会一头雾水。你不应该感到惊讶。我的意思是，这就像是：给它一个任务，过几天再回来，希望一切都能顺利完成。没人盯着它。没人明白它在做什么。呃，这很成问题。所以，你知道，我们可以谈谈这些智能体行为的可解释性，这会非常重要。嗯，但现在它就像一个黑箱，没人知道里面发生了什么。大家只是希望，你知道，能发生点什么，然后从另一端冒出个结果来。不过，现在是进入科技行业的绝佳时机。AI 将从根本上改变世界，而前沿实验室正在做着令人惊叹的事情。但归根结底，还得靠安全技术来推动这类技术的发展。Guest: No, they're not. I mean, you know, you you see some of these things in the wild and you're scratching your head. You shouldn't be surprised. I mean, it was like, let's give it a task and we'll we'll come back in a few days and we'll hope it all worked. Nobody's watching it. Nobody understands what it's doing. Uh, and it's problematic. So, you know, we can we can talk about sort of the uh explanability of what these agents are doing, which is going to be really important. Um, but it it right now it's like a black box and nobody knows what's going on. They hope, you know, something happens and pops out on the on the other side of it. But it's a great time to be in technology. AI is fundamentally going to change the world and the Frontier Labs are doing amazing things. But ultimately, it's going to come down to security enabling these sort of technologies.

而且这是第一次，安全性成了油门，而不是刹车。这正是我们正在看到的。And for the first time, security is the gas pedal, not the brake. And that's what we're seeing.

主持人：你是在什么时候意识到这种转折正在发生的？至少在安全领域是这样。我觉得对很多人来说，Hugging Face 和 OpenAI 的那件事算是一个重要时刻，让大家意识到，能力和技术正在发生巨大的变化，可能会出现什么情况——正如你提到的，这些事情是否都在受到监控，等等。那对你来说是一个开创性的时刻吗？或者说，还有哪些开创性的时刻？Host: What is the moment that you realized this inflection was happening, at least in the security world? And I think for a lot of people kind of the hugging face open AAI issue was kind of like a big you know moment where people kind of realized there's a big sea shift in terms of capabilities and technology and what could happen and to your point are things being monitored etc. Was was that a seminal moment for you or what are some of the seminal moments?

主持人：我先问你一个问题。那么，你从 Hugging Face 得到的最大收获是什么？Host: Let me ask you a question first. So what was your big takeaway from hugging face

嘉宾：任何运行前沿模型的人都需要，嗯，呃，注意其环境质量，并做好持续的生产监控。Guest: that anybody running frontier models needs to um uh be careful of their environment quality and have very good ongoing production monitoring.

主持人：是的。这就是结论。不过我觉得很多人会说，这是第一次端到端的智能体攻击。这些都没错。你看看这些东西在做什么，也知道人们会谈到它们如何隐藏自己、如何试图活到下一代之类的。这些都没错。我最大的感想是，防守方不具备与进攻方同等的能力。好吧，你看这件事可能会说，安全气囊被拆掉了，因为这是一次实验，智能体的防护栏被撤掉了，安全气囊也被拆掉了，最终它们找到了逃出沙盒的方法。但事实是，Hugging Face，你知道，他们首先做的就是去找前沿模型，然后问：“什么？”Host: Yeah. That is the takeaway. But I think what a lot of people would say is well this was the the first agentic endto-end attack. All true and you kind of look at you know what these things were doing and you know people uh talk about them you know hiding themselves and you know trying to basically survive to the next generation and those sort of things. All true. My biggest takeaway was that the defenders didn't have the same capabilities as the offense. Okay. Now, you would look at this and say the airbags were off because this was an experiment where the agents, you know, the guardrails were down, the airbags were off, and ultimately they found a way out of the sandbox. But the reality was hugging face, you know, when the first thing they did was they went to the Frontier models and they said, "What?

“这东西是真的吗？到底怎么回事？”然后他们被拒绝了，对吧？因为那边的安全气囊是开启的。所以，他们不得不怎么做？他们实际上……Like, is this thing real? What's going on?" And they got refused, right? Because airbags were on over there. So, what did they have to do? They actually

CrowdStrike 前沿安全模型CrowdStrike Frontier Security Models

不得不转向开放权重模型，来梳理所有这些数据并弄清楚情况。所以，对我来说，一个重要的方面，基本上就是确保防御方拥有与攻击方所使用的同等水平的前沿防御型 AI。这是我得到的一大启示，也是 CrowdStrike 正在努力推进的工作之一。 那么，结合你刚才提到的情况来看，这件事是不是很难？前沿实验室的模型设有非常严格的护栏，在某种程度上，你可以说，并非每家企业都能获得同等的防御能力。换句话说，他们不得不转向开放权重模型，这似乎说明目前用实验室的模型实际上还做不到这一点。那么接下来的问题就是，该如何采用这种方法？是的。had to go to openweight models to kind of sift through all this data and figure it out. So, a big piece for me is to is to basically make sure that defenders have the same level of a frontier defensive AI as as what the offense is is using. And that was a big takeaway uh for me and and part of the things that that CrowdStrike is working. And is that hard in the context that you mentioned of um the guardrails being so strong on the state-of-the-art labs models that to some extent you could argue there aren't that those same defensive capabilities available to every enterprise. In other words, the fact that they had to go to openweight models kind of suggests that you can't actually do it currently with the labs. And so the question then how do you adopt that? Yeah.

嘉宾：是的，是的。我的意思是，你试着用一个前沿模型做一份关于网络安全的 PowerPoint，它却拒绝帮你。所以你知道，这也是我们创建 Safe Mind，以及我们的红队和蓝队模型的原因之一，因为我们想为客户提供前沿级别的防御能力，让他们能够开展工作，而不必遇到所有这些拒绝响应。我认为，这在未来会成为许多公司的一个重要因素。回到进攻与防御这个话题，我觉得每次技术转型时，只要做事方式发生重大变化，进攻能力似乎总会稍微领先一些，原因有几个。我的意思是，其中一个原因就是，进攻可以非常有针对性：我需要去做 X。Guest: Yeah. Yeah. I mean like you you try to make a PowerPoint in in with a frontier model around cyber security and it refuses you. So, you know, this is part of why we created Safe Mind and and our and our red and blue models just for that reason because we wanted to give uh frontier caliber defenses uh to our customers that allow them to operate without sort of all these sort of refusals. Um and and I think that's going to be an important element going forward for for many companies. I think um I guess back to the offense versus defense side, it seems like with every technology shift, offensive capabilities are always a little bit ahead when you have a big change in how things are done for a few reasons. I mean, one of them is just with offense, it can be very directive. I need to go do X.

而在防御方面，你需要覆盖的面很广。所以在某种程度上，你需要覆盖更多内容。其次，这还涉及技术的采用和扩散，对吧？攻击者可能是一个，也可能是一群。你知道自己要攻击什么，而你要防御的则是分散在各处的企业生态系统。Crow Strike 如何确保这些防御能力能够广泛提供？While with defense, it's a broad surface area you need to cover. So, to some extent, you need to cover more stuff. And then secondly, it's just technology adoption and diffusion, right? You are one attack. You are a set of attackers. you know what you're going after versus the diffuse ecosystem of enterprises that you need to defend against. How does Crow Strike think about ensuring that those defensive capabilities are very broadly available?

主持人：嗯，这是我们重点关注的事情之一。呃，在这个说法流行起来之前，我们就创建了自己的网络超级智能实验室，我想可以这么说。嗯，所以我想我们算是把这件事做对了。基本上，我们所做的就是创建了自己的前沿型实验室，完全专注于安全模型，包括红队和蓝队模型，以及测试框架。我们把这个系统称为 SafeMine；不过我们还有 Red Tempest 和 Blue Solano，这是目前的两个模型。我们在这件事上与英伟达进行了广泛合作，呃，我们基本上会利用几种模型类型，但 Neotron 是我们在蓝队方面主要使用的模型之一。我们和他们坐下来谈过，我也和 Jensen 坐下来谈过。过去六个月我们一直在为此合作，也一起梳理了这件事该如何运作，以及……Host: Well, this is one of the things that we focused on. Uh we created our own cyber super intelligence labs before the term was was popular, I might say. Um so I guess we we kind of nailed that one. But essentially what we've done is we've created our own basically frontier type type lab that is totally focused on uh security models both red and blue and the harness and we call it safe mine as a system but we've got red tempest and blue solano those are the two models now we worked extensively with Nvidia on this and uh we basically leverage we leverage a few model types but Neotron is one of the main models that we leverage on on the blue side and when We sat down with them and I sat down with Jensen. We were working on it for the last 6 months and we kind of went through how this would work and

CrowdStrike 核心产品与服务CrowdStrike Key Offerings

基本上就像你说的那样，防御方似乎总是慢一步。我们所做的很大一部分，就是不断迭代红队和蓝队的工作。对于客户，我们会了解他们的环境是什么样的。然后，Jensen 当时坚持说，你必须有一个数字孪生体。你看看其他行业，他们都有数字孪生体。他说，安全领域也必须有一个。所以，我们构建了一个数字孪生体，可以利用我们掌握的关于客户环境的所有信息，在虚拟世界中基本上重现他们的环境，然后迭代这些模型，并让模型循环运行：红队不断尝试入侵，直到无计可施，因为蓝队 Solano 已经弄清楚红队在做什么。我认为，这确实是我们跟上威胁步伐的方式，因为红队一直在不断迭代，而我们必须建立起这样的循环。essentially to your point like you know the defense always seems to be one step behind. So the big part of of what we're doing is we're iterating the red and the blue. So for our customers we have a view of what their environment looks like and then and this was Jensen sort of insisting like you have to have a digital twin. If you look at other industries, they have digital twins and he goes, you have to have one security. So, we built a digital twin where we can take all of the information that we know about a customer's environment. We can basically recreate that in a virtual world and then we can iterate the models and loop the models where red continually is focused on getting in until it runs out of turns because blue Solano has actually figured out what red was doing. And that's really the way I think we have to keep up with uh the threats because red is constantly iterating and you're going to have to have this loop in place.

嘉宾：嗯，这说得通。我的意思是，即便只是从传统网络安全的角度来看，了解自己的环境也是防御者为数不多的优势之一。CrowdStrike 也开创了共享情报、了解对手的能力，对吧？嗯，实际上我觉得，不妨先退一步，介绍一下 CrowdStrike 目前在研发方面的主要产品和投入，因为我觉得它是网络安全领域的一家巨头。不过，我认为把它的核心看作端点保护和了解对手，可能已经是过时的观点了。Guest: Uh that makes sense. I mean even just from a classic cyber security perspective like knowing your environment is one of the um only advantages that you have as a defender as well as CrowdStrike has pioneered like you know the ability to share intelligence and understanding of the adversary right um actually I I think it'd be useful to just like zoom out and describe like uh you know the the uh key offerings and investments on the R&D side that Crowdstrike is making today because I think like it's a it's massive cyber player. Um, but I think the the root of like endpoint protection and understanding the adversary might be an outdated view.

主持人：是的，我觉得没错。你知道，过去四五年左右，我们确实拓展到了功能更全面的平台。我们现在有 33 个模块。说到底，我创办公司时只有一个模块，现在已经有 33 个了。我们的能力建设不仅涵盖终端，还涉及云、IT、身份管理以及许多其他领域。回顾我们在一些领域投入的大量资源——比如刚刚提到的我们最近举办的 Falcon 用户大会上讨论的内容——其中就包括我们所说的 AI。我们率先推出了 EDR（终端检测与响应），早期这项工作很大程度上依靠计算机和人工完成。之后，我们显然又在此基础上增加了许多功能，也加入了预防等各类能力。Host: Yeah, I think that's right. You know, we we've really, you know, probably over the last, you know, four or five years, I guess, really moved into, you know, much broader platform capabilities. We've got 33 modules today. And, you know, ultimately when I started the company, we had one module and now we've got 33. We build capabilities not only across endpoint but around cloud uh IT identity um and many different areas that are out there. So if you look at where we've made a lot of investments in some of the things that we just talked about at our latest user conference Falcon um it was around what we call aid. So we pioneered EDR endpoint detection response which a lot of it in the early days was a computer and a human. Um and then you know we've added obviously many more things around that and prevention and all those sort of things.

但当你看看智能体在做什么时，它本质上就像一个超人。它有自己的身份，能访问算力，在你的计算机上运行——或者也可以在容器中运行。它能访问数据，也具备联网能力。对吧？所以，我们想做的第一件事，就是找出所有这类失控的 AI 和影子 AI，因为没人知道它们在哪里。就像你刚才说的，你得先了解自己的环境是什么样的。第二，我们可以从智能体诞生之初就开始监测它的行为。比如说，你在 Claude Code 里启动一个智能体，它有子智能体，会调用工具，会连接到 MCP 服务器、拉取数据，不管它在做什么，我们都能把整个事件链完整地呈现给你。这一点至关重要；然后，我们还可以围绕这些行为添加策略和拦截规则。But when you look at what an agent is doing it's it's essentially a superhuman. It has an identity. It has access to compute. It runs on your computer. Uh or it can run in a container. Um it has access to data. It has networking capabilities. Right. So what we wanted to be able to do is to first find all of this rogue and shadow AI because nobody knew where it was. Like back to your point, you got to know what your environment looks like. And then two, we can instrument from the birth of an agent what that agent does. So if you're in, you know, claw cloud code, you start an agent, has sub agents, it has tool calls, it, you know, goes to an MCP server, it pulls data, whatever it's doing, we can actually tell you the entire chain of events and that's incredibly important and then be able to add policy and blocking rules around those.

嗯，所以我们在这方面花了很多时间。我认为从商业角度来看，如果你看看目前的统计数据，今天的平均值——我其实觉得这个数偏低，但他们说……Um, so we've spent a lot of time in that area and I think from a business perspective if you look at the stats right now on average today, which I actually think is low, but they say

CrowdStrike 的模型有何不同What Differentiates CrowdStrike’s Models

你可以拥有90个不同的智能体，作为员工。嗯，这对我们来说可能是一个比单纯做 EDR 大得多的机会。所以这是我们一直重点关注的一个领域。嗯，正如我提到的，第二个领域是建立这个超级智能实验室，并创建我们自己的前沿模型。同样，我们的模型会从现有模型起步，比如 Neotron 级别的模型，你知道，主要是 Neotron 3 这一类。我们正在帮助他们微调一些我们需要用于即将推出的 Neotron 4 的内容。嗯，然后我们会对模型进行后训练，并利用过去15年来我们积累的所有宝贵数据，争取取得最好的效果。我再补充一点，然后就先说到这里。这个框架带来的差别非常大。几乎无论我们采用什么模型，不管是前沿模型，还是现成的开放权重模型，借助我们为它打造的框架，都能以低得多的成本取得好得多的结果。you can have 90 different agents, you know, as as an employee. Um, well, that can be a much bigger opportunity than just EDR for us. So that's one area that we've been focused on. The um the second area then as I mentioned is creating this super intelligence lab and creating our own frontier models. Again our models we start with existing models like uh a Neotron class you know mostly the three class of Neotron. We're helping them sort of fine-tune some of the things we need for the four that's coming out. Um but then you know we kind of you know post train them and and using all the valuable data that we have over the last 15 years to get the best outcome. And I and I'll say one more thing and I'll pause. The the harness makes a huge difference. Like almost any model we take whether it's Frontier whether it's just an offtheshelf uh openweight model we can get much better results at a way lower cost because of the the harness that we we created that goes with it.

这些就是一些重大投入。嗯，你知道，在身份认证方面还有更多内容。我很乐意介绍其中任何一项，不过我先说到这里，看看你对这些有没有其他评论或想法。So those are some of the big investments. Um, you know, there's more around identity. I'm happy to cover any of those, but let me pause there and see if you had any any other comments or thoughts on those.

嘉宾：这本来就是我对你们实验室在这方面投入的一个大问题。我觉得，问任何模型训练项目一个合理的问题就是：为什么这不该只是前沿实验室来做？你们和 OpenAI、Anthropic 这样的机构合作，提升能力。嗯，这些实验室当然也意识到安全的重要性，于是推出了面向经过审核的防御者的项目，比如 Daybreak，或者某种形式的 Mythos 访问权限。为什么这还不够？我的意思是，你们有一个关于能力的论点，基于……利用你们的能力，以及你们对内部数据的了解。除此之外，你觉得独立安全供应商在这里还有什么结构性优势吗？Guest: That was going to be one of my um big questions about your lab's investment here, which is I I think a reasonable question to ask about any model training effort is like why isn't that just a frontier? Um, you know, you work with open eye and anthropic uh like capability. Um uh these these labs of course uh they um realize the importance of security and then they have these programs for like vetted defenders be it like Daybreak or sort of mythos access. Why is that insufficient? I mean you have you have a capability argument based on you know harness your ca your understanding internally data. Is there is there anything else structurally that you think is important for an independent security vendor here?

主持人：是的，我觉得这里有几件事。首先，实验室正在创造的东西令人惊叹。我的意思是，看看它们一路走来、创造出的成果，简直难以置信，对吧？我们也希望让客户能够使用这些成果。所以，总体来说，如果你有 Safe Mind，并且想利用前沿模型，那很好，我们可以帮你接入。如果你想使用我们的模型，也完全没问题。我们的模型会便宜得多。举个例子，如果你要进行一般性的修复，使用最高级别的前沿网络安全模型，可能要花大约 10 美元；而我们的模型只要 3 美分。所以成本差异非常大，这一点很重要。但当你了解我们的模型时，会发现，由于我们使用自己的数据对模型进行了后训练，一个很大的优势是我们拥有这些数据——根本没有什么类似 Reddit 的平台能提供 CrowdStrike 的安全数据。Host: Yeah I think there's a few things there. So first what what the labs are creating is incredible. I mean where they've come from where what they created it's unbelievable right and we also want to make that available to our customers so the general theory is if you have safe mind and you want to leverage a frontier model great we can we can leverage that u if you want to leverage our models fantastic our models will be significantly cheaper so just to give you an example um so for general remediation if you use like uh the highest caliber frontier cyber model it probably cost you about $10. Ours is like 3 cents. So there is a huge cost difference um which is important. But then when you look at you know our model because of the way we've postrained it on our data a huge difference is we have the data like there is no Reddit of crowd strike security data.

你可以创建合成数据，但那真的和我们拥有的数据不一样。我们有15年的数据——嗯，也许略少一点，因为我是15年前开始做这件事的——但你知道，可以说我们有十多年的安全数据，其中包括来自我们托管检测与响应（MDR）服务的数据，涵盖各种威胁与漏洞配对，以及攻击链之类的信息。我们基本上积累了海量数据，用来训练它。所以我们在这方面取得了很好的效果。嗯，我认为它非常专注，这要归功于后训练。你知道，我再说一次，它不会治愈癌症，但能在安全领域给你带来很好的效果。这是第一点。第二点是安全与信任方面。我们拥有客户提供的所有数据。过去15年来，客户一直把这些数据托付给我们。You can create synthetic data but it's it's really not the same as what we have. We got 15 years uh well maybe a little bit less because I started 15 years ago but you know call it over a decade of security data including from our MDR our managed detection response service that has all of the sort of threat and and vulnerability pairings and sort of the attack chains and we've basically created this massive amount of data that we use to train it. So we we're getting great results on it and um I think it is very focused because of the post training you know again it's not going to cure cancer but it's going to give you great results for security. That's number one. Number two is there is a safety and trust aspect. We have all of the data from our customers. We've been entrusted with that for the last 15 years.

我们知道如何保护它。我们知道他们想要什么，也知道他们想如何使用它。客户确实对此有所担忧。他们不希望所有这些数据都被发送到各个实验室，也不希望自己的所有敏感信息被送出去。他们不希望自己的信息被用于训练，对吧？因此，我们可以提供一种方式，让他们获得所需的信任与安全保障，以满足自身的安全计划需求。而且，AI 主权这一概念对我们的许多客户都非常重要。所以，我们并不是说要排除各个实验室，因为它们拥有令人难以置信的技术；但你可以选择……We know how to protect it. We know what they want. We know how they want to use it. they there's a real concern by customers. They don't want all this data going out to the labs. They don't want all their sensitive information. They don't want it their information being trained on, right? So, we can provide a way to get them the trust and security that they want um for for their own security program. And this this sort of concept of AI sovereignty is very important to many of our customers. So again, we're not saying exclude the labs because they have incredible technology, but you can pick

安全领域的近期现状Near Term State of Security World

你想要什么，或者说你可以在各种模型之间进行路由，但归根结底，我们希望让客户拥有选择权。what you want or you can route between, you know, the various models, but ultimately we want to give customers choice.

嘉宾：我想你也稍微谈到了政教分离。你知道，你想确保在某种程度上有一个中立的第三方，也可以，呃，你知道，审视实验室提供的一些模型，或者其他一些开源模型。而且你知道，Cross Track 似乎也完全可以扮演这个角色。所以，嗯，我认为安全领域最重要的因素就是获得信任，成为值得信赖的合作伙伴。你知道，看看市场上的供应商，显然有几家大型厂商，也有很多安全公司，但信任是点滴积累起来的，却会瞬间荡然无存。你知道，客户不想要那种可能会让他们的数据面临风险的鲁莽行为。嗯，你知道，我再说一遍，大家都在尽力而为，但我们的全部重点就在这里，而且我认为，专注对任何企业来说都至关重要。Guest: I think you've also talked a little bit about separation of church and state. You know, the you want to make sure that to some extent there's a neutral third party that can also uh you know, interrogate some of the models that the labs are providing or some of the other open source out there and you know it seems like cross track can definitely play that role as well. So, um, I think the number one factor in security is being trusted, being a trusted partner. You know, you can look at the vendors out there. There's obviously a few big guys that are that are out there. There's many security companies, but uh trust is is uh built in drips and lost in buckets. And you know, c customers, they they don't want sort of cowboy activities that may put their their data at risk. And um you you know again I everybody's do trying to do the best they can but I our whole focus right and I think focus is incredibly important in any business.

我们的全部重点都放在安全上，致力于保障客户的安全；我们并不是想要解决 AI 和其他领域里的世界性难题。所以我认为，这当然是我们的优势。Our whole focus is security and our whole focus is keeping our customers safe and we're not we're not trying to solve the world's problems in in AI and other areas. So I think that is is certainly an advantage for us.

主持人：如果展望两年后，你觉得安全领域会是什么状况？你觉得从现在开始会发生哪些变化？两年之内，肯定会有各种本地模型在个人电脑上运行。嗯，你知道，可能还会出现本地模型与某种“最后一公里”路由相结合的模式，接入前沿模型。开放权重模型也会多得多。嗯，你知道，看看过去六个月的代币变化，就能看出这种转变。Host: If you were to think out two years from now, what do you think is the state of the security world? Like what do you think has shifted from this point forward? Certainly within two years you're going to have local models that are all running on people's computers. Um you know you may have a mixture of local models plus sort of last mile routing into into Frontiers in terms of what they look like. You're going to have a lot more openweight models. Um you know you can just see the shift in the tokens you know over the last 6 months.

主持人：你看到的变化是什么？规模大概有多大？你觉得这种情况还会持续多久？Host: What what is the shift that you're seeing and how what's the order of magnitude there? How long do you think that continues for?

嘉宾：我几乎每天都和 CIOS 交谈。今晚我和一位来自纽约一家大型银行的 CIOS 共进晚餐，基本上他的评论是，你知道，我们已经……Guest: I talked to CIOS you know almost every day. I had dinner with one tonight from big bank in New York and and basically the comment is you know we got

模型蒸馏现状State of Model Distillation

我们全面投入了 AI，很好，但成本完全失控了。你知道，大家一个劲儿地消耗 token，直到有人收到账单巨额的账单才停下来。然后我就说：“那你们怎么看这件事？打算怎么办？”他们说：“现在的开放权重模型已经非常出色了，就算落后三个月，也完全够我们用了。”其中一位客户评论说——他们知道我很喜欢一级方程式——“银行需要的任何一级方程式相关工作都会交给前沿模型。如果能带来 alpha、能赚钱，而且特别重要，我们愿意花这笔钱，没什么大不了的。但其他所有事情，成本实在太高了，所以我们会自己创建所有开放权重模型，再对它们做更多后训练之类的。这就是他们打算采用的做法。”into AI we went all in great the costs were out of control you know token maxing you know stopped when people got big bills and you know I said well what how do you think about this like what are you going to do and they said the models are so the openweight models right now are so good even if they're three months behind it's plenty for what we need and one of the customers comments and and they know I'm into Formula 1. They said any Formula 1 activity that we need for the bank will go to the frontier. Like if it's alpha and we're making money and it's like, you know, super important, we'll pay the money. Not no big deal. But for all the other things, like it's really expensive and we're going to create all of our own openweight models, you know, and do more post training on them and those sort of things that that's the way they're going to operate.

而且我对此坚信不疑。我看到一个又一个客户都在朝这个方向发展。我认为，你知道，当前环境中的一些挑战在于，大家似乎都看到了这一点，而且，你知道，在前沿领域，人们确实有些担忧，不知道接下来会发生什么。And I firmly believe that. I'm seeing that in customer after customer that that's the way they're going to go and I think you know part of some of the challenges in the current environment is I think people are are all seeing that and you know there there is some concern you know at the frontier level of of what's going to take place

主持人：所以我想，关于开放权重模型，常有人提到的一种说法是，它们至少有一部分是从那些实验室的模型中蒸馏出来的。换句话说，中国的开源厂商实际上是在针对 Anthropic、OpenAI 或其他一些前沿模型来训练自己的模型。嗯，现在很多实验室都在努力推出反蒸馏措施，也就是试图让模型具备防止自身被蒸馏的能力。你觉得这会不会只是这样一种来回较量，能力不断提升，就像网络安全领域的攻防一样？或者说，你预测两年后，或是在一个合理的时间范围内，蒸馏会发展到什么程度？Host: and so I guess on the openweight model one of the uh statements that tends to be made is that they are at least in part distilled from the labs in other words you have Chinese open source players who are effectively training their models against that enthropic or open AI model or you know other sort of frontier models. Uh and many of those labs are now trying to roll out anti-distillation efforts. So they're trying to build into the model the capability to not get distilled. Do you think it's just sort of a back and forth where the capabilities keep going almost like offense and defense and cyber or what's your prediction in terms of the state of distillation in two years or you know within some reasonable time frame?

嘉宾：我是说，我想我的第一个看法是，前沿模型在获取所有那些信息时，究竟蒸馏了什么？我是说，我们都知道那些关于数据来源以及事情如何发展的诉讼，对吧？所以，我们就从这里开始。Guest: I mean I guess my first comment is what were the frontier models distilling when they got all that information? I mean, we know all of the, you know, sort of the lawsuits around where the data came from and and how that unfolded, right? So, we kind of start from that.

主持人：所以，这就像一种上等烈酒。它经过三次蒸馏，你知道。所以，Host: So, it's like a fine liquor. It's triple distilled, you know. So,

嘉宾：它是经过三次蒸馏的。没错。就是，蒸馏是从哪里哪里开始的？Guest: it's triple distilled. Exactly. Like, so where where does the distillation start

开放权重模型的重要性与成本Open Weight Model Importance and Cost

来自哪里，你知道吧？嗯，我觉得现状就是这样，不过各家实验室确实都在努力防止模型蒸馏。显然，你看那些中国模型，会发现有大量的蒸馏行为，还有成群的系统在互联网上到处运行，进行蒸馏。我觉得挑战在于，精灵已经出瓶了。我的意思是，蒸馏已经在大量进行。那些模型非常出色，但我们也得实事求是地给予肯定。如果你没有那么多芯片，就得想办法灵活应对，同时又想拥有顶尖模型，那么我觉得美国以外确实正在发生一些创新，我们得对此保持关注，对吧？嗯，所以，是挺难的。from, you know? Um, so I think it is what it is, but the labs certainly are focused on on trying to prevent distillation and and obviously, you know, when you look at the Chinese models that are out there, there's a lot of distillation takes there's factories of systems that are, you know, bouncing all over the internet distilling this. I I think the challenge is that the genies out of the bottle. I mean, there there's a lot of distillation already taking place. The models are very good, but we also have to give credit where it's due. if if you don't have all the chips and you have to be resourceful and you still want to have top models, you know, I think there's some innovative things that are taking place outside of the US and we got to be mindful of that, right? Um, so yes, it's hard.

我我觉得，要让蒸馏这件事重新回到瓶子里恐怕很难。他们会尽力而为，不过，嗯，你知道，很多模型都已经……你知道，我我觉得，现在的问题是，两年后会是什么样？我不知道。你们现在已经有很棒的模型了。它们可以用上一段时间，而且里面已经封装了很多数据。所以，我们拭目以待吧。这个问题上，我我真希望自己有水晶球。I I think it's going to be hard to put the genie back in the bottle on distillation. They're going to do the best they can, but um, you know, a lot of the models are are, you know, I I think now you what does it look like in two years? I don't know. you you've got great models today. They they can last for a while and you have a lot of data that's already packed into them. So, we'll we'll see how it unfolds. I I wish I had the crystal ball on that one.

主持人：哦，我只是有点好奇。还有一个问题，我想这些事情总是很难预测，不过我还是有点好奇。你觉得中国公司在保持开源和开放权重领域至少处于前沿方面，是否具有结构性优势？还是说，美国的开放权重模型很可能会在近期迎头赶上？Host: Oh, I was just curious. And then I guess another it's always hard to predict these things, but I'm a little bit curious. Do you think there's structural advantages for Chinese companies to stay at the frontier of at least open source and open weights or do you think uh US openweight models are likely to catch up in the near term?

嘉宾：嗯，现在有一些开放权重模型正在推出。我想你也知道，你可能也投资了这些模型。嗯，我认为，拥有优秀的美国开放权重模型会非常重要。所以，你知道，我们与英伟达合作的原因之一当然是 Neimotron 是开放权重且开源的，不只是开放权重；但拥有其他替代方案也会非常重要。而且我觉得，嗯，再说一次，在科技行业待得够久，就会多少看清事物的发展脉络：互联网的操作系统就是 Linux，毫无疑问，好吗？这是有原因的。人工智能也会有自己的操作系统。在我看来，等到那时，随着一切逐步展开，它很可能会是一个开源、开放权重的系统。总之，我们拭目以待。Guest: Well, there are some openweight models coming out. I'm I'm sure you you know you may be invested in them as well. Um I I think having great US openweight models is going to be very important. So, you know, one of the reasons why we worked with with Nvidia was, of course, Neimotron is openweight and open source, not just openweight, but having other alternatives is going to be very important. And I think there's, you know, again, just being in technology long enough, you kind of look at how things unfold like the the operating system of the internet is Linux, like period. Okay? And there's a reason for that. And there's going to be an operating system of AI. And it's pro in my mind it's going to be, you know, an open- source open weight when that happens and how it all unfolds. I mean, we'll see.

而且这并不意味着那里And it doesn't mean there

评估真实智能体威胁Assessing Real Agent Threat

已经没有空间容纳更多前沿模型了。这并不意味着人们不会靠它赚大钱。你知道，围绕它构建的应用技术栈，还有各种工具框架、系统如何连接，以及如何看待企业应用，所有这些都会非常重要。但归根结底，现在涉及的成本实在太高了。而且你看看很多这些前沿模型，它们正在商品化。如果中国的模型落后三个月，你知道，很多公司会想：三个月又算什么，我能从中得到什么？它已经能满足我的所有需求了。这就是难题。最近，一家排名前五的金融机构的人对我说——你知道，那是当地一个大型技术采购方——他们的组织根本不可能有能力每十天就吸收一个新模型的发布。所以，落后三个月反而比他们组织所能接受的合规节奏还快。isn't room for any frontier model. It doesn't mean that people are not going to make a bunch of money off it. You know, the application stack sort of around it. Um, you know, the harnesses and how things are connected and how you think about enterprise, all those sort of things are going to be really important, but ultimately like there's just too much cost involved right now. And and when you look at a lot of the these frontier models, I mean, they're being commoditized. If the Chinese models are three months behind, you know, a lot of companies are going like three months is not what what am I going to get out of it? I does everything I want. And that's the challenge. a a top five financial said to me um uh recently like a you know a large technology buyer there um that uh there was no chance that their organization had the capability to absorb a new model release every 10 days anyway and so 3 months behind you know was uh uh faster than the acceptable like regulated pace of their organization.

所以在某种程度上，这对他们来说没什么区别。嗯，我觉得开放仍将是这道大谜题中重要的一部分。呃，你知道，有个问题我想我们会一直问你，那就是让你展望未来——这多少有点不可能——但考虑到你在安全领域的经历，这是我整个科技职业生涯中第一次看到一些非常聪明、受过良好教育的技术专家说：“我们得把互联网关掉。我们只能把所有东西都做成物理隔离，因为互联网明年就要崩溃了，让关键系统接入互联网会不安全。”嗯，你觉得未来出现这种情况的可能性有多大？也就是社会有很多关键系统，无法依赖互联网？我的意思是，我觉得这简直是胡说八道。你知道，安全问题、智能体能做什么，以及它们带来的风险，这些问题确实很严重吗？So it made no difference to them to some degree. Um I I think open is here to stay as a big part of the puzzle. Uh you know one one question I I know we're going to keep asking you to look into the future and it's kind of impossible but given your history in in security. This is the first time in my career in technology where some very uh smart well-educated technologists are like we need to turn the internet off. we just got to air gap everything because the internet is gonna fall apart in the next year and will be unsafe for critical systems to touch it. Um how how much do you believe that to be like a reasonable probability in the future where there are lots of critical systems that society cannot rely on touching the internet? I mean, I think it's nonsense, you know, uh like is security and and what agents can do and and the risk that they pose, is that serious?

当然，这可是件严肃的事。我们都应该看看这件事，然后说：“是啊，我们得确保把安全措施做到位，你懂的。但世界会因为这些智能体而很快毁灭吗？”不会。而且你看看这些智能体在做什么，我觉得人们对智能体的作用有种神话般的想象。好像智能体能直接从你的电脑“传送”到另一台电脑似的。其实它只是做着人类会做的同样的事，只不过做得更好、更快，对吧？所以想想 CrowdStrike 这样的公司长期以来做的事：我们一直在防范这类攻击。现在，攻击可能会来得更快，也可能会把更多较低层级的漏洞串联起来，诸如此类。嗯，这对攻击者来说当然是好事，对防御者来说就不是什么好事了，但是……Of course, that's serious business. We should all look at that and go, "Yeah, we got to make sure that we lock things down, you know, but is the world going to end because of these agents anytime soon?" No. And when you look at what the agents are doing, I I think there's this sort of mythical view of what an agent does. It's like an agent doesn't just beam itself from your computer to another computer. It actually just does the same thing a human would do just better and faster, right? So if you think about what companies like Crowd Strike have done for a long time, it we prevent against these sort of attacks. Now they might come faster. There might be, you know, more lower level sort of vulnerabilities chained together and those sort of things. great um you know great for the adversary not great for the defender but

人工智能领域值得关注的方向Areas to Watch in AI

它正在构建能够应对所有这类攻击的技术。那么，AI 做了什么？它带来了更多漏洞，所以这类漏洞变多了，而且你知道，漏洞利用出现得也更快了，所以就是更多、更快。但这并不是说突然出现了一种全新的技术类别，某个智能体能一下子把自己传送到什么地方。我觉得人们只是被这种想法绕晕了，好像出现了一种全新的技术类别似的。其实并没有，它只是基于我们已知的东西训练出来的，还没有发展出自己的东西。这就是为什么我会觉得，安全将再次成为 AI 的助力。我认为这对安全行业至关重要，因为通常情况下，你是在踩刹车。你知道，我们得花时间，你不能让业务按自己期望的速度推进。it's building the technology that um can deal with all these sort of attacks so what has AI done it's created more vulnerability so you have more of them and you know you have more exploitation coming faster so it's more and faster but it there isn't like a unique class that all of a sudden an agent just beams itself somewhere and I think people just get wrapped around the axle of like there's some new class of technique that's taken there. There isn't it's just been trained on what we know. It hasn't developed its own yet. And that's why I kind of look at it and go security again is going to be an enabler for AI, which I think is incredibly important for the security industry because normally it's, you know, you're putting the brakes on. You know, we have to take time. You're not letting the business run as fast as they want.

现在，大家都来找 CrowdStrike 和其他公司，说：“那么，怎样才能让 AI 更安全，这样我们就能更快地推进 AI？”所以我会这么看待这件事。AI 会一直存在。我认为确实有一些担忧，而且这种担忧也应该存在，但围绕这些事情也有很多……嗯，某种末日论调，我觉得这些担忧未必有必要。我知道你有时也会以天使投资人的身份参与一些公司，也涉足生态系统的其他领域。除了安全领域之外，目前 AI 世界里还有哪些变化或重点领域让你特别兴奋？你觉得最近发生或即将发生的哪些事情特别令人振奋？And now everyone's coming to to CrowdStrike and and others going, "Well, how do you make AI secure so we can go faster in AI?" So that that's the way I would look at it. Like AI is here to stay. I think some of the the concern is there and it should be there, but sort of like there's a lot of um uh kind of dumerism around some of this stuff and I I just don't think it's it's necessarily warranted. I know you're also um you know sometimes involved with companies as an angel investor and you know other parts of the ecosystem. Are there other areas that you're very excited about right now in terms of key areas of change or focus in the AI world in addition to the security world? Like what are some of the things that you think are really exciting that either just happened or are coming?

主持人：我想你知道，芯片以前多少有点不受追捧。显然，我们知道它们如今很受追捧，而且未来对芯片的需求只会更多，对速度的要求也会更高，芯片也会更加专业化。我确实认为，芯片领域会出现一些重大突破。你知道，单说速度吧，拿摩尔定律来说，它可能有点过时了，但说到底，芯片不断问世，东西也就变得越来越快、越来越便宜。我不知道未来的发展轨迹会怎样，但我可以保证，未来的东西会变得更快、更便宜，这一点我看得出来。随着这些专用芯片不断开发出来，专注于某些特定领域；随着围绕数学发展的新科学不断涌现，性能可能会出现阶跃式提升，比如功耗降低之类的。Host: I think you know chips used to be kind of not in fashion. Obviously we we know they are in fashion today and you're only going to need more of them and faster of them and and more specialized. And I I do think there will be some big breakthroughs in the chip world. You know, when we think about just sort of the speed, just take, you know, Moore's law might be a bit da but dated, but at the end of the day, you know, they they the chips came out and things just got faster and cheaper. I don't know the trajectory, but I can guarantee you it's going to be faster and cheaper in the future like that. I I can see. So as these specialized chips are developed that you know focus in maybe particular areas as new science is developed around the mathematics um you know there can be step function increases in performance you know uh less power those sort of things.

所以我觉得芯片是一个很重要的方面。那些开放权重模型，嗯，我觉得……So I think chips is a big one. The the openweight models um I think

防守主导地位的论证Case for Defense Dominance

当然，发展空间还很大，尤其是美国的模型。我确实认为，在如何改进这些模型并缩小它们的体积方面，会有很大进展，这样你就能在手机上运行它们。我的意思是，我们可能都知道这段历史，记得当年得用一整个足球场那么大的计算机房，才能发射第一枚进入太空的火箭吧。如今，我们 iPhone 的算力比当时整个机房的算力还强。所以这一天终将到来。我认为，能让 AI 运行得更快、更便宜、更高效，并把它带到桌面电脑和手机上，让你在边缘端运行它，这些都是令人兴奋的机会。因为我认为，未来很可能会采用混合模型，但现在你已经可以在本地完成很多事情。再看看 TPU、NPU 等等就知道了。there's a lot of room to grow certainly the US models and I do think there's going to be a lot of advancements in taking these models and shrinking them down you know and being able to run them on your phone as an example. So I I mean we probably all know the history remember the days where you know it took a football fieldsized room of computers to to you know launch the space you know first rocket like we have more power in our iPhone today than than they had in the entire room. So that's going to come and I think the area where you can make AI faster, cheaper, more efficient and and you bring it to the desktop and you bring it to the phone where a you're doing it at the edge. I think all of those are sort of exciting opportunities because that's really where I see you know you you're probably going to have a hybrid model but you can do a lot of it now locally and again just looking at the um you know the TPUs, NPUs, etc.

比如这些，它们都在我们现有的系统上，而且很多时候都被浪费了，但我认为这种情况不会持续太久。like these are they're they're all on systems that we have today and and a lot of times they're going to waste but I I don't think they're going to be going to waste that that much longer

嘉宾：毕竟，你知道，整天积极思考防御方能做些什么是你的工作。我不会说，呃，AI 防御者——抱歉，安全防御人员——在这个意义上是末日论者，但他们总体上有点悲观，对攻防猫鼠游戏能否朝着有利于防御方的方向变得更好，基本不抱太大希望。你能不能尽量论证一下：如果大家投入得当，做出更好的产品，并且实现大量自动化，那么未来十年里，防御方有没有可能真正占据主导地位？我觉得有可能，因为我们只要再次运用这些概念——你知道，它们未必是新的或独特的——关键就在于，始终能够迅速了解攻击方在做什么，并确保防御跟得上。Guest: just because it is you know your job to think ambitiously about what the defenders can do all day uh I I wouldn't say like AI def uh sorry defenders security defenders are doomers in that specific term but they're like generally kind of dow and like pretty pessimistic about the um cat-andmouse game getting any better in their favor. Can you can you make your best case for like actually you know if people invest correctly and we make better products and we automate many like is there a version where is actually defense dominant over the next decade? I think it can be because if we again just with with the concepts that uh you know are they're not necessarily new or unique but that the the ability to always rapidly understand what the offense is doing and making sure that your defense is keeps up with it is is going to be the key.

我的意思是，这某种程度上就是我多年前参与合著的《黑客攻防揭秘》一书在当今的版本。那本书的理念是：如果你向人们展示攻击是如何运作的，防御就会强得多。那时的节奏比今天慢得多，但我认为这仍然很重要。作为一个行业，确保这一点对我们来说至关重要。我也反复向我们 CrowdStrike 团队强调：你必须为防御方提供与攻击方一样好、甚至更强的能力。只要你稍微占优，从数学角度来看，你仍然会有优势。我认为这始终会是一场猫鼠游戏，但要以高明的方式应对，这一点至关重要。如今，对抗 AI 的唯一办法就是用更好的 AI。I mean it's sort of the the modernday version of the the hacking exposed book that that I co-authored many years ago which was like if you show people how the offense works the defense is going to be a lot better. Now, that was a much slower sort of tempo than it is today, but I think that's going to be important. And as an industry, making sure, this is a key thing for us, and and I really drill this home with with our our team at CrowdStrike, is you've got to give as good or better capabilities to the defender than what the offense has. And if you're just slightly better, you know, just mathematically, you're still going to have an advantage. And I think again, it's always going to be a cat-and- mouse game, but being really smart about it is going to be important. The only way to fight AI today is with better AI.

而且你知道，你得善用自己拥有的优势。防御方了解自己的系统，掌握架构图，也拥有代码，对吧？那么，怎样才能把这些都利用起来，大幅提高你智胜进攻型 AI 的概率呢？And you know, you've got to use the advantages you have. The defender knows their own systems. The defender has the architectural diagrams. The defender has the code, right? So, how do you put all of that into something that you can dramatically increase your probability of outsmarting the offensive AI?

主持人：我觉得唯一一个战术层面上挺有意思的想法是，嗯，如果未来大部分代码都是由智能体编写的，随着时间推移，我们或许可以强制采用更好的安全架构。嗯，你知道，也许人们会因为担心漏洞而这么做。很多人——你知道，形式化验证一直都是非常小众的领域；但即便不做到那一步，也可以想想：为什么所有软件都不安全？一个很重要的原因是，工程师人数有限，能力也有限，而且大家都在努力快速推进。但如果你能按照算力增长的速度，或者按照你愿意投入的资源来扩大规模，而智能体又有无限的耐心，那么也许随着时间推移，我们真的能做出更安全的软件。这是个挺有意思的希望。Host: The only tactical thing I I think is an interesting idea is the idea that um you could over time if if it is going to be mostly agent written code, you could enforce better security architecture. Um and you know perhaps fear of vulnerabilities or a lot of people where you know formal verification has always been enormously niche right but even even short of that like okay why is all the software insecure because you know one one big reason is that engineers are um you know there's a limited number of them with limited capacity and we're all trying to move very quickly but if you can scale at the speed of compute or your willingness to spend and agents have infinite patience like maybe we could actually end up with more secure software over time. It's an interesting hope.

嘉宾：我我觉得，行业现在到了这样一个阶段，代码可能已经足够好了，你肯定能从中获得一些好处。但话说回来，代码也在其他领域的数据上训练过，比如训练所用的内容可能存在一些问题。所以，你知道，我有我的……Guest: I I think there there is like the industry is at a point now where where the code is is probably good enough where you're going to get definitely some benefits but again the code was was also trained in other area like it was trained on stuff that you know may have had issues. So you know I have my

高管建议Executive Advice

自己的开发者许可证。我自己开发各种应用，比如 Claude 之类的东西。而且你知道，我有自己的安全代理来审查代码。事情就变成了：Claude 刚生成了代码，接着它又通过自己的安全应用指出代码不安全——可这代码明明是你刚写的啊，你一开始为什么要这么做？当然，也许这样能做一些优化，但我觉得，人们未必意识到的是，即使做到零漏洞——我希望整个行业能达到这个目标——你仍然会遭遇安全漏洞事件。因为我们如今看到的很多情况并不是有人在试图闯入，而是他们在登录。身份认证方面存在很大的问题。不过归根结底，如果我们能写出更好的代码、减少漏洞，并让这种改进层层扩展开来，那就太好了。own developer license. I I build my own sort of apps and things like Claude and and you know I have my own security agent that reviews the code and it's like well Claude just created the code but then Claude is now with its security app going well that code is insecure well you just created it like why did you just do that in the first part you know maybe there's some optimization but I think um what people don't necessarily realize uh is that even with with zero vulnerabilities which I hope the industry can get there you're still going to have breaches Because a lot of what we see people today doing is they're not necessarily breaking in, they're logging in. There's such an issue around identity. But at the end of the day, if we can get better code and we can have less vulnerabilities and we can ripple that through, that'll be great.

难点在于，这件事涉及的范围实在太广。以微软为例，就因为他们想维持兼容性——也就是向后兼容——你知道，有些东西永远都不会得到修复或现代化改造。这只是我们生态系统中一个重要的组成部分，但想想外面还有多少其他软件。你不可能一夜之间把所有问题都解决掉。所以，这会是一个非常漫长的过程。The challenge is it's such a long tail. You take Microsoft as an example just because of of all of the the um you know compatibility that they want to maintain going backwards backward compatibility. You know, there's things that never kind of get fixed or or modernized. And you know, that's just one important element in our ecosystem, but think about all the other software that's out there. You're not going to just fix it all overnight. So, it's going to be a really long tail.

主持人：你在 CrowdStrike 已经工作了大约 15 年，在那之前你是一位创业者。嗯，安全领域……我一直很喜欢安全，因为作为一个商界人士，我们面对的是一项既有趣又重要的使命；而且从商业角度来说，这个问题不会消失，对吧？嗯，不过我觉得，在这段时期，许多大型成熟上市公司的 CEO 都有些无所适从，因为他们凭借既有地位创造的许多价值正受到挑战。你会如何思考怎样让自己跟上时代、让组织保持敏捷并持续取胜？对此有什么通用的建议吗？ 嘉宾：当然，很乐意聊聊这个。首先，我认为你得意识到，这是不同的运营模式和节奏。你知道，我们有成千上万名员工，对吧？Host: You've been running CrowdStrike for uh 15 years and before that you were an entrepreneur. Um uh security, I I've always loved security because like we're never as a business person, it's both an interesting and important mission and as a business person like this problem is not going away, right? Um but uh I I feel many comp many CEOs of large established public companies uh are feeling a little bit unmed in this period of time because so much of the value that they have built uh in their incumbency is is being challenged. Like how do you how do you you know think about keeping yourself up to date, keeping the organization agile, continuing to win? Any any general advice on that? Yeah, h happy to jump into that. So, first I think you have to recognize it's a different operating model and cadence, you know, we've got thousands and thousands of people, right?

再想想那些刚刚起步的 AI 原生公司，它们只有一小群人，你知道，Claude 全天候运行。他们让智能体处理各种各样的事情。这就是新世界，很棒。但你不能抱着那种瀑布式开发思维——倒不是说我们是这样——事情拖到一年后才推出，那你就要倒闭了。所以我觉得，你首先得意识到，组建庞大的团队、制定漫长的开发计划，这样行不通。你需要的是规模更小的团队，比如小队，或者专注于某个问题的小组，他们可以借助 AI，但你不能让成千上万人都来解决一个问题。你得有一个规模更小的团队来做到这一点。And when you think about some of the AI native companies that just started, they have a handful of people, you know, Claude is running all day. They've got agents doing all kinds of stuff. like that's the that's the new world which is great but you you can't be in a uh not that we are but you can't be in like the waterfall mentality of development like things pop out a year later like you're going to be out of business. So I think you first have to realize that having these massive teams and these long schedules like that is not going to cut it. So having smaller teams that are, you know, squads or, you know, smaller groups that are focused on a problem, they can leverage AI, but you can't have like thousands of people show up to solve a problem. You got to have a smaller team to be able to do that.

我们已经这么做了。比如，我们在 Falcon Guardian 产品上就是这么做的。正如你所说，我本人也参与其中，每周开三天会，也参与了它的设计。我会直接说：“我们要做这些。把这个改一下，把那个也改一下。”你得回归创业初期的状态，因为在更大的公司里，变革需要达到这种程度。还有很多公司比我们大得多，你知道，你不能行动迟缓、拖拖拉拉。所以第一点，就是要意识到运营模式和节奏正在改变，你必须做出改变，在内部推动自我革新。然后你得看看，嗯，你们的护城河是什么？如果你是一个记录系统，那护城河就相当深。我们是一个安全领域的记录系统，对吧？And we've done that. We've done that with like our Falcon Guardian product. And to your point, like I was personally involved, you know, had three day a week meetings. I was involved in the design of it. It's like here's what we're doing. Like change this, change that. And you got to get back to your like startup roots because it takes that level of change you know in in a bigger company and there are many companies that are much bigger than we are and you know you just can't be slow and plotting. So that's number one is to realize that the operating model and cadence is changing and you got to change what you got to disrupt yourself internally and then you just have to look at like what well what are your emotes like if you're a system of record it's a pretty big moat. We're we're a security system of record, right?

而且我们多年来一直拥有所有人的数据。顺便说一句，我们是数据的净生产者。我们的传感器，我们称之为安全代理，对吧？但我的意思是，别把它们和 AI 代理混淆，它们是终端代理软件，对吧？它们是数据的净生产者。所以我们每天大约会产生 7 万亿条事件。如果你一直在不断生成这股新的数据流，那就会让你占据优势，因为任何 AI 系统的命脉是什么？就是你输入其中的数据。所以我……And we have everyone's data for many years. And by the way, we're net data creators. Our our sensors, we call them security agents, right? But I mean like not to be confused with AI agents, but the software that that endpoint agents, right? That is a net data creator. So we're creating 7 trillion or so events like a day. And if you're constantly creating this new data stream, that is going to give you an advantage because what's the lifeblood of any AI system? is the data that you feed into it. So I

变化创造机遇Shifts Create Opportunity

我想，对我们来说，一直以来的问题都是：我们的护城河是什么？我们该如何看待它？我们如何确保自己颠覆了那些老牌厂商——赛门铁克、McAfee，以及其他那些玩家？他们大多都已经消失了，或者转型了、被收购了，诸如此类。我们永远不希望自己陷入被别人颠覆的境地。所以我们总在想：怎样才能颠覆自己？怎样才能换种方式做事？怎样才能换种方式思考？但归根结底，正如我一直对团队说的，如果我们把客户照顾好，其他事情自然会水到渠成。客户想要的是受到保护，信任他们的供应商和合作伙伴。说到底，他们还希望成本尽可能低。think you know for us it's always like well what are our modes? How do we look at it? How do we make sure that you know we we disrupted the incumbents we disrupted the semantics the McAfei you know all those other players are like all gone for the most part or changed or bought or whatever and you never want to be in a position where somebody you know disrupts you. So we're always like how do we disrupt ourselves? How do we do things differently? How do we think differently? But at the end of the day, you know, if my my main focus, and I tell this to our team all the time, if we take care of the customer, the rest takes care of itself. What the customer wants is they want to be protected. They want to have trust in their in their vendor and their partner. And at the end of the day, they want to make it super economical.

就是说，没人想花不必要的钱。所以，要以最划算的成本，为他们带来最好的结果，同时让整个体验简单轻松，不用跑来跑去操作七个控制台、坐六把椅子之类的。把事情变简单就好。这是最重要的一点。你知道，客户总是跟我说：“部署你们的东西太容易了，装上就能用。”我今天早上开了个会，有两家公司正在合并，准备选择我们。嗯，另一家公司之前没用过我们，他们的说法是：“你们的东西部署起来真的太容易了。”我觉得这很重要，关键在于让客户尽快看到成效。 现在很多人在谈 AI 会导致失业，尽管这种情况其实还没有发生。我认为到目前为止，净就业岗位的变化恰恰相反，是在增加。类似地，大家也有很多疑问：我的孩子应该学什么？未来的就业会是什么样？诸如此类。Like, no one wants to spend unnecessary money. So give them the best outcome at the best cost and in an experience that doesn't take you know seven consoles and six chairs to run around and and just make it easy. And that's the number one thing you know that our customers always tell me is like it is so easy to deploy your stuff just work I had a meeting this morning and it was like you know what we're there's two companies merging together they're going to go with us. Um the other company didn't have us and it was like your stuff just is so easy to deploy and I think that's a big one is time to value. So a lot of people right now are talking about job losses due to AI although that really hasn't happened. I think the opposite has happened in terms of net job creation so far and similarly there's a lot of questions what should my kids study and what will happen in the future for jobs and the like.

你对此怎么看？或者说，今后你会建议大家往哪个方向发展？你觉得这甚至会是一个长期问题吗？总体而言，从人工智能的角度来看，如果回顾过去几百年里发生的技术变革，每一次重大转变都带来了更多机会和更多工作岗位。岗位性质不同了，但数量更多了。我坚信这次也会如此。比如说编程，你仍然需要有人来管理智能体，也仍然需要有人思考这些架构。事情并不是部署好就可以撒手不管。我知道很多人喜欢这么说，但我们仍然需要人类的创造力。我觉得尤其是在网络安全领域，这是我一直会和年轻人、大学里的学生谈到的一个方向，它是……What is your view on that or where would you recommend people go going forward or you know is this even a long-term issue? Well, just in general from an AI perspective, and if you look at the the technology changes um that have taken place over the last couple hundred years, like each major shift has created more opportunity and more jobs. They're different, but there were more of them. And I firmly believe that's going to be the case here. And you know, when you look at whether it's programming, you're still going to need people to manage the agents. You're still going to need people to think about these architectures. It's not just fire and forget. I know a lot of people would like to say that, but there is human creativity that you're going to need. I think specific to cyber security, this is one of the areas that that I always talk to to young folks about people in uh in university is it's

结论Conclusion

这是一个很棒的领域。如果你了解人工智能，也了解安全，就会知道这是一个不断变化的领域。它其实并不是一个可以彻底解决的问题。我想，每个供应商都愿意说他们解决了这个问题，但它确实无法彻底解决。你必须降低风险，拿出最好的解决方案，并保护好客户。你知道，这其中涉及很多事情，而且情况总是在不断变化。五年后会出现一些我们今天甚至还没想到的技术。如果你在上大学，你们当中就会有人思考该如何保护这些技术。所以，我总是尽量回馈社会，也会关注大学生们在做什么；家长们也总是问我：“我的孩子应该学什么？”a great field to be in. If you understand AI, if you understand security, it it's an everchanging field. It and it's it's actually not a solvable problem. I think every vendor would like to say they solve the problem. It's not solvable. And you you've got to mitigate the risk. You've got to come up with the best solutions. You've got to be able to protect the customers. And you know, there's a lot of things that go into it, but it's always evolving. There's going to be technology in five years that we haven't even thought about today. And if you're in university, you're going to be some of the people that are going to be thinking about how you protect it. So, I always, you know, try to give back and think about the what college kids are doing or I get asked all the time from parents, what should what should my kids study?

我认为网络安全、技术和人工智能都是非常出色的领域。以此作结再好不过了。乔治，非常感谢你参与这次交流。I think cyber security and technology and AI is a fantastic field. It's a great note to end on. Thanks so much for doing this, George.

嘉宾：感谢您参加我们的节目。Guest: Thanks for joining us.

主持人：哦，不客气。非常感谢。Host: Oh, you're quite welcome. Thank you so much.

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