The AI Cybersecurity future is here

Summary

In a discussion on the future of cybersecurity, Jeetu Patel from Cisco emphasizes that AI will be central to enhancing security, particularly in handling the overwhelming amount of data from attacks like email phishing. He highlights the need for integrated platforms over multiple point solutions to correlate data effectively. Patel also addresses the talent shortage in cybersecurity, advocating for a diverse workforce and the importance of user-friendly design in security products. The conversation touches on the potential of AI to augment human efforts, not replace them, and the ethical considerations of data use in AI for security.

Takeaways

• 🤖 The future of cybersecurity is heavily reliant on AI, which will enhance security platforms' effectiveness.
• ⏳ Over 80% of cyber attacks originate from emails, often leading to malware downloads and subsequent network breaches.
• 🚨 The current challenge in cybersecurity is not a lack of data, but the overwhelming amount that overwhelms human analysis capabilities.
• 🛡 AI is crucial for sifting through vast data sets to detect anomalies and predict potential threats, moving from a reactive to a proactive stance.
• 🔒 Critical infrastructure like hospitals, water supply, and power grids are vulnerable to cyber attacks, emphasizing the need for robust security measures.
• 🧩 The cybersecurity industry has been fragmented, with numerous niche companies addressing specific threats, but there's a shift towards integrated platforms for comprehensive security.
• 🔗 Correlation of data across different security control points (email, web, network, identity, endpoint) is key to improving threat detection and response.
• 👨‍💻 The talent shortage in cybersecurity is significant, with millions of jobs going unfilled annually, necessitating AI to scale operations.
• 💼 AI's role in cybersecurity is to assist, augment, and automate tasks, allowing human experts to focus on more complex challenges.
• 🔄 The discussion around the potential Splunk acquisition indicates a strategic move towards becoming a data-centric AI company to bolster security offerings.

Q & A

What is the significance of AI in the future of cybersecurity?

AI is projected to be a driving force in the future of cybersecurity, as it can handle the vast amounts of data and detect patterns that would be overwhelming for human analysts. It's expected to shift the balance in favor of defenders, providing a data advantage and enabling predictive security measures rather than just reactive ones.

Why are email attacks so prevalent, and how does AI help mitigate them?

Email attacks are prevalent because they are a common entry point for malware, often through social engineering tactics. AI assists by correlating data from various security layers such as email, web, and endpoint protection, to detect and respond to threats more effectively.

How does the complexity of managing multiple cybersecurity products impact organizations?

Managing a multitude of cybersecurity products can be untenable due to the high complexity, leading to inefficiencies and potential security gaps. The industry is moving towards integrated platforms that can correlate data across different security layers, simplifying management and improving security efficacy.

What is the role of AI in handling the cybersecurity skills shortage?

AI plays a crucial role in addressing the cybersecurity skills shortage by automating routine tasks, allowing human analysts to focus on higher-order tasks. This approach helps to augment the capabilities of existing staff and maintain security effectiveness despite the talent gap.

How does the encryption visibility engine (EVE) improve cybersecurity?

EVE enhances cybersecurity by detecting anomalies in encrypted traffic, which is a significant challenge as traditional deep packet inspection is not feasible with encryption. EVE uses AI to infer potential threats based on packet behavior, improving the efficacy of security measures.

What is the importance of a single policy engine in integrated cybersecurity platforms?

A single policy engine simplifies the management of security policies across an organization by providing a unified interface. This approach reduces complexity, allows for consistent enforcement of security policies, and enables better correlation of data from various security layers.

How does AI help in making cybersecurity more efficient and cost-effective?

AI contributes to efficiency by automating routine tasks and improving threat detection rates. It also helps in cost-effectiveness by reducing the need for extensive manual intervention, lowering the resources required for managing security, and preventing breaches that could lead to costly damages.

What is the potential impact of a cybersecurity breach on critical infrastructure like hospitals and power grids?

A breach in critical infrastructure can have severe consequences, including loss of life, disruption of essential services, and significant economic impact. AI can help prevent such breaches by providing advanced threat detection and response capabilities.

How does AI assist in managing firewall rules and improving security policy?

AI can analyze and optimize firewall rules, identifying duplicates and suggesting improvements. It allows for the creation and management of policies using natural language, reducing complexity and the need for specialized knowledge, thus enhancing the user experience and security efficacy.

What is the vision for the future of cybersecurity in terms of talent and diversity?

The future of cybersecurity envisions a more diverse workforce, including individuals from various backgrounds and disciplines, to address the broad and complex challenges in the field. This diversity is seen as essential for creating effective security solutions for a global and interconnected world.

How does the concept of 'hallucination' in AI differ between generative AI like ChatGPT and AI used in cybersecurity?

In generative AI, like ChatGPT, 'hallucination' refers to the AI's ability to create new content, which is a feature. In contrast, cybersecurity AI must be precise and accurate, as it deals with critical data and operations where false creations or 'hallucinations' can be detrimental to security.

Outlines

00:00

🛡️ The Pivotal Role of AI in Cybersecurity

The paragraph emphasizes the indispensable role of AI in the future of cybersecurity. It highlights the overwhelming volume of data from email attacks that humans can't manage alone, necessitating AI's assistance. The conversation points out the severe consequences of cyber attacks on critical infrastructures such as hospitals, water supply, and power grids. It also touches on the historical advantage of attackers over defenders due to the high number of variables defenders must account for. However, AI's data correlation capabilities are seen as a potential game-changer, possibly tipping the scales in favor of defenders. The discussion also addresses the evolution from numerous niche cybersecurity products to integrated platforms, which can better correlate data and respond effectively to threats.

05:02

🔍 AI's Capacity to Detect and Prevent Cyber Threats

This section delves into how AI can analyze vast amounts of data to distinguish between normal and anomalous behavior, allowing for a proactive response to cyber threats. It discusses the importance of detecting patterns of activity to predict and prevent breaches, rather than merely detecting and responding after they occur. The conversation also addresses the challenge of data overload for human analysts and how AI can provide a significant improvement by deriving meaningful insights from data. Furthermore, it touches on the integration of AI into various security products, such as firewalls, to increase efficacy, improve user experience, and contain costs.

10:03

🤖 AI as an Assistant and the Broader Impact on Security

The paragraph discusses the application of AI across different security products, not just firewalls, to enhance security efficacy, user experience, and economics. It mentions the introduction of AI assistants that can manage policies in natural language, reducing the complexity and skill required for certain tasks, thus addressing the talent shortage in cybersecurity. The conversation also highlights the importance of handling cybersecurity at a machine scale due to the increasing attack rates and the need for a rapid response that humans alone cannot provide. The potential of AI to assist, augment, and automate tasks is emphasized, allowing human experts to focus on higher-order efforts.

15:03

💧 The Ripple Effect of Cybersecurity on Critical Infrastructure

This section underscores the impact of cybersecurity on critical infrastructures, such as hospitals and utilities, and the potential for loss of life and services due to breaches. It discusses the need for cybersecurity measures to scale up to machine levels to handle the increasing sophistication and volume of cyber threats. The conversation also briefly touches on the potential of AI to manage all security products and the importance of data in driving security strategies. The discussion hints at the company's intention to integrate AI across its product line and the significance of data in improving security outcomes.

20:05

🧠 AI's Evolution and Its Implications for the Future Workforce

The paragraph discusses the evolution of AI and its implications for the future workforce in cybersecurity. It challenges the misconception that AI will replace human jobs, instead arguing that AI will assist and augment human efforts, allowing for a higher bandwidth of work. The conversation emphasizes the need for a diverse workforce in the cybersecurity industry, including individuals from non-technical backgrounds, to reflect the global population that relies on secure technology. It also addresses the importance of designing user-friendly enterprise products and the company's commitment to making its products as intuitive and accessible as consumer technologies.

25:08

🔗 The Importance of Identity in Cybersecurity

This section focuses on the importance of identity as a key component of cybersecurity. It discusses the challenges of managing identity in a world where attacks increasingly target identity as a薄弱环节. The conversation highlights the company's approach to identity management, which involves creating a thin analytics layer that sits on top of existing identity providers to correlate user, machine, and application data. The goal is to enable continuous authentication and response to potential threats based on behavior and context, rather than providing yet another identity provider.

Keywords

💡Cybersecurity

Cybersecurity refers to the practice of protecting systems, networks, and programs from digital attacks. It is central to the video's theme as it discusses the future of security in a world increasingly reliant on digital infrastructure. The script mentions the evolution of cybersecurity, emphasizing the need for AI to combat the overwhelming volume of cyber threats, such as email-based attacks leading to malware downloads.

💡Artificial Intelligence (AI)

AI is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is portrayed as a critical tool for the future of cybersecurity, capable of handling vast amounts of data and identifying patterns that could signify a cyber attack, thus providing a 'data advantage' to defenders over attackers.

💡Data Overload

Data overload occurs when there is too much information to process effectively. The video script highlights this issue, noting that the sheer volume of data from cyber activities is too much for humans to manage, thus necessitating AI's role in making sense of this data and enhancing security measures.

💡Machine Learning

Machine learning is a subset of AI that enables machines to learn and improve from experience without being explicitly programmed. The script alludes to machine learning's importance in cybersecurity, particularly in correlating data from various sources to predict and prevent cyber threats.

💡Encryption

Encryption is the process of encoding messages or information to ensure secure communication and data storage. The script discusses the challenge that widespread encryption poses to traditional cybersecurity methods, such as deep packet inspection, and how AI can help infer potential threats from encrypted traffic.

💡Anomaly Detection

Anomaly detection is the identification of unusual patterns that indicate a potential cyber threat. The video emphasizes AI's role in anomaly detection, as it can analyze patterns of behavior to differentiate between normal and abnormal activities, allowing for a more proactive cybersecurity stance.

💡Integrated Platforms

Integrated platforms refer to comprehensive systems that consolidate multiple functionalities into a single, unified platform. The script discusses the shift from numerous specialized cybersecurity tools to integrated platforms that can correlate data more effectively, enhancing the overall security posture.

💡Natural Language Processing (NLP)

NLP is a branch of AI that focuses on the interaction between computers and humans through natural language. The video script mentions the use of NLP in AI assistants for firewalls and other security products, allowing for more intuitive policy setting and management.

💡Identity Intelligence

Identity intelligence involves the use of AI to analyze and protect user identities and access privileges. The script discusses the importance of identity as a critical attack vector and the need for an 'identity intelligence layer' that can provide continuous authentication and anomaly detection.

💡Efficacy

Efficacy in the context of cybersecurity refers to the effectiveness of security measures in preventing cyber threats. The video script repeatedly emphasizes the need for AI to increase the efficacy of cybersecurity by improving the correlation of data and the ability to predict and prevent attacks.

💡Human-Machine Collaboration

Human-machine collaboration is the partnership between humans and machines, where each complements the other's strengths. The script suggests that AI in cybersecurity is not about replacing human jobs but about augmenting human capabilities, allowing for a more efficient and effective cybersecurity approach.

Highlights

The future of cyber security is expected to be powered by AI, which will be essential for an effective security platform.

80% of cyber attacks originate from email, highlighting the importance of email security in cyber defense.

AI can help manage the overwhelming amount of data in cybersecurity, which is often too much for human analysts.

Cyber warfare can have severe real-world consequences, such as disrupting hospitals, water supply, power grids, and financial systems.

There's a significant shortage of skilled cybersecurity professionals, emphasizing the need for AI to scale defenses.

AI can correlate data effectively to predict and prevent cyber attacks, moving beyond just detection and response.

The cybersecurity industry has been fragmented, with many niche companies offering specific solutions, but a shift towards integrated platforms is occurring.

Integrating multiple cybersecurity products into a single platform allows for better data correlation and a more unified defense.

AI can help reduce the complexity of managing numerous cybersecurity products, leading to more efficient defenses.

The importance of natural language processing in AI for simplifying cybersecurity policy management and reducing the need for technical expertise.

AI can assist in addressing the talent shortage in cybersecurity by automating routine tasks and allowing experts to focus on more complex issues.

Cisco's AI initiatives aim to increase security efficacy, improve user experience, and contain economic costs.

AI's role in cybersecurity is not to replace human jobs but to assist, augment, and automate tasks, allowing humans to focus on higher-level challenges.

The potential of AI in cybersecurity extends beyond just firewalls, with plans to integrate AI across all Cisco products for a comprehensive defense.

AI's ability to handle large volumes of data is crucial for cybersecurity, as the field is essentially a data game.

Cisco's focus on privacy ensures that AI is used responsibly, with explicit consent required for any data used to train models.

The future of cybersecurity will require a diverse workforce, including not just AI experts but also individuals from various backgrounds and disciplines.

AI's role in cybersecurity is to assist in decision-making, not to replace human judgment, which remains crucial for the foreseeable future.

Transcripts

Is it fair to say that the future of cyber security is AI?

The future of cyber security is going to be powered by AI and anyone that thinks otherwise

is not going to have an effective platform for security.

If you think about 80% of the attacks that happen originate from email.

Yep.

Like you get an email from some prince from some exotic country and then they'll say,

well, click on this link and you can download your $10 million.

Yep.

From there, you actually download some malware.

But the problem people have, right?

Too much data, too much it's overload for a human.

Correct. That's where AI helps.

Okay.

It took a while for folks to realize is at this point, think about it, warfare starts with cyber.

Yeah.

A hospital, people would lose their lives if the hospital got breached.

Yeah.

Because people wouldn't be able to get dialysis.

Yeah.

Or whatever the treatment is that they're looking to get.

Water supply can stop for a city.

You can have power grids completely stopped.

You can have the financial system stop.

Your healthcare system stops.

There's a tremendous impact with that shortage of skill.

We have to make sure that we actually get to that at machine scale.

Hey everyone, it's David Bombal coming to you from Cisco Live back with Jeetu.

Jeetu, welcome.

Great to have you back here.

Thank you.

Thank you for having me, David.

It's great to see you again, man.

Likewise.

You on stage.

I saw today lots of announcements.

Is anyone on stage these days?

Well, but big question.

Recently, they're calling it the mother of all breaches.

26 billion records got leaked.

Last time we spoke about the attackers are, you know,

it looks like the attackers are winning.

Hopefully, you guys are going to do something about that.

Well, you know, for 30 years, David, it's been the way that things have actually transpired.

The adversary has the advantage over the defender.

And the reason for that is because they have to be right once.

We have to be right every single time.

I think this is the first time, at least in my professional career,

that I see a light at the end of the tunnel where I think the scales might tip in favor

of the defender because they'll have a data advantage.

The defenders will have a data advantage over time.

Now, it's not quite, we're not there today.

OK.

But I do see that being a possible state in the future.

And we're pretty excited about the things that we can do with it

because it's not just having a lot of data.

We've always had a lot of data.

I think the ability to correlate data effectively

and then reason with that intelligence that's there and then be able to feed that

into everything else is where I think we'll be able to predict and prevent rather than just detect

and respond.

Last time we spoke about the fact that cybersecurity especially has all of these sort of niche

companies, it seems, that are providing a very specific solution, but there's no overall solution.

Yeah, the way that innovation has evolved in cybersecurity, it's all through patchwork.

There's a new threat that comes up.

There's a new company that came out to go out and help the threat.

The ironic part about this is that model started so that the efficacy would be high for security.

That is the model that's breaking security down today because on average people have

50 to 70 different products in their cybersecurity stack.

Yeah.

And it's untenable to go out and manage that anymore.

Exactly.

The complexity is too high.

And so what needs to happen, and I think there's the shift that's occurring right now in the market.

We'd like to think we're one of the people that are driving it, but the shift that's occurring is

moving from a bunch of point solutions to integrated platforms.

And what that'll allow you to do is have a single policy engine.

It'll have a single kind of core design system.

And it's going to allow us to do things that we couldn't do before, largely because you

could correlate data sets that you weren't able to in the past.

If you think about 80% of the attacks that happen originate from email.

You get an email from some prince from some exotic country.

You're very polite.

Yep. And then they'll say, well, click on this link and you can download your $10 million.

It takes you to a website that didn't exist two hours ago.

From there, you actually download some malware.

That malware kicks off a process on your PC that then does lateral movement throughout

the entire network.

That is 80% of the attacks that happen today.

Now, what happens in those attacks and how the defenses work?

There is a company that does email protection.

There's a company that does web protection and DNS protection.

There's a company that's actually doing things around endpoint protection.

And then there's a company that's doing something on network.

Each one of them have their own telemetry.

And so an email that comes in gets passed as a low level alert.

And then a website that you go to might get passed as a low level alert.

And a process that got kickstarted on the endpoint might be tagged as a low level alert.

But if you put those three low level alerts together, that is a high level alert.

Low level alerts get ignored because of the signal and noise ratio being way off.

But a high level alert, you would do something about.

And that only happens when you actually start to correlate telemetry.

And that's the idea of data, right?

That's the idea of making sure that you have data across multiple control points,

email, web, network, identity, endpoint, that you can start to correlate together

and say what's anomalous versus what's normal behavior.

And when you start detecting patterns of legitimate activity

versus patterns of abnormal activity, you can then start to have a graduated set of

responses that you can apply to that.

Quarantine this.

Make sure that you kill this session.

Make sure that you actually have gone out and done a snapshot backup.

All of those things can start happening once you have that core set of, you know,

kind of indication that this is in fact a breach that's about to happen.

And therefore, I'm going to predict and prevent rather than just detect and respond.

But the problem people have, right?

Too much data, too much.

It's overload for a human.

Correct. That's where AI helps.

So up until now, we've not had a data scarcity issue.

We've had an issue of making deriving meaning from that.

And that's where AI can massively kind of bits of step function improvement.

So I know that I interviewed DJ, I believe, is in your team.

Love DJ, man.

On the firewall.

Like there's an assistant for a firewall.

Yes. Are we talking about that or is there more?

No. So what we're talking about on the firewall, so the way that we think about it is there's

three major objectives in security that we're trying to go out and solve.

First one is massively increase the efficacy of security.

Second one is make sure that your experience gets way better than what it is right now.

Yeah.

And then third one is make sure that the economics are contained so that you don't

end up spending more money.

Because right now, if you look at the trajectory over the past 15 years,

people have spent an inordinate amount of money in security.

And it's not effective because it's not.

It's not effective actually.

Yeah.

And so you have to do all these three at once.

In order to do all these three things at once, on the efficacy side,

what I'm talking about is a correlation of data.

Okay.

On the efficient experience side, what I'm talking is what DJ was talking about,

which is I can go out and set a policy in natural language.

And what that policy natural language is going to do is it's going to reduce the

dexterity that people need to go out and get certain things done.

And so then you actually can address the talent shortage.

Because right now, 4 million jobs just in the US go unfilled every year.

Millions go unfilled throughout the world.

And how can we go out and make sure that that changes quite dramatically?

Because if the attack rates keep going up and keep compounding,

and your talent shortage remains, you just by definition get weaker.

So you have to handle this at machine scale.

You can't handle it at the human scale.

So does Cisco just have AI on a firewall?

Is it bigger than that?

No, no.

So the beauty about this is we started with AI on the firewall,

but it was such a huge problem.

And we said, wow, firstly, wouldn't it be nice if you had the ability to have

natural language interface on every single product of ours?

So you can set policy.

You can do basic hygiene on policy.

So one of the demos we showed this morning at the keynote was the ability to say,

I've got 1200 rules in my firewall, but 350 of them are duplicates.

So what do I do?

Do I disable them?

Do I delete them?

In order to go out and even determine that in a reasoning engine,

when you're creating a rule, it would be something that right now is not something that people do.

And now this is something that you can do in a very easy way.

It's not only is it easier, it does more while making it a whole lot easier.

And so then the people that are working on these things,

it's not like you don't need them.

You will just be able to have them focus on higher order efforts rather than the things

that actually you use up a huge amount of cycles.

It requires a very high level of dexterity and isn't really progressing us as fast as we need

to progress.

We need all the human minds on the problem of security.

So one thing that I think people need to take away from this is

it's not that we're trying to eliminate jobs.

What we're trying to do is make sure that those jobs that are being done

can be done at a higher bandwidth so that the mundane work can actually happen

with the augmentation of a machine.

I mean, old people like me, you know, firewall...

You're still younger than me, which is really depressing.

Firewall rules are a nightmare, right?

CLI was difficult in the old days.

It's like assembly code.

That's right.

Yeah.

And I mean, you don't want to touch it because you might break it.

Yes.

So we need something...

And actually, the people that wrote the rules retired, the kids graduated from college.

Exactly.

And so now you've got...

There's companies we know that have millions of rules on the firewall.

And so what they do is because they don't want to break anything,

they just don't touch it.

Exactly.

And then at some point in time, that system which was designed for efficacy

is actually the most ineffective system that's out there.

I mean, you mentioned in your keynote, AI is not going to replace it.

Assist, I think, augment and automate, will you?

Assist, augment and automate.

So why we do...

Why we are using AI is to increase efficacy,

improve the experience and improve the economics.

How we do it is by assisting every human to actually have an assistant.

Yeah.

Augmenting so that the detection is going to happen at a much faster pace

and make sure that the human's in the loop and then automate certain tasks

that can be done so that humans can actually be graduated to doing better tasks.

Yeah, because the warrior's jobs, right?

Like you mentioned, and you've addressed that.

We just simply don't have the number of people to go out

and address the number of attacks that are happening.

And by the way, this is critical infrastructure

that's going to damage other critical infrastructure.

The thing that people...

It took a while for folks to realize is at this point,

think about it, warfare starts with cyber.

A hospital, people would lose their lives if a hospital got breached

because people wouldn't be able to get dialysis

or whatever the treatment is that they're looking to get.

Water supply can stop for a city.

You can have power grids completely stopped.

You can have the financial system stop.

Your healthcare system stops.

So there's a tremendous impact with that shortage of skill.

And we have to make sure that we actually get to that at machine scale.

So you've mentioned firewalls quite a bit.

There are other products, right, where the AI is involved in.

So our AI assistant actually manages all of our products.

So we started with firewall.

We just added secure access, which is our competitor, the SASE market.

And so you can actually go out and have policies that are set

with natural language in our secure access product.

We have announced a SOC assistant, which will come out later in the year.

We will have, we currently also have a documentation site for our,

if people want to find out exact documentation that,

how do you do this? How do you do that?

That's actually going to be in that that's available with an AI assistant.

And we will continue to keep adding email as well, right?

Yeah. So email has, we just announced today that email threat defense

will have some AI capabilities to do better detection of spam

and so on and so forth.

And so every single product will have different uses for it,

but we will make sure that AI gets injected across the board.

Because frankly, the way I think about it, security is a data game.

Yeah.

And you cannot make sense of data without having strong, robust AI models in place.

How do you get the data?

Is that perhaps related to the possible Splunk acquisition?

Splunk will definitely be one of the contributors of that.

And the way you think about this is, you know, we are a networking company

in order to be a great networking company, we have to be a great security company.

I love that. Yeah.

If you're going to be a great security company, you have to be a great AI company.

And you cannot be a great AI company if you're not a great data company.

And Splunk will definitely allow us to scale that to a very different level.

We can't talk much about that because we're in the regulatory approval process right now.

But in addition to Splunk, we just have to think about all aspects where we have telemetry

that can be pulled together.

We have data about every packet that flows through the network.

We have data about every email and every forward that happens.

We have data about every process that gets kickstarted on an endpoint.

We have data about every WebDNS query that gets made.

We have data about every identity, human or machine identity.

Now with the identity intelligence engine,

where you can tell whether or not there's anomalous behavior,

both reactively, David's doing something that seems out of character, or proactively.

I've got these three devices that I've never logged in for three years.

Maybe I should decommission those devices and instill a level of basic hygiene.

So all of those things, that telemetry, if we can take and start to correlate,

where there's magic that can happen.

And that's why I think there's an advantage to,

once you start really mining this data effectively for the customer being safe.

I'm going to push on that word mining because a lot of people may be concerned

about giving their personal data or confidential data to AI.

Yeah, so the way that we do it is firstly,

when you start thinking about the world of security,

it's slightly different in the sense that if there's an incident that's happening,

you want to know what that incident is so that others can actually get prevented from it.

But we don't use anyone's data.

So what I'm talking about is an instance that you would have with your data.

Okay, so it's personal to my company?

Personal to your company.

And you'll say, okay, here's your data.

Not like the typical check GBT thing.

No, no, no.

And so we would actually make sure that that's there.

But in security, in the world of security,

you typically have a pretty common practice of making sure that certain kinds of data

on incidents and breaches get shared with the community so that you know what's going on.

Otherwise, you're fighting against the adversary.

But the thing to keep in mind is this, what I'm talking about over here is largely for

like, one of our big core value systems is privacy.

And we think it's a basic human right.

And we should, if we ever used a data set to train the model,

there would be with explicit consent.

I mean, I suppose that's an advantage, right?

Because you've got AI for my company, but Cisco also have a lot of data from many people that

are perhaps sharing it with you.

So I think one of the concern is ChatGPT is like pulling data from the internet,

but that's very different to the AI that you're using.

Yeah, I mean, on the data side, like it's a very different thing from ChatGPT,

because in ChatGPT, for example, you would say, what's the weather like today?

With us, it's more around how do I go out and configure this policy?

And that's a very different kind of way that you've actually trained the model.

And it's a very constrained set of data that we've actually trained the model on.

It's called retrieval augmented generation, where you actually make sure that

you train the model based on a very specific custom set of data for that account.

And that's important because of hallucinations as well.

Yeah, hallucinations in ChatGPT is a feature, not a bug, right?

Because when you start thinking about it, in some cases, what ends up happening is

the creative personas.

If you think about how AI evolved, and Sam Altman had a great kind of thought over here

in one of the podcasts, he was in where he said that if you would have asked him five years ago,

seven years ago, how is AI going to evolve?

He would have said, it starts with the blue collar workers.

Yeah, exactly.

Right.

And then it's going to go to the knowledge workers and then to the super high creatives.

It's actually turned out exactly the opposite.

Exactly.

Yeah.

Why is that?

Part of the reason is because hallucination actually helped in actually creating the

creative wandering that can happen.

That does not work in security.

I was going to say.

I can't hallucinate about a firewall policy I want to implement.

Exactly.

So it has to be pretty precise.

So what we do is we actually create a...

The way in which you train the models is very decent.

I think another misconception people perhaps have is just because of ChatGPT, they think

it's just an assistant or an interface, but like on the firewalls, you've got the assistant,

but you've also got EVE, right?

Yes.

Can you just explain that and how perhaps AI is doing similar things on other products?

So I think generative AI through transformers is one dimension of AI that actually has gotten a

lot of popularity because of ChatGPT over the course of the past year.

I think it's fantastic because what it's done is it's allowed us to, as humans, communicate

with a machine without learning the language of the machine, but actually requiring the machine

to learn the language of humans, which is great.

This is the first time that's happened.

But that's the transformer side and that's the understanding of natural language.

There's a bunch of other areas in machine learning and AI

which actually have little to do with language expertise.

And more to do with detecting anomalies and making sure that you can find patterns of behavior.

And so encryption visibility engine is a really interesting problem to solve because

that we solved with the encryption visibility engine, because if you think about majority of

the traffic today is encrypted.

It's a problem.

And so we used to have this technology, we have this technology in firewalls called deep

packet inspection, where you can actually go and inspect a packet to know if there's malware inside

that packet.

And if there is, then you can intercept or block it.

Now, if everything's encrypted, there's no way for you to tell.

So the only way you can tell is by inferring based on the movement of the packet and whether

or not it's anomalous behavior.

And that's what EVE does.

And that's actually a pretty major step function improvement in driving efficacy once again.

So what you'll see is we're doing one of three things.

Something like Eve drives efficacy.

Something like AI assistant drives the experience.

And it also drives the economics.

And so those are the ways that we kind of think about it.

So EVE kind of like, I don't want to put it, I'm not quite sure how to say it, but like the EVE

kind of technology perhaps will be used on email and other spheres as well.

It'll be used across the board.

EVE itself is being used in the firewall largely.

Yeah, but like a similar kind of concept.

The concept of empowering these different tools with strong AI that's not generative in nature,

but strong AI and also machine learning aspects.

That's something we'll have in every single one of our products.

And if you think about our AI team and you said you spoke to DJ, we bought his company

and they actually are driving all of our AI efforts right now.

And that company is actually getting funded quite aggressively so that we can hire more

and more people.

We need PhDs and researchers and developers and all different kinds of people in AI.

And we will continue to keep making sure that the clock speed of innovation over there

is very, very high.

So I'm glad you mentioned that because looking forward, where do you see things going?

Because I think a big concern for people who are moving into this industry or interested in this

industry or perhaps younger is it's pointless.

AI is going to take my job away.

There's no future in this.

Or can you address that?

And also like, where do you see things going?

Just to give people a roadmap and like sort of a vision.

Yeah, I think in my mind, I think human judgment is going to be pretty important for the foreseeable

future.

And now that I am completely wide-eyed about the fact that there's some upside in AI, but

there's also going to be a significant downside to humanity.

And I think we can go into the areas of regulatory kind of aspects that need to be.

And we should talk about that a little bit.

But when you start thinking about specifically, you know, new talent coming in, the addressable

market for security is 8 billion people, right?

Every human on the planet is going to be connected and every human on the planet needs to be

secured.

Yeah, we need more people from diverse backgrounds to come into this industry.

This industry is very homogeneous on multiple dimensions, largely male dominated, largely a

technical audience, very jargon fill.

They actually, the industry has made it very intimidating for someone that doesn't know

security to say, what do I do with security?

Exactly.

I think we have to make sure that we remove that friction.

And that requires, if you're going to build products for 8 billion people, you better

have the group that's building products be representative of the population makeup of

the 8 billion people that you're building products for.

By definition, if you don't have half the people that are women, you're probably not

going to be as effective for the people that you're building them for.

We need more people who are women in this industry.

We need more people with liberal arts backgrounds.

I would love to have more people with a design background.

I would love to have more people with backgrounds that come from the consumer tech industry.

Like I, you know, one of the things that I've always asked my teams is hire people sometimes

from Spotify because they know how hundreds of millions of people can actually turn on

us all with a couple of clicks.

I read a blog, I think you wrote about like the interface is important or you said something

about like the interface is so important.

It's like historically me going back to Cisco many years.

Cisco weren't well known for that.

One of my commitments I've made to Chuck is, hey, when you think about Cisco and you see it,

you know, fast forward five years, 10 years, what I'd like to leave this company in the

hands of the next generation is going to be running it is to be one of the most designed

forward companies only second to Apple.

That's correct.

In fact, I admire Apple so much for what they've done with all of their products.

And the great part is we have a great partnership with them.

They happen to be, you know, they were here at the event and we had a fireside chat with

them and we've actually just built a product on the collaboration side of the house with Vision

Pro.

And I would love to have that same kind of obsession on finesse that's instituted in enterprise

products as it was in consumer products.

And you're starting to see that now.

And so all the new products that we have coming out, they're beautiful to use and they're not

overly complex.

And we'll make them we'll keep chipping away them and make them simpler and simpler and

simpler as time goes on.

Like, you know, if you think about passwordless, you think about, you know, all of the things

that we're doing with SSE, we try to make the demos for the end user the most boring demos.

Yeah.

Because that means that you don't have to do much.

It's just invisible.

And behind the scenes, it just works.

I think the concern with AIs, do I need a PhD?

A lot of guys in the beginning needed PhDs and like all this kind of knowledge.

But do you see like the teams that you're hiring?

Are you hiring all kinds of different types of people at different levels?

Not just PhD people for AI?

Yeah. I mean, look, I think there's a tremendous role that AI researchers and PhDs play, but

that's not the only role.

Yeah.

Right.

And anything that's a transformative platform, which I think AI is probably the most wide

transformation we will have seen in our lifetimes.

You know, it's the most consequential one.

You're going to need people from all walks of life.

And you're going to need people that deeply understand the tech.

You need people who deeply understand the limitations of the tech.

You need people who deeply understand the human dynamic.

And many, many other kinds of vocations that you're going to need.

So I personally feel like the kind of people we're going to need, we just have to make

sure that the kind of people we attract to these fields keep a level of diversity to

effectively address development of products for the target market.

You introduced yourself as Jeetu, but how do I know that you actually are Jeetu?

You know, the identity is one of the biggest attack vectors that's actually emerging right

now. And it's already emerged, frankly.

Yeah.

And so many people say identity is a new perimeter.

Many people say it's one of the largest attack vectors.

And what you've seen as a challenge with identity so far, firstly, anyone who goes

out and tries to tackle the identity problem tries to go out and provide you with another

an IDP.

Yeah.

An identity provider.

They become an identity provider.

Our goal is I don't think the world needs yet another active directory.

I don't think the world needs yet another LDAP.

We don't need another identity provider.

We've got plenty of them.

We need to make sure that we have a thin analytics layer that sits on top of the IDPs

that can take the data for users, for machines, for applications, and then correlate that

data together.

Right.

And so we launched this identity intelligence layer, which I think is fundamentally going

to change how every application and every platform can get hydrated with identity

intelligence so that we then are able to take that data and not just apply enforcements

for identity at the point of authentication, but do it on a continuous basis based on

behavior and context.

So David logged in.

Yeah.

But he's doing something funny with customer database.

That's not what David typically does.

Maybe I need to quarantine that.

Yeah.

Maybe I have a honeypot that I create so that I can let it keep going for a while to see

what's happening without actually killing the session so that I see if I can actually

catch someone doing something wrong.

But those are the kind of things that we have to actually create.

One is you have to have a better mechanism for detecting post authentication, how anomalies

are occurring, and then based on that, have a graduated set of responses against that

potential threat and also apply hygiene measures so that you can say, well, these are three

devices, like I said earlier, that have never been used before.

Let's decommission them.

I was doing a POC on Zendesk and I was actually connecting that to my Salesforce account.

But then we decided we are not going to do anything with the POC.

And then I still have that account connected to my Salesforce account.

That doesn't seem like the right thing to do.

I need to make sure that I killed that connection.

And so what are those kinds of things that we need to have really come up and surface up?

That's what we're trying to do.

Is it fair to say that the future of cybersecurity is AI?

The future of cybersecurity is going to be powered by AI and anyone that thinks otherwise

is not going to have an effective platform for security.

Jeetu, I really want to thank you for sharing.

I know you got to go.

Thanks so much.

Appreciate it.