When we imagine a world of autonomous vehicles, we picture them functioning in a calm and orderly fashion up and down the highway, like robots in a well organised factory. But if your organisation is operating a fleet of them, and a major emergency strikes – let’s say an earthquake – then you need to be sure you have a rock solid way to centrally control and orchestrate these vehicles or chaos will result.

Galeal Zino, Founder and CEO with zero trust connectivity specialist NetFoundry, confronted this challenge in a recent interview conducted by Roy Chua, Founder and industry analyst at AvidThink. The ramifications were starkly clear: “In any kind of serious emergency you need to get emergency vehicles on the scene as fast as possible, and that means making sure that other cars are not clogging the roads,” points out Zino. “If you imagine that most are autonomous then that’s a pretty big test for how well they are centrally controlled.”

The orchestration of these vehicles might rely on a V2X (vehicle to everything) communication system that enables the sharing of information, or if not that then some other form of centralised control. “Well, if you can control cars in this way, so can a hacker,” warns Zino. “Somebody with malice in mind can turn your autonomous car or truck into what is essentially a missile.”

NetFoundry recently demonstrated a solution to this problem in tandem with Lockheed Martin and the University of Auburn: “We showed how you can handle all that V2X communication safely, over a native zero-trust NetFoundry network. You can control vehicles in a number of possible emergency situations, but an attacker has no access to them at all.”

A solution of this sort is really the only way you can establish a reliable private global network between multiple end points, whether that’s between an autonomous car and a server, or between a drone and somebody on the ground with a 5G handset, or even between an API and the agentic AI that’s talking to it. Beyond the confines of the WAN, where its either a VPN or the Internet carrying your traffic, then whether we’re talking agentic flows, autonomous flows or IoT flows, none are really secure. This means people have historically had to rely either on open communications, or have had to build their own secure encryption stack.

Some, says Zino, are using an Access Point Name (APN), a unique identifier that tells a mobile device how to connect to a specific network: “It’s a private mobile connection, but it fosters a dangerous illusion because what a private APN really does is take traffic to the nearest cell location, like a Packet Data Network Gateway (PGW). Then it’s a VPN connection from that PGW back to wherever the server is, on AWS, Azure, GCP or whatever. Opening up a huge network-level tunnel like that is a really bad idea. We don’t do that inside our WAN with SASE, and yet we do it for more critical workloads outside the WAN.”

A range of use cases

A zero-trust secure connection has many applications beyond automotive. It’s really for any industry that cares about security, and managing that security at scale. Zino says that financial services is a prime instance, with NetFoundry active in the majority of the top US banks. He also cites healthcare and critical infrastructure.

“The problem all these sectors have had is being forced to trade off between security and complexity,” he says. “But if you move to a ‘secure by design’ basis, similar to what happened with DevOps as we shifted left, all of a sudden everything becomes a lot simpler to implement. That’s where we have found the most amount of traction so far. If you secure the underlying layer and provide an abstraction that’s also secure, with the communication on top, you don’t have to worry about all the complicated things like identity authentication and mutual authentication.”

Clearly this model is preferable to starting with an inherently insecure network, one whose job it is to deliver packets whether authenticated or unauthenticated. Before long you’re bolting a bunch of day two security on top to compensate for the fact that there are some bad packets in there. A software-based overlay like NetFoundry’s adopts the opposite model where no packets are allowed on the overlay unless they have been strongly identified, authenticated and authorised. By defining what is permitted on the network, then any device or vehicle trying to talk to a server that it shouldn’t be talking to can’t even get on the network. The same applies with any kind of critical infrastructure – oil, gas local government applications, law enforcement, public safety, manufacturing and of course the financial sector.

Drilling down into the example of manufacturing, Zino points out that while it accounts for around 15% of the world’s GDP, it is largely ‘air-gapped’, or at least not very connected. “This will need to change in a future of robotics, edge AI, preventative maintenance, digital twins, energy optimisation. Manufacturing organisations do have to connect outside these days. We work with one of the world’s biggest industrial automation leaders, headquartered in Germany. They’ve built their products to make them zero trust native. So for a manufacturer or a grid operator or anybody deploying their vast array of products, they are doing it via a zero trust connection without having to go and figure out port forwarding, IP addresses, VLANs and VPNs. That always means a mess.”

The trick, Zino says, is to let an expert stack take care of all the complicated things below, so you can just focus on getting the applications right and not worry about other elements. By trying to handle the whole infrastructure around the trust issue yourself, then suddenly you’re deep in identity management, certificates, mutual authentication, and that’s the messy part that people too often get wrong. That’s usually where the weakness is, rather than in the actual transport encryption protocol. When it comes to trying to identify the cause of a cyberattack then people are soon blaming the fact that they didn’t upgrade, or somebody misconfigured the system. Naturally bad things are going to happen when it’s all a highly complex day two operation. But it doesn’t have to be like that. You don’t need to handle it yourself.

Now, according to Zino, NetFoundry is taking everything a step further: “Normally the encryption key stays sovereign to the endpoints, but not in our model,” he explains. “Data sovereignty is important, especially when you have clients all over the world. In the EU there’s a particular sensitivity around who holds the encryption keys, especially where an organisation is not native to Europe.

The future of networking

On the subject of NetFoundry’s future ambitions. “We just want to make the world a more secure place,” he says. “We’re already working with some very impressive names. That’s probably because the motivation to use us is strongest in Fortune 500 banks, in critical infrastructure, in manufacturing. Retail and hospitality are likely future targets. We’ve always managed to punch above our weight as a startup.”

The company’s future focus, he believes, is likely to be as much horizontal as vertical, reflecting the reality of modern networking: “We want to be looking at secure networking as part of a wider business transformation initiative. The old days was about building cloud native apps and the lifting and shifting of apps to the cloud, app modernisation, digital transformation. Now it’s more about agentic AI, where people want AI and LLMs to be local, sovereign, in their data centre. The data it’s talking to is likely to be really sensitive data. You might need it to talk to the rest of the world. People are starting to want an application-specific network, a network that agentic AI uses to talk to its databases where nothing else is allowed on the network. It’s quarantined, isolated by design. That’s not something you want to be trying to figure out after the fact, let alone building for yourself. This will be a big growth area for us.”

By Guy Matthews, Editor, NetEvents

The post When emergency strikes, it’s time for a native zero-trust network appeared first on NetEvents.

Many of today’s digitally-focussed businesses are looking to extend high-performance network connectivity to a hybrid workforce, sometimes on a global basis. And they are having to do this in the face of a surge in security threats. At the same time, Generative and Agentic AI are rewriting the rulebook on how and where work gets done, creating the need to safeguard and connect a whole new class of AI-driven apps and services. CIOs and CISOs are expected to manage this three way challenge – networks, security and AI. The ideal way for them to achieve this is through some kind of converged solution that is designed to handle all three.

The idea of converged solutions is, of course, nothing new. Over the past few years, we have seen a coming together of the once separate worlds of networking and security. Solutions such as SASE have tried to meet the need for a more integrated approach. But surging levels of cyber risk are driving hard pressed network managers and CISOs to seek new and better answers. They must move on from a mess of point solutions in favour of a platform that offers optimal network protection while giving them full visibility of possible anomalies. 

AI is busily complicating the challenge further, argues Rik Turner, Chief Analyst, Cybersecurity with independent consultancy Omdia: “AI is changing the game once more, providing a hugely increased attack surface and a raft of new headaches,” he says. 

It may be time, he believes, to incorporate AI into the mix, converging it with the existing SASE model. It’s certainly time to move on from the kind of basic SASE envisaged by Gartner in 2019 when it first launched the concept of the secure access service edge: “Originally it was a bundle of technologies spanning the two domains – networks and security,” says Turner. “With the explosion of the new variants of AI since 2022, first Generative then Agentic, it was inevitable that these new technologies would be applied to SASE, just as they are invading every other aspect of tech. AI can help speed path selection on the networking side of things, while in security it can streamline the detection of threats and enable faster response.”

It’s time, he suggests, for organisations to benefit from a form of SASE that supports their employees’ use of software-as-a-service (SaaS) apps, given that a lot of GenAI is delivered in SaaS mode. “In this way, SASE can also be used to provide security and governance around those services,” he says. “In other words, which services are sanctioned and which blocked, and within the sanctioned ones, what kinds of prompts can be used and what data can be up- and downloaded by the employee.”

Bringing it all together

The kind of converged solution that can meaningfully address the triple challenges of networking, security and AI cannot just be stitched together from legacy products, says Aditya K Sood, VP Security & AI Strategy at Aryaka, a company specializing in unified SASE. The need for an integrated and converged approach to networking, security and AI is, he says, behind Aryaka’s newly launched Unified SASE as a Service 2.0. The new platform incorporates several features to accommodate booming remote work and rising AI adoption, ensuring that users can securely connect to any application, anywhere, with performance, simplicity, and agility.

“The attack surface is expanding,” he explains. “And simplistic solutions are not going to help us. The route to protection lies in a solid network. The days of looking at alerts and logs in siloes is over, and what we need is a convergence point offering one place where you can look at any anomalies happening in the network. A place where you easily see what’s happening with availability as well as security and integrity. This will give you a true picture of what your enterprise looks like. CISOs won’t get this from a series of individual tools. With networking and security in their own silo, you don’t get visibility of both sides. If both are contextualized at the same time you can get a true 360 degree picture. You need a platform to serve that purpose.”

Integrating the networking and security part is one thing, he says, but bringing in the issue of AI takes it to a new level: “Use of AI is increasing exponentially, which means we need to understand the behaviour of users and how they are interacting with it. At Aryaka, we believe that you need one console for all this – network, security and AI. With a unified control plane you can enforce policies. You can design policies so that they go across similar data assets. This means you don’t have to deal with individual policies for different kinds of asset.” 

Visibility is key to any such platform, argues Sood, given that the CISO is not dealing with one standard threat model: “You need visibility that looks at more than one aspect at a time,” he says. “If the CISO doesn’t have this then they are in difficulty. They need a very definite taxonomy that defines, say, the different components of the AI ecosystem. What is the threat model for each component? From there you can start to build your defences and strategy. 

The need for accountability

A converged platform is the only way to offer today’s CISO a way to confront a major difficulty: accountability. In the event of a security breach, the CISO will be called upon to report not only to their board level colleagues but also to the regulatory authorities. The visibility offered by a converged platform is their best weapon.

“There was a time when there would be an incident and the CISO would react in a certain way,” says Sood. “With the world as it is now, you have to assume that the breach has already happened. The CISO must be proactive rather than reactive. When talking to regulators or other executives on the board, numbers are what they understand. Your fellow C-suite people will be asking about ROI for security spend. You need to be able to talk about the number of high risk vulnerabilities you have tackled over a particular period of time. This needs to be transformed into an exploitation matrix, showing how you’ve reduced the attack surface. The numbers have to speak.  It’s a way to demonstrate the return on the tools deployed. You need a way to show how AI has increased the attack surface, perhaps by 4x. You have to say ‘We are entering into the AI world and this is the risk we face. AI adds to the need for visibility, to see for example which users are bringing in their own devices and using AI on them.” 

A fully converged approach to networking and security is the only way to help enterprises gain the performance, protection and visibility needed to confidently adopt AI at scale, in both on-premise and hybrid environments. The right converged offering allows organizations to innovate without exposing intellectual property and assets, and permits its employees to operate where they need to with all the tools at their disposal to enable maximum productivity.

By Guy Matthews, Editor, NetEvents

The post Time to converge networks, security and AI to beat the CISO blues appeared first on NetEvents.

Artificial Intelligence (AI), powered by large language models (LLMs), is transforming the way we approach a range of tasks. In addition to innumerable consumer use cases, powerful AI tools are also making inroads into the corporate sphere, changing how business is done.

The next frontier for AI is tackling the challenge of cybersecurity, in particular through improving the process of testing for vulnerabilities across the ICT ecosystem. Commonly deployed to support rather than to replace the efforts of human experts, AI is a useful weapon when it comes to finding weaknesses, adept as it is at automating processes, improving speed and scale, and detecting threats that old school methods might miss.

But the kind of language models that power most AI use cases are less effective at dealing with security risks than they are with other areas of business. That is because, by their nature, cyber threats come from the kind of shady and unscrutinized places that lie outside the realm of the typical dataset.

“AI and LLMs work by ingesting everything on the public Internet,” explains Dave Gerry, CEO of crowdsourced cybersecurity specialist Bugcrowd. “Then they use that data to train their models to answer questions based on what they’ve seen. But when it comes to security, that method isn’t very helpful as it will only be able to find out about things that are already known.”

The answer lies in developing models that are trained using Reinforcement Learning (RL) rather than standard autocomplete capabilities and static datasets. “What RL does is act as a training gym for AI models,” says Gerry. “They learn based on new data that companies like Bugcrowd provide, thanks to the work of our army of ethical hackers. This teaches them how to find and recognise vulnerabilities, create exploits that validate them and finally how to fix them. It does that using training datasets that we provide, made up of all the data that our products have seen from around the world – proprietary data, not available on the public Internet.”

“RL is key for training LLMs because unlike traditional methods, it learns through trial and error in real time, adapting to new challenges without needing fixed threat patterns, such as you get with labelled datasets,” agrees Rik Turner, chief analyst with independent consulting firm Omdia’s cybersecurity team, with responsibility for covering emerging cybersecurity technology trends. “This makes RL useful in enabling them to detect advanced threats like zero-days.”

To the next level

The role of AI in cyber protection is now moving to a new evolutionary stage with Bugcrowd’s acquisition of Mayhem Security, a pioneer in AI offensive security and a specialist in the next generation of human-led, AI-powered security testing.
The combined operation will take Reinforcement Learning in new directions, claims Dr David Brumley, Chief AI & Science Officer: “One of the big problems of scanning for security risks has been false positives,” he explains. “The latest research suggests that something like 50% of the findings of a typical scan are not actual threats. You don’t want to be training an AI where 50% of the things you’re training it on are fake. You’re effectively training it on hallucinations.”
RL has the power, he claims, to effectively eliminate false positives, hugely helping with the training of foundational models, and doing so at scale: “We’ve delivered hundreds of 1000s of environments to some of these model companies,” he says. “There are very few players out in the market today that are able to approach this at that scale, using real data, not something synthetic.”

Mayhem Security has, he says, been using RL for a while to train agents to carry out actions and solve problems. Now the combined power of Mayhem and Bugcrowd will enable AI-driven offensive cyber testing in new ways, such as helping to analyse applications and attack surfaces more effectively, build automated test plans, learn from attacker behaviour at scale, and teach AI models how to find real vulnerabilities, all adding to the efforts of human testers.

Keeping the human angle is key to the mission. Bringing together Mayhem’s database of hundreds of thousands of vulnerabilities with the Bugcrowd database will provide even richer training data, working like an AI agent to help do the same things that a human security agent can do.

“Customers will, longer term, get more and more interested in using LLMs for security testing,” predicts Brumley. “It will never replace humans, but it will be able to do a lot of the work, especially the low hanging fruit. This will free humans to focus on the very complex vulnerabilities that AI can’t handle yet.”

Mayhem brings the AI talent to help Bugcrowd grow its existing and future AI efforts, says Gerry: “They have the knowhow to train AI agents, feed in Retrieval-Augmented Generation (RAG) data, create automations, and help us to ‘shift left’ earlier into the planning, design, and coding phases of the software development lifecycle.”
It’s not just Bugcrowd and Mayhem making waves here. In the market for security testing solutions, AI looks set to be a game changer with other testing providers poised to move in the direction of more tightly focused AI training as well.

“There is virtually no area of tech that is completely impervious to the application of AI, and threat detection is no exception,” believes Omdia’s Turner. Like Brumley, he doesn’t think that AI agents are going to replace or do away with the human threat researchers just yet: ”I suspect there will always be a need for the human in the loop. Indeed, given that something like 75% of all alerts are actually false positives, the first task for AI agents will be to separate the wheat from the chaff in the alert tsunami coming at human SecOps teams.”

Turner believes that it is important for CISOs to assertively make the case for continued human involvement: “They should avoid the idea that you can just automate threat detection altogether and do away with pesky humans that need to sleep and take the occasional vacation. In that context, I’d say it’s a smart move for Bugcrowd to team up with Mayhem and get ahead of that trend for a human-led approach.”

Data quality is key

Security testing is an essential process, a vital part of identifying vulnerabilities in an organisation’s software and networks. AI is an increasingly important weapon in the world of testing. But if AI is to become a true game changer, then clearly it is only going to be as good as the data on which it is trained. The more dynamic and rich the data, the better the AI model will be. Reinforcement Learning is the secret sauce to drive AI to the next level. Without RL environments, security professionals are limited by AI that consumes the same data that is available to everybody. In the security world, that is not enough. That’s why RL is a certain growth area in the field of training AI agents to identify risks.

By Guy Matthews, Editor, NetEvents

The post Time to call up AI reinforcements in the cyber wars appeared first on NetEvents.

Is your organization in the right place to take advantage of AI? How well positioned are you to reap its ability to deliver greater productivity, improved efficiency and better business outcomes?

If you thought that you were fairly well set, here’s a sobering statistic for you: A report from MIT, published late last year, reveals that around 95% of AI initiatives don’t deliver any measurable returns, with most getting cancelled before they can get out of pilot stage and into production.

The truth is that enterprise AI sits at a difficult crossroads. It has moved decisively beyond experimentation, and is starting to pop up in isolated use cases across the enterprise. But it has yet to break through to the core, and deliver truly autonomous decision making, end-to-end automation and real time intelligence in a mission critical setting. Adoption is widespread, but true enterprise-wide scale is rare. Proof of concept is a million miles from sustained, production-grade deployment.

The network is everything

If you are not as ready to take advantage of AI as you first thought, then that probably isn’t down to a lack of ambition or failure to invest in the right AI tools. It’s far more likely to be about your ongoing reliance on network architectures that were not designed for high-bandwidth, low-latency, policy-driven AI workloads.

Let’s stop to consider a handful of the connectivity challenges that must be resolved before AI can thrive at scale:

• The network must be able to cope with AI workloads that are distributed across multiple clouds, geographies, and edge locations
• Your network must be ready for the kind of unpredictable performance and uncertain costs that will be the inevitable result of greater autonomy
• A network won’t be able to manage AI workloads well if it only gives you fragmented visibility and throws up security blind spots
• An older network will make trouble for you when it comes to scaling AI securely and cost effectively, let alone with the right kind of governance and auditability to keep regulators happy

All of this means that the priority for CIOs must be to re-architect their digital foundation for AI, rather than layering AI on top of legacy infrastructure. AI systems introduce continuous, machine-driven workloads that put new demands on connectivity, latency, resilience and data movement. But you can’t reverse-engineer AI readiness into systems that were designed for a different world. AI is already piling unprecedented pressure on to digital foundations that were not designed for it, and that isn’t going to get better on its own.

Don’t let your network let you down

It is vital not to allow your network, designed as it was for elastic, human-paced applications, to become the limiting factor in your AI transformation. You need a better solution that eliminates infrastructure silos across network, cloud, edge and security end points. You need a unified, AI-optimised digital backbone that enables AI workloads to move seamlessly.

Before they can call themselves truly ‘AI ready’, CIOs need a connectivity solution that removes infrastructure complexity and lets them focus on AI-led innovation, faster time to value and measurable business outcomes. There can be no successful AI transformation without it. Only a foundation like this can determine whether AI delivers ROI or a world of trouble, risk and disappointment.

NetEvents will be hosting their CIO Forum roundtables and workshops titled “Unleashing the Power of AI within the Enterprise” on Friday, April 24th, 2026. These events are “by invitation only” and enable CIOs to share their challenges and experiences with industry peers, plus engage in expert-led discussions. At this event experts include London School of Economics, industry analysts Omdia and leading technologists, covering all the issues above. If you are a C-Level executive and would like to find out more about future CIO Forum roundtables, workshops and summits, please contact mfox@netevents.org

The post How ready are you for AI’s transformative power? appeared first on NetEvents.

AI is rewriting the rules around networking and security. Data-intensive AI workloads are placing significant demands on network infrastructure. At the same time, AI is becoming both a weapon for threat actors and a potential target for attacks. New approaches and new solutions are needed if enterprises are to prosper in the AI era.

The impact of AI cannot be overestimated. It is acting like a new kind of gravity, pulling together the previously distinct worlds of networking and security, while reshaping traffic flows across every network layer. We have become accustomed to the changes that AI is bringing about within the data center, and the overheads it is placing on data center infrastructure. But it is now apparent that its influence is being felt much more widely, reshaping requirements across the entire ICT landscape, from the WAN down to the campus.

AI is turning security on its head, believes Mauricio Sanchez, Senior Director of Enterprise Security and Networking Research with independent analyst firm Dell’Oro Group: “AI has been transforming the hacker community, enabling even mediocre players to become super hackers,” he believes. “The positive aspects of AI are being turned on their head and used for evil.”

Add in the perennial demand of enterprises for five nines of uptime, and a cogent argument is emerging for a convergence of networking and security: “Network operations and security operations can simply no longer exist in parallel silos,” notes Sanchez.

Enterprises are all at different stages on this journey, working out how to unite AI with converged networking and security at their own pace. Some are simply applying AI to legacy network and security operations. “They are finding, as they try to move on from this point, that they are getting into what I would call AI-strained infrastructure,” says Sanchez. “This is where administrators are sweating bullets, not really empowered or able to react appropriately.”

Some progressive companies are in a better place than this when it comes to embracing AI and bringing it to bear on both networking and security, according to Dell’Oro’s findings (See Figure 1), moving towards what Sanchez describes as the AI augmented-network stage. “Beyond that is a level where both infrastructural change and operational change lead to an AI-empowered network that is being run cleanly and efficiently, able to use AI and serve AI applications well,” he concludes. “That is the objective over the next five years for many enterprises.”

New challenges, new solutions

To further explore these themes, Sanchez was invited by Unified SASE as a Service provider Aryaka to join a ‘fireside chat’. Along with Sanchez was Renuka Nadkarni, Chief Product Officer, Aryaka and Kevin Deierling SVP Marketing, Networking, NVIDIA.

All three agree that AI is changing cybersecurity in many different ways. “AI is using a ton of data, and that creates some opacity,” observes NVIDIA’s Deierling. “It’s hard to see what’s happening when AIs are talking to other Ais, and that creates new challenges.”

Agility and flexibility are essential responses: “The amount of data that’s being created by AI is massive, and the networking performance needed is incredible,” he says. “We’re shipping 400 gigabit per second networks today, moving to 800 gigabits per second, with 1.6 terabits right around the corner. You can’t just statically create a set of rules and hope for the best. It’s about being dynamic and responsive in the face of all these new challenges.”

Nadkarni of Aryaka believes it all comes down to an age old problem, that of reconciling performance with security: “Back in the day, you had separate networking and security teams making separate decisions,” she points out. “The security people were often getting in the way of the business, with frequent conflict between the two. Now our customers are migrating heavily towards a converged networking and security play. And it’s not easy, because the whole industry has been divided into networking vendors and security vendors. The whole unified SASE as a service that we are trying to bring to the table was architected from the start to bring things together.”

An added challenge, according to Nadkarni, is that AI introduces a certain amount of undeterministic behaviour, both on the networking and the security side: “Customer network architecture and network design used to be about a point to point link,” she says. “It was deterministic, because people would typically buy from service providers in increments of 10 Mbps or 100 Mbps, defined as between offices and data centers. But now users are everywhere. Applications are hosted in public clouds, accessed via SaaS. We’re seeing a lot of AI applications coming in as SaaS. Traffic patterns have changed drastically, but the need for security is something that hasn’t changed.”

AI has certainly spelled the end for the static workloads of yesterday where it was easy for networking and security  managers to keep tabs on what is happening. In an era of agentic AI workflows, where AIs are talking to Ais, and then interacting with humans, the pace has picked up and complexity can be overwhelming, often at the expense of security.

Deierling says that to assist here, NVIDIA has developed a platform called Morpheus that characterizes behaviours: “It characterizes devices as well as people, and we developed a digital fingerprint using AI to do that,” he explains. “We stream the data in real time to these powerful AI engines that can detect anomalous behaviours. If suddenly a human being is firing passwords at the speed of a computer, we can detect that in real time and actually isolate that traffic. We accelerate things with our networking hardware, and we stream telemetry data so we can perform AI very quickly. And then we provide those solutions to partners to build something that customers can use.”

Nadkarni says Morpheus works well with Aryaka’s platform: “In most security implementations, you take a subset of your traffic and share it with a security vendor,” she says. “But if you already knew what traffic needed to be processed you wouldn’t need to do that. Because of Morpheus, we have the ability to process all traffic through our system, and we don’t have to make choices.”

She invites a comparison between the fast-evolving AI we see today and the recent emergence of DevSecOps: “It touches so many aspects of a customer’s activities. Many of our customers are telling us they are creating an AI adoption team. We advise them to break down the problem into smaller pieces. Identify all the stakeholders who are accountable for it. For example, who owns the data? Security of data is really important when it comes to AI.”

AI is here, it’s massive and it’s going to transform every industry, concludes Deierling: “Every enterprise should focus on their core expertise, and use AI to accelerate that,” he advises. “AI is fast, and it uses huge amounts of data. It’s a different type of challenge than what we’ve seen, but I agree with Renuka that it’s an evolution of DevSecOps. Call it AISecOps. You need to protect models, you need to protect data, you need to protect users.”

Nadkarni believes we are in ‘very exciting times’: “We’ve already seen adoption of cloud, and of different SaaS applications,” she says. “AI will have a bigger impact than that. But as Kevin was saying, enterprises should focus on the most important things for their business and then leverage the latest AI technologies that are available, as well as make sure that their network is modernized. It’s the industry’s job to focus on providing the best technology, offering the best solutions, making it easier to adopt AI and go through the changes that are coming. It’s a privilege to be around at this time, seeing all the benefits of this new technology as they unfold.”

Sanchez from Dell’Oro Group concludes with the advice that all enterprises go back to basics, focussing on things like visibility: “In order to do that, they need to have the right infrastructure in place, the right foundational elements, because you can’t build a strong house on weak foundations,” he claims. “Don’t try to figure this out for yourself. There are smart people, from companies like NVIDIA and Aryaka, that can help on this journey to make sure that that you don’t stumble.”

By Guy Matthews, Editor, NetEvents

The post How AI is driving the convergence of networking and security appeared first on NetEvents.