Verizon is offering a FREE Google Pixel 10 Pro when you open a new line, no trade-in required
That's $1,000 in value, compared to buying the phone new.
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Verizon is offering a FREE Google Pixel 10 Pro when you open a new line, no trade-in required
That's $1,000 in value, compared to buying the phone new.
Verizon is offering a FREE Google Pixel 10 Pro when you open a new line, no trade-in required
That's $1,000 in value, compared to buying the phone new.
Verizon is offering a FREE Google Pixel 10 Pro when you open a new line, no trade-in required
That's $1,000 in value, compared to buying the phone new.
Cisco warns enterprises: Without tapping machine data, your AI strategy is incomplete
Cisco executives make the case that the distinction between product and model companies is disappearing, and that accessing the 55% of enterprise data growth that current AI ignores will separate winners from losers. VentureBeat recently caught up with Jeetu Patel, Cisco's President and Chief Product Officer and DJ Sampath, Senior Vice President of AI Software and Platform, to gain new insights into a compelling thesis both leaders share. They and their teams contend that every successful product company must become an AI model company to survive the next decade. When one considers how compressed product lifecycles are becoming, combined with the many advantages of digital twin technology to accelerate time-to-market of next-gen products, the thesis makes sense. The conversation revealed why this transformation is inevitable, backed by solid data points. The team contends that 55% of all data growth is machine data that current AI models don't touch. OpenAI's Greg Brockman estimates we need 10 billion GPUs to give every human the AI agents they'll need, and Cisco's open source security model, Foundation-Sec-8B, has already seen 200,000 downloads on Hugging Face. Why the model is becoming the product VentureBeat: You've stated that in the future, every product company will become a model company. Why is this inevitable rather than just one possible path? Jeetu Patel: In the future, there's no distinction between model companies and product companies. Great product companies will be model companies. The close tie-in between model and product is a closed loop. To enhance the product, you enhance the model, not just a UI shim. These companies being formed right now that are a thin shim on top of a model; their days are numbered. The true moat is the model you build that drives product behavior. This requires being simultaneously good at two things: building great models in domains where you have great data, and building great product experiences powered by those models in an iterative loop where the models adapt and evolve when you have product enhancement requests. DJ Sampath: This becomes even more critical when you think about things moving to agents. Agents are going to be governed by these models. Your moat is really going to be how well your model reacts to the changes it needs to. Harnessing machine data's growth is key VentureBeat: You mentioned that 55% of data growth is machine data, yet current models aren't trained on it. Why does this represent such a massive opportunity? Patel: So far, models have been very good at being trained on publicly available, human-generated data freely available on the internet. But we're done with the amount of public data you could crawl. Where else do you go next? It's all locked up inside enterprises. 55% of data growth is machine data, but models are not trained on machine data. Every company says 'my data is my moat,' but most don't have an effective way to condition that data into an organized pipeline so they can train AI with it and harness its full potential. Imagine how much log data will be generated when agents work 24/7 and every human has 100 agents. Greg Brockman from OpenAI said if you assume every human has a GPU, you're three orders of magnitude away from where you need to be; you need 10 billion GPUs. When you think that way, if you don't train your models with machine data effectively, you're incomplete in your ability to harness the full potential of AI. Sampath: Most of the models are being trained on public data. The data that's inside enterprises is mostly machine data. We're unlocking that machine data. We give each enterprise a starting model. Think of it as a starter kit. They'll take that model and build applications and agents fine-tuned on their proprietary data inside their enterprises. We're going to be a model company, but we're also going to make it incredibly easy for every single enterprise to build their own models using the infrastructure we provide. Why hardware companies have an advantage VentureBeat: Many see hardware as a liability in the software and AI era. You argue the opposite. Why? Patel: A lot of people look down on hardware. I actually think hardware is a great asset to have, because if you know how to build great hardware and great software and great AI models and tie them all together, that's when magic starts to happen. Think about what we can do by correlating machine data from logs with our time series model. If there's a one-degree change in your switch or router, you might predict system failure in three days, something you couldn't correlate before. You identify the change, reroute traffic to prevent problems, and solve the issue. Get much more predictive in outages and infrastructure stability. Cisco is the critical infrastructure company for AI. This completely changes the level of stability we can generate for our infrastructure. Manufacturing is one of the top industries for the data volume generated daily. Combined with agentic AI and accumulated metadata, it completely changes the competitive nature of manufacturing or asset-intensive industries. With enough data, they can transcend disruptions around tariffs or supply chain variations, getting them out of price and availability commoditization. Cisco's deep commitment to Open Source VentureBeat: Why make your security models open source when that seems to give away competitive advantage? Sampath: The cat is out of the bag; attackers also have access to open source models. The next step is equipping as many defenders as possible with models that make defense stronger. That's really what we did at RSAC 2025 when we launched our open source model, Foundation-Sec-8B . Funding for open source initiatives has stalled. There's an increased drain in the open source community, needing sustainable, collaborative funding sources. It's a corporate responsibility to make these models available, plus it provides access to communities to start working with AI from a defense perspective. We've integrated ClamAV , a widely used open source antivirus tool, with Hugging Face , which hosts over 2 million models. Every single model gets scanned for malware. You have to ensure the AI supply chain is appropriately protected, and we're at the forefront of doing that. Patel: We launched not just the security model that's open source, but also one on Splunk for time series data. These correlate data; time series and security incident data, to be able to find very interesting outcomes. With 200,000 downloads on Hugging Face, we're seeing resellers starting to build applications with it. Taking the customers' pulse after Cisco Live VentureBeat: Following Cisco Live's product launches, how are customers responding? Patel: There are three categories. First, completely ecstatic customers: 'We've been asking for this for a while. Hallelujah.' Second, those saying 'I'm going to try this out.' DJ shows them a demo with white glove treatment, they do a POC, and they're dumbfounded that it's even better than what we said in three minutes on stage. Third are skeptics who verify that every announcement comes out on the exact days. That group used to be much bigger three years ago. As it's shrunk, we've seen meaningful improvements in our financial results and how the market sees us. We don't talk about things three years out, only within a six-month window. The payload is so large that we have enough to discuss for six months. Our biggest challenge, frankly, is keeping our customers up to date with the velocity of innovation we have. Obsessing over customers, not hardware VentureBeat: How are you migrating your hardware-centric installed base without creating too much disruption? Patel: Rather than fixating on 'hardware versus software,' you start from where the customer is. Your strategy can no longer be a perimeter-based firewall for network security because the market has moved. It's hyper-distributed. But you currently have firewalls that need efficient management. We're giving you a fully refreshed firewall lineup. If you want to look at what we've done with public cloud, managing egress traffic with Multicloud Defense with zero trust, not just user-to-application, but application-to-application. We've built Hypershield technology . We've built a revolutionary Smart Switch. All managed by the same Security Cloud Control with AI Canvas on top. We tell our customers they can go at their own pace. Start with firewalls, move to Multicloud Defense, add Hypershield enforcement points with Cilium for observability, and add Smart Switches. You don't have to add more complexity because we have a true platform advantage with Security Cloud Control. Rather than saying 'forget everything and move to the new thing', creating too much cognitive load, we start where the customer is and take them through the journey. What's next: energizing global partners to turn AI into a revenue opportunity The interview concluded with discussions of November's Partner Summit in San Diego, where Cisco plans significant partner activation announcements. As Patel noted, "Sustained, consistent emphasis is needed to get the entire reseller engine moving." VentureBeat is convinced that a globally strong partner organization is indispensable for any cybersecurity company to attain its long-term AI vision.
VentureBeat
Cisco warns enterprises: Without tapping machine data, your AI strategy is incomplete
Cisco executives make the case that the distinction between product and model companies is disappearing, and that accessing the 55% of enterprise data growth that current AI ignores will separate winners from losers. VentureBeat recently caught up with Jeetu Patel, Cisco's President and Chief Product Officer and DJ Sampath, Senior Vice President of AI Software and Platform, to gain new insights into a compelling thesis both leaders share. They and their teams contend that every successful product company must become an AI model company to survive the next decade. When one considers how compressed product lifecycles are becoming, combined with the many advantages of digital twin technology to accelerate time-to-market of next-gen products, the thesis makes sense. The conversation revealed why this transformation is inevitable, backed by solid data points. The team contends that 55% of all data growth is machine data that current AI models don't touch. OpenAI's Greg Brockman estimates we need 10 billion GPUs to give every human the AI agents they'll need, and Cisco's open source security model, Foundation-Sec-8B, has already seen 200,000 downloads on Hugging Face. Why the model is becoming the product VentureBeat: You've stated that in the future, every product company will become a model company. Why is this inevitable rather than just one possible path? Jeetu Patel: In the future, there's no distinction between model companies and product companies. Great product companies will be model companies. The close tie-in between model and product is a closed loop. To enhance the product, you enhance the model, not just a UI shim. These companies being formed right now that are a thin shim on top of a model; their days are numbered. The true moat is the model you build that drives product behavior. This requires being simultaneously good at two things: building great models in domains where you have great data, and building great product experiences powered by those models in an iterative loop where the models adapt and evolve when you have product enhancement requests. DJ Sampath: This becomes even more critical when you think about things moving to agents. Agents are going to be governed by these models. Your moat is really going to be how well your model reacts to the changes it needs to. Harnessing machine data's growth is key VentureBeat: You mentioned that 55% of data growth is machine data, yet current models aren't trained on it. Why does this represent such a massive opportunity? Patel: So far, models have been very good at being trained on publicly available, human-generated data freely available on the internet. But we're done with the amount of public data you could crawl. Where else do you go next? It's all locked up inside enterprises. 55% of data growth is machine data, but models are not trained on machine data. Every company says 'my data is my moat,' but most don't have an effective way to condition that data into an organized pipeline so they can train AI with it and harness its full potential. Imagine how much log data will be generated when agents work 24/7 and every human has 100 agents. Greg Brockman from OpenAI said if you assume every human has a GPU, you're three orders of magnitude away from where you need to be; you need 10 billion GPUs. When you think that way, if you don't train your models with machine data effectively, you're incomplete in your ability to harness the full potential of AI. Sampath: Most of the models are being trained on public data. The data that's inside enterprises is mostly machine data. We're unlocking that machine data. We give each enterprise a starting model. Think of it as a starter kit. They'll take that model and build applications and agents fine-tuned on their proprietary data inside their enterprises. We're going to be a model company, but we're also going to make it incredibly easy for every single enterprise to build their own models using the infrastructure we provide. Why hardware companies have an advantage VentureBeat: Many see hardware as a liability in the software and AI era. You argue the opposite. Why? Patel: A lot of people look down on hardware. I actually think hardware is a great asset to have, because if you know how to build great hardware and great software and great AI models and tie them all together, that's when magic starts to happen. Think about what we can do by correlating machine data from logs with our time series model. If there's a one-degree change in your switch or router, you might predict system failure in three days, something you couldn't correlate before. You identify the change, reroute traffic to prevent problems, and solve the issue. Get much more predictive in outages and infrastructure stability. Cisco is the critical infrastructure company for AI. This completely changes the level of stability we can generate for our infrastructure. Manufacturing is one of the top industries for the data volume generated daily. Combined with agentic AI and accumulated metadata, it completely changes the competitive nature of manufacturing or asset-intensive industries. With enough data, they can transcend disruptions around tariffs or supply chain variations, getting them out of price and availability commoditization. Cisco's deep commitment to Open Source VentureBeat: Why make your security models open source when that seems to give away competitive advantage? Sampath: The cat is out of the bag; attackers also have access to open source models. The next step is equipping as many defenders as possible with models that make defense stronger. That's really what we did at RSAC 2025 when we launched our open source model, Foundation-Sec-8B . Funding for open source initiatives has stalled. There's an increased drain in the open source community, needing sustainable, collaborative funding sources. It's a corporate responsibility to make these models available, plus it provides access to communities to start working with AI from a defense perspective. We've integrated ClamAV , a widely used open source antivirus tool, with Hugging Face , which hosts over 2 million models. Every single model gets scanned for malware. You have to ensure the AI supply chain is appropriately protected, and we're at the forefront of doing that. Patel: We launched not just the security model that's open source, but also one on Splunk for time series data. These correlate data; time series and security incident data, to be able to find very interesting outcomes. With 200,000 downloads on Hugging Face, we're seeing resellers starting to build applications with it. Taking the customers' pulse after Cisco Live VentureBeat: Following Cisco Live's product launches, how are customers responding? Patel: There are three categories. First, completely ecstatic customers: 'We've been asking for this for a while. Hallelujah.' Second, those saying 'I'm going to try this out.' DJ shows them a demo with white glove treatment, they do a POC, and they're dumbfounded that it's even better than what we said in three minutes on stage. Third are skeptics who verify that every announcement comes out on the exact days. That group used to be much bigger three years ago. As it's shrunk, we've seen meaningful improvements in our financial results and how the market sees us. We don't talk about things three years out, only within a six-month window. The payload is so large that we have enough to discuss for six months. Our biggest challenge, frankly, is keeping our customers up to date with the velocity of innovation we have. Obsessing over customers, not hardware VentureBeat: How are you migrating your hardware-centric installed base without creating too much disruption? Patel: Rather than fixating on 'hardware versus software,' you start from where the customer is. Your strategy can no longer be a perimeter-based firewall for network security because the market has moved. It's hyper-distributed. But you currently have firewalls that need efficient management. We're giving you a fully refreshed firewall lineup. If you want to look at what we've done with public cloud, managing egress traffic with Multicloud Defense with zero trust, not just user-to-application, but application-to-application. We've built Hypershield technology . We've built a revolutionary Smart Switch. All managed by the same Security Cloud Control with AI Canvas on top. We tell our customers they can go at their own pace. Start with firewalls, move to Multicloud Defense, add Hypershield enforcement points with Cilium for observability, and add Smart Switches. You don't have to add more complexity because we have a true platform advantage with Security Cloud Control. Rather than saying 'forget everything and move to the new thing', creating too much cognitive load, we start where the customer is and take them through the journey. What's next: energizing global partners to turn AI into a revenue opportunity The interview concluded with discussions of November's Partner Summit in San Diego, where Cisco plans significant partner activation announcements. As Patel noted, "Sustained, consistent emphasis is needed to get the entire reseller engine moving." VentureBeat is convinced that a globally strong partner organization is indispensable for any cybersecurity company to attain its long-term AI vision.
Cisco warns enterprises: Without tapping machine data, your AI strategy is incomplete
Cisco executives make the case that the distinction between product and model companies is disappearing, and that accessing the 55% of enterprise data growth that current AI ignores will separate winners from losers. VentureBeat recently caught up with Jeetu Patel, Cisco's President and Chief Product Officer and DJ Sampath, Senior Vice President of AI Software and Platform, to gain new insights into a compelling thesis both leaders share. They and their teams contend that every successful product company must become an AI model company to survive the next decade. When one considers how compressed product lifecycles are becoming, combined with the many advantages of digital twin technology to accelerate time-to-market of next-gen products, the thesis makes sense. The conversation revealed why this transformation is inevitable, backed by solid data points. The team contends that 55% of all data growth is machine data that current AI models don't touch. OpenAI's Greg Brockman estimates we need 10 billion GPUs to give every human the AI agents they'll need, and Cisco's open source security model, Foundation-Sec-8B, has already seen 200,000 downloads on Hugging Face. Why the model is becoming the product VentureBeat: You've stated that in the future, every product company will become a model company. Why is this inevitable rather than just one possible path? Jeetu Patel: In the future, there's no distinction between model companies and product companies. Great product companies will be model companies. The close tie-in between model and product is a closed loop. To enhance the product, you enhance the model, not just a UI shim. These companies being formed right now that are a thin shim on top of a model; their days are numbered. The true moat is the model you build that drives product behavior. This requires being simultaneously good at two things: building great models in domains where you have great data, and building great product experiences powered by those models in an iterative loop where the models adapt and evolve when you have product enhancement requests. DJ Sampath: This becomes even more critical when you think about things moving to agents. Agents are going to be governed by these models. Your moat is really going to be how well your model reacts to the changes it needs to. Harnessing machine data's growth is key VentureBeat: You mentioned that 55% of data growth is machine data, yet current models aren't trained on it. Why does this represent such a massive opportunity? Patel: So far, models have been very good at being trained on publicly available, human-generated data freely available on the internet. But we're done with the amount of public data you could crawl. Where else do you go next? It's all locked up inside enterprises. 55% of data growth is machine data, but models are not trained on machine data. Every company says 'my data is my moat,' but most don't have an effective way to condition that data into an organized pipeline so they can train AI with it and harness its full potential. Imagine how much log data will be generated when agents work 24/7 and every human has 100 agents. Greg Brockman from OpenAI said if you assume every human has a GPU, you're three orders of magnitude away from where you need to be; you need 10 billion GPUs. When you think that way, if you don't train your models with machine data effectively, you're incomplete in your ability to harness the full potential of AI. Sampath: Most of the models are being trained on public data. The data that's inside enterprises is mostly machine data. We're unlocking that machine data. We give each enterprise a starting model. Think of it as a starter kit. They'll take that model and build applications and agents fine-tuned on their proprietary data inside their enterprises. We're going to be a model company, but we're also going to make it incredibly easy for every single enterprise to build their own models using the infrastructure we provide. Why hardware companies have an advantage VentureBeat: Many see hardware as a liability in the software and AI era. You argue the opposite. Why? Patel: A lot of people look down on hardware. I actually think hardware is a great asset to have, because if you know how to build great hardware and great software and great AI models and tie them all together, that's when magic starts to happen. Think about what we can do by correlating machine data from logs with our time series model. If there's a one-degree change in your switch or router, you might predict system failure in three days, something you couldn't correlate before. You identify the change, reroute traffic to prevent problems, and solve the issue. Get much more predictive in outages and infrastructure stability. Cisco is the critical infrastructure company for AI. This completely changes the level of stability we can generate for our infrastructure. Manufacturing is one of the top industries for the data volume generated daily. Combined with agentic AI and accumulated metadata, it completely changes the competitive nature of manufacturing or asset-intensive industries. With enough data, they can transcend disruptions around tariffs or supply chain variations, getting them out of price and availability commoditization. Cisco's deep commitment to Open Source VentureBeat: Why make your security models open source when that seems to give away competitive advantage? Sampath: The cat is out of the bag; attackers also have access to open source models. The next step is equipping as many defenders as possible with models that make defense stronger. That's really what we did at RSAC 2025 when we launched our open source model, Foundation-Sec-8B . Funding for open source initiatives has stalled. There's an increased drain in the open source community, needing sustainable, collaborative funding sources. It's a corporate responsibility to make these models available, plus it provides access to communities to start working with AI from a defense perspective. We've integrated ClamAV , a widely used open source antivirus tool, with Hugging Face , which hosts over 2 million models. Every single model gets scanned for malware. You have to ensure the AI supply chain is appropriately protected, and we're at the forefront of doing that. Patel: We launched not just the security model that's open source, but also one on Splunk for time series data. These correlate data; time series and security incident data, to be able to find very interesting outcomes. With 200,000 downloads on Hugging Face, we're seeing resellers starting to build applications with it. Taking the customers' pulse after Cisco Live VentureBeat: Following Cisco Live's product launches, how are customers responding? Patel: There are three categories. First, completely ecstatic customers: 'We've been asking for this for a while. Hallelujah.' Second, those saying 'I'm going to try this out.' DJ shows them a demo with white glove treatment, they do a POC, and they're dumbfounded that it's even better than what we said in three minutes on stage. Third are skeptics who verify that every announcement comes out on the exact days. That group used to be much bigger three years ago. As it's shrunk, we've seen meaningful improvements in our financial results and how the market sees us. We don't talk about things three years out, only within a six-month window. The payload is so large that we have enough to discuss for six months. Our biggest challenge, frankly, is keeping our customers up to date with the velocity of innovation we have. Obsessing over customers, not hardware VentureBeat: How are you migrating your hardware-centric installed base without creating too much disruption? Patel: Rather than fixating on 'hardware versus software,' you start from where the customer is. Your strategy can no longer be a perimeter-based firewall for network security because the market has moved. It's hyper-distributed. But you currently have firewalls that need efficient management. We're giving you a fully refreshed firewall lineup. If you want to look at what we've done with public cloud, managing egress traffic with Multicloud Defense with zero trust, not just user-to-application, but application-to-application. We've built Hypershield technology . We've built a revolutionary Smart Switch. All managed by the same Security Cloud Control with AI Canvas on top. We tell our customers they can go at their own pace. Start with firewalls, move to Multicloud Defense, add Hypershield enforcement points with Cilium for observability, and add Smart Switches. You don't have to add more complexity because we have a true platform advantage with Security Cloud Control. Rather than saying 'forget everything and move to the new thing', creating too much cognitive load, we start where the customer is and take them through the journey. What's next: energizing global partners to turn AI into a revenue opportunity The interview concluded with discussions of November's Partner Summit in San Diego, where Cisco plans significant partner activation announcements. As Patel noted, "Sustained, consistent emphasis is needed to get the entire reseller engine moving." VentureBeat is convinced that a globally strong partner organization is indispensable for any cybersecurity company to attain its long-term AI vision.
Cisco warns enterprises: Without tapping machine data, your AI strategy is incomplete
Cisco executives make the case that the distinction between product and model companies is disappearing, and that accessing the 55% of enterprise data growth that current AI ignores will separate winners from losers. VentureBeat recently caught up with Jeetu Patel, Cisco's President and Chief Product Officer and DJ Sampath, Senior Vice President of AI Software and Platform, to gain new insights into a compelling thesis both leaders share. They and their teams contend that every successful product company must become an AI model company to survive the next decade. When one considers how compressed product lifecycles are becoming, combined with the many advantages of digital twin technology to accelerate time-to-market of next-gen products, the thesis makes sense. The conversation revealed why this transformation is inevitable, backed by solid data points. The team contends that 55% of all data growth is machine data that current AI models don't touch. OpenAI's Greg Brockman estimates we need 10 billion GPUs to give every human the AI agents they'll need, and Cisco's open source security model, Foundation-Sec-8B, has already seen 200,000 downloads on Hugging Face. Why the model is becoming the product VentureBeat: You've stated that in the future, every product company will become a model company. Why is this inevitable rather than just one possible path? Jeetu Patel: In the future, there's no distinction between model companies and product companies. Great product companies will be model companies. The close tie-in between model and product is a closed loop. To enhance the product, you enhance the model, not just a UI shim. These companies being formed right now that are a thin shim on top of a model; their days are numbered. The true moat is the model you build that drives product behavior. This requires being simultaneously good at two things: building great models in domains where you have great data, and building great product experiences powered by those models in an iterative loop where the models adapt and evolve when you have product enhancement requests. DJ Sampath: This becomes even more critical when you think about things moving to agents. Agents are going to be governed by these models. Your moat is really going to be how well your model reacts to the changes it needs to. Harnessing machine data's growth is key VentureBeat: You mentioned that 55% of data growth is machine data, yet current models aren't trained on it. Why does this represent such a massive opportunity? Patel: So far, models have been very good at being trained on publicly available, human-generated data freely available on the internet. But we're done with the amount of public data you could crawl. Where else do you go next? It's all locked up inside enterprises. 55% of data growth is machine data, but models are not trained on machine data. Every company says 'my data is my moat,' but most don't have an effective way to condition that data into an organized pipeline so they can train AI with it and harness its full potential. Imagine how much log data will be generated when agents work 24/7 and every human has 100 agents. Greg Brockman from OpenAI said if you assume every human has a GPU, you're three orders of magnitude away from where you need to be; you need 10 billion GPUs. When you think that way, if you don't train your models with machine data effectively, you're incomplete in your ability to harness the full potential of AI. Sampath: Most of the models are being trained on public data. The data that's inside enterprises is mostly machine data. We're unlocking that machine data. We give each enterprise a starting model. Think of it as a starter kit. They'll take that model and build applications and agents fine-tuned on their proprietary data inside their enterprises. We're going to be a model company, but we're also going to make it incredibly easy for every single enterprise to build their own models using the infrastructure we provide. Why hardware companies have an advantage VentureBeat: Many see hardware as a liability in the software and AI era. You argue the opposite. Why? Patel: A lot of people look down on hardware. I actually think hardware is a great asset to have, because if you know how to build great hardware and great software and great AI models and tie them all together, that's when magic starts to happen. Think about what we can do by correlating machine data from logs with our time series model. If there's a one-degree change in your switch or router, you might predict system failure in three days, something you couldn't correlate before. You identify the change, reroute traffic to prevent problems, and solve the issue. Get much more predictive in outages and infrastructure stability. Cisco is the critical infrastructure company for AI. This completely changes the level of stability we can generate for our infrastructure. Manufacturing is one of the top industries for the data volume generated daily. Combined with agentic AI and accumulated metadata, it completely changes the competitive nature of manufacturing or asset-intensive industries. With enough data, they can transcend disruptions around tariffs or supply chain variations, getting them out of price and availability commoditization. Cisco's deep commitment to Open Source VentureBeat: Why make your security models open source when that seems to give away competitive advantage? Sampath: The cat is out of the bag; attackers also have access to open source models. The next step is equipping as many defenders as possible with models that make defense stronger. That's really what we did at RSAC 2025 when we launched our open source model, Foundation-Sec-8B . Funding for open source initiatives has stalled. There's an increased drain in the open source community, needing sustainable, collaborative funding sources. It's a corporate responsibility to make these models available, plus it provides access to communities to start working with AI from a defense perspective. We've integrated ClamAV , a widely used open source antivirus tool, with Hugging Face , which hosts over 2 million models. Every single model gets scanned for malware. You have to ensure the AI supply chain is appropriately protected, and we're at the forefront of doing that. Patel: We launched not just the security model that's open source, but also one on Splunk for time series data. These correlate data; time series and security incident data, to be able to find very interesting outcomes. With 200,000 downloads on Hugging Face, we're seeing resellers starting to build applications with it. Taking the customers' pulse after Cisco Live VentureBeat: Following Cisco Live's product launches, how are customers responding? Patel: There are three categories. First, completely ecstatic customers: 'We've been asking for this for a while. Hallelujah.' Second, those saying 'I'm going to try this out.' DJ shows them a demo with white glove treatment, they do a POC, and they're dumbfounded that it's even better than what we said in three minutes on stage. Third are skeptics who verify that every announcement comes out on the exact days. That group used to be much bigger three years ago. As it's shrunk, we've seen meaningful improvements in our financial results and how the market sees us. We don't talk about things three years out, only within a six-month window. The payload is so large that we have enough to discuss for six months. Our biggest challenge, frankly, is keeping our customers up to date with the velocity of innovation we have. Obsessing over customers, not hardware VentureBeat: How are you migrating your hardware-centric installed base without creating too much disruption? Patel: Rather than fixating on 'hardware versus software,' you start from where the customer is. Your strategy can no longer be a perimeter-based firewall for network security because the market has moved. It's hyper-distributed. But you currently have firewalls that need efficient management. We're giving you a fully refreshed firewall lineup. If you want to look at what we've done with public cloud, managing egress traffic with Multicloud Defense with zero trust, not just user-to-application, but application-to-application. We've built Hypershield technology . We've built a revolutionary Smart Switch. All managed by the same Security Cloud Control with AI Canvas on top. We tell our customers they can go at their own pace. Start with firewalls, move to Multicloud Defense, add Hypershield enforcement points with Cilium for observability, and add Smart Switches. You don't have to add more complexity because we have a true platform advantage with Security Cloud Control. Rather than saying 'forget everything and move to the new thing', creating too much cognitive load, we start where the customer is and take them through the journey. What's next: energizing global partners to turn AI into a revenue opportunity The interview concluded with discussions of November's Partner Summit in San Diego, where Cisco plans significant partner activation announcements. As Patel noted, "Sustained, consistent emphasis is needed to get the entire reseller engine moving." VentureBeat is convinced that a globally strong partner organization is indispensable for any cybersecurity company to attain its long-term AI vision.
I put my gaming PC in the wrong spot, and learned it the hard way
I had a desktop gaming rig built for me about 10 years ago. I chose large and chunky parts, which the tower reflected in its sheer enormity. The size of it meant that it was never going to be something I could place on my desk without it looking out of place. It was heavy too. But the base was skinny, so it wobbled when I moved it. I figured if it fell off the side of my desk it was going to cause some serious damage — if not to me, then my floor. So, under my desk it went to live, and it stayed there next to my legs as I played endless rounds of Day of Defeat . The position worked from a practical point of view, in that I had more space to spread out on my desk. I was glad it wasn’t blowing hot air anywhere near my face, too. Overall, I was happy — I had a solid, reliable workhorse that wasn’t an eyesore or a drop hazard. Fast forward six months, and one day I messed all that up when I moved it to get to my AC outlets. The move caused the four tiny feet on the rig’s underside to fall off, first the two on the back and then the two on the front. I thought that was no big deal. It’s not like I needed them to run my games, so I chose to just leave them off. But without those tiny feet to lift the tower an inch off the floor, my PC had to rest on my fluffy carpet. It was probably a year after that, that I started getting throttling problems and big temperature-rise alerts in my PC stats — but I didn’t know why. Had I pushed my fans too far by overclocking, or was something else to blame? A friend diagnosed the problem after running some diagnostics: It turns out I had turned my rig into a dust bucket with my shoddy careless placement on the carpet, such that for the last year it had been pulling in every spec of microfiber, disintegrated skin cells, pollen, and fur from my American Shorthair that it possibly could. Pexels: Atahan Demir Needless to say, it was now chock-full of the stuff — especially the fans. There was so much fluffy dust in there that I could seriously have opened my own ceiling insulation business; it took more than a whole day’s work to clean it all out. Another thing… my PC was so far backed up against my wall that airflow through the rear vents was almost nonexistent. I’ll round up my story by saying that I now know how important it is to find the right place to place my PC. So, I’ll leave you with a few PC placement dos and don’ts that I now go by to keep them running smoothly. The Dont’s of PC placement Place it on carpet when any vents on the underside are going to be blocked and prevent air circulation and where your rig is going to be susceptible to getting dust inside it. Place it so far back against a wall or other object that air circulation at the back is going to be stifled. Place it too close to heaters, hot lights, or other sources of heat. Cover it with anything like a blanket. The Dos of PC placement Place it on a desk or shelf or on a PC stand. Some users like a desk that’s separate from their own desk so that they can free up space. Placing it on a non-carpeted floor is okay too, preferably with elevation. Clean dust out of your gaming rig at least once a year. Related content 6 big PC hardware myths, exposed How to check your PC’s CPU temperature 7 advanced tools every PC enthusiast needs in their toolkit Clean up cable clutter: These tricks create beautiful cord order
PCWorld
I put my gaming PC in the wrong spot, and learned it the hard way
I had a desktop gaming rig built for me about 10 years ago. I chose large and chunky parts, which the tower reflected in its sheer enormity. The size of it meant that it was never going to be something I could place on my desk without it looking out of place. It was heavy too. But the base was skinny, so it wobbled when I moved it. I figured if it fell off the side of my desk it was going to cause some serious damage — if not to me, then my floor. So, under my desk it went to live, and it stayed there next to my legs as I played endless rounds of Day of Defeat . The position worked from a practical point of view, in that I had more space to spread out on my desk. I was glad it wasn’t blowing hot air anywhere near my face, too. Overall, I was happy — I had a solid, reliable workhorse that wasn’t an eyesore or a drop hazard. Fast forward six months, and one day I messed all that up when I moved it to get to my AC outlets. The move caused the four tiny feet on the rig’s underside to fall off, first the two on the back and then the two on the front. I thought that was no big deal. It’s not like I needed them to run my games, so I chose to just leave them off. But without those tiny feet to lift the tower an inch off the floor, my PC had to rest on my fluffy carpet. It was probably a year after that, that I started getting throttling problems and big temperature-rise alerts in my PC stats — but I didn’t know why. Had I pushed my fans too far by overclocking, or was something else to blame? A friend diagnosed the problem after running some diagnostics: It turns out I had turned my rig into a dust bucket with my shoddy careless placement on the carpet, such that for the last year it had been pulling in every spec of microfiber, disintegrated skin cells, pollen, and fur from my American Shorthair that it possibly could. Pexels: Atahan Demir Needless to say, it was now chock-full of the stuff — especially the fans. There was so much fluffy dust in there that I could seriously have opened my own ceiling insulation business; it took more than a whole day’s work to clean it all out. Another thing… my PC was so far backed up against my wall that airflow through the rear vents was almost nonexistent. I’ll round up my story by saying that I now know how important it is to find the right place to place my PC. So, I’ll leave you with a few PC placement dos and don’ts that I now go by to keep them running smoothly. The Dont’s of PC placement Place it on carpet when any vents on the underside are going to be blocked and prevent air circulation and where your rig is going to be susceptible to getting dust inside it. Place it so far back against a wall or other object that air circulation at the back is going to be stifled. Place it too close to heaters, hot lights, or other sources of heat. Cover it with anything like a blanket. The Dos of PC placement Place it on a desk or shelf or on a PC stand. Some users like a desk that’s separate from their own desk so that they can free up space. Placing it on a non-carpeted floor is okay too, preferably with elevation. Clean dust out of your gaming rig at least once a year. Related content 6 big PC hardware myths, exposed How to check your PC’s CPU temperature 7 advanced tools every PC enthusiast needs in their toolkit Clean up cable clutter: These tricks create beautiful cord order
I put my gaming PC in the wrong spot, and learned it the hard way
I had a desktop gaming rig built for me about 10 years ago. I chose large and chunky parts, which the tower reflected in its sheer enormity. The size of it meant that it was never going to be something I could place on my desk without it looking out of place. It was heavy too. But the base was skinny, so it wobbled when I moved it. I figured if it fell off the side of my desk it was going to cause some serious damage — if not to me, then my floor. So, under my desk it went to live, and it stayed there next to my legs as I played endless rounds of Day of Defeat . The position worked from a practical point of view, in that I had more space to spread out on my desk. I was glad it wasn’t blowing hot air anywhere near my face, too. Overall, I was happy — I had a solid, reliable workhorse that wasn’t an eyesore or a drop hazard. Fast forward six months, and one day I messed all that up when I moved it to get to my AC outlets. The move caused the four tiny feet on the rig’s underside to fall off, first the two on the back and then the two on the front. I thought that was no big deal. It’s not like I needed them to run my games, so I chose to just leave them off. But without those tiny feet to lift the tower an inch off the floor, my PC had to rest on my fluffy carpet. It was probably a year after that, that I started getting throttling problems and big temperature-rise alerts in my PC stats — but I didn’t know why. Had I pushed my fans too far by overclocking, or was something else to blame? A friend diagnosed the problem after running some diagnostics: It turns out I had turned my rig into a dust bucket with my shoddy careless placement on the carpet, such that for the last year it had been pulling in every spec of microfiber, disintegrated skin cells, pollen, and fur from my American Shorthair that it possibly could. Pexels: Atahan Demir Needless to say, it was now chock-full of the stuff — especially the fans. There was so much fluffy dust in there that I could seriously have opened my own ceiling insulation business; it took more than a whole day’s work to clean it all out. Another thing… my PC was so far backed up against my wall that airflow through the rear vents was almost nonexistent. I’ll round up my story by saying that I now know how important it is to find the right place to place my PC. So, I’ll leave you with a few PC placement dos and don’ts that I now go by to keep them running smoothly. The Dont’s of PC placement Place it on carpet when any vents on the underside are going to be blocked and prevent air circulation and where your rig is going to be susceptible to getting dust inside it. Place it so far back against a wall or other object that air circulation at the back is going to be stifled. Place it too close to heaters, hot lights, or other sources of heat. Cover it with anything like a blanket. The Dos of PC placement Place it on a desk or shelf or on a PC stand. Some users like a desk that’s separate from their own desk so that they can free up space. Placing it on a non-carpeted floor is okay too, preferably with elevation. Clean dust out of your gaming rig at least once a year. Related content 6 big PC hardware myths, exposed How to check your PC’s CPU temperature 7 advanced tools every PC enthusiast needs in their toolkit Clean up cable clutter: These tricks create beautiful cord order
This Is the Legit Way to Get a Free iPhone 17 and Apple’s Fine With It
T-Mobile will hook you up with a free iPhone 17 or 17 Pro when you trade in your old phone and qualify.
Gizmodo
This Is the Legit Way to Get a Free iPhone 17 and Apple’s Fine With It
T-Mobile will hook you up with a free iPhone 17 or 17 Pro when you trade in your old phone and qualify.
The 10 Scariest Stephen King Adaptations, Ranked
Ahead of 'It: Welcome to Derry' arriving on HBO, we’re counting down our favorite Stephen King nightmares from screens big and small.
The 10 Scariest Stephen King Adaptations, Ranked
Ahead of 'It: Welcome to Derry' arriving on HBO, we’re counting down our favorite Stephen King nightmares from screens big and small.
The 10 Scariest Stephen King Adaptations, Ranked
Ahead of 'It: Welcome to Derry' arriving on HBO, we’re counting down our favorite Stephen King nightmares from screens big and small.