Every company lives in fear of a ransomware attack, whether they have suffered one or not, and this is even more critical in the era of AI. This episode of the Tech Field Day Podcast looks forward to Commvault SHIFT in November with a discussion of the importance of data protection to AI applications with Tim Zonca from Commvault, frequent delegate Gina Rosenthal, and host Stephen Foskett. AI applications are reliant on good data, and yet this same technology makes it easier for attackers to breach corporate controls. Today’s social engineering and phishing is more convincing than ever thanks to generative AI, and this has helped ransomware crews to adopt larger and more powerful attacks. Ransomware is a massive business, and it isn’t going away any time soon. At the same time, GenAI applications offer a new attack surface, as agentic AI is empowered to take action based on untrusted inputs. Not only can we not stop ransomware, but the pace and technical capabilities of these attacks keeps accelerating. There is reason for optimism, however, as data protection tools keep getting better. Today’s AI-optimized tools can effectively categorize data, burst or migrate to different locations, and roll back or recover from corruption or compromise. In the future, we will see increasing use of AI to monitor systems and data, detecting patterns and hardening the attack surface.
Moving Enterprise AI Applications From Experiments to Production with NetApp
Running enterprise applications in production is a lot different from the AI experiments many of us have been involved with so far. This episode of the Tech Field Day podcast, recorded prior to NetApp Insight 2025, features Ingo Fuchs from NetApp along with Gina Rosenthal, Glenn Dekhayser, and Stephen Foskett. AI applications often start as experiments with a limited data set, but once these are moved to production there are many critical decisions to be made. Data must be classified and cleaned, removing personal and financial data and proprietary information before it even reaches an LLM. Data also must be structured for embedding and vectorization prior to use by an LLM. And we have to ensure that data is up to date or the application will not serve the customer properly. Finally we have to consider whether it is proper and ethical to share and act on this data. Many of the challenges facing modern AI applications are similar to the historic issues faced by enterprise storage, and this is an area in which NetApp and their customers have decades of experience.
Passkeys are the Future
Passwords create friction and therefore users find ways around them. New technology such as secure enclaves and PKI allow us to create better solutions like passkeys. In this episode of the Tech Field Day Podcast. Alan Shimel and Kate Scarcella join Tom Hollingsworth to discuss the problems with traditional passwords and how passkeys overcome them. They also talk about why it has taken so long to adopt passkeys and what barriers remain to full implementation. The wrap up with a look at what might lay ahead on the horizon for the future of user security.
The Latest in Cybersecurity Innovation at Security Field Day 14
The world should be more secure, and Tech Field Day is helping make it that way with the latest edition of Security Field Day. The event features two full days of presentations from leading security companies, innovative startups, and expert delegates, all focused on keeping people and software safe in an ever-evolving threat landscape. Tune […]
Unified Flash Memory and Reduced HBM are Reshaping AI Training and Inference with Phison
AI will need less HBM (high bandwidth memory) because flash memory unification is changing training and inference. This episode of the Tech Field Day podcast features Sebastien Jean from Phison, Max Mortillaro, Brian Martin, and Alastair Cooke. Training, fine-tuning, and inference with Large Language Models traditionally use GPUs with high bandwidth memory to hold entire data models and data sets. Phison’s aiDaptiv+ framework offers the ability to trade lower cost of infrastructure against training speed or allow larger data sets (context) for inference. This approach enables users to balance cost, compute, and memory needs, making larger models accessible without requiring top-of-the-line GPUs, and giving smaller companies more access to generative AI.
Software is Automating Your AI Data Centre Infrastructure
Hardware always matters, especially in AI and now software is automating your AI data centre infrastructure. This episode of the Tech Field Day podcast features Gina Rosenthal, Barton George, Andy Banta, and Alastair Cooke. Generative AI brought new hardware into enterprise data centres; GPUs, TPUs, NPUs, XPUs all offload AI processing from CPUs for more performance and efficiency. Feeding these accelerators requires fast networks and fast storage, common topics for AI Infrastructure Field Day events. In parallel, sophisticated software to automate the deployment and operation of this new hardware is vital to return value fast and optimize the value from the hardware investment. Automation platforms are moving up towards delivering multiple AI applications on shared XPU infrastructure, where AI inference delivers the business value.
Pushing the Boundaries of AI Performance, Scale, and Innovation at AI Infrastructure Field Day 3
Tech Field Day is heading back in Santa Clara, California on September 10th and 11th for AI Infrastructure Field Day 3. You can watch live on the Tech Field Day website, LinkedIn Page, or Techstrong TV to see how the boundaries of performance, scalability, and innovation are being pushed by our presenting companies. The event […]
Agentic AI Spells the End of Dial Twiddlers
If you haven’t already, start working with Generative AI now and make sure to control your ongoing costs. This episode of the Tech Field Day podcast features Russ Fellows, Mitch Lewis, and Brian Martin, all from Signal65, and is hosted by Alastair Cooke. Generative AI is delivering value to businesses of all sizes, but significant evolution in models and technologies remains before maturity is achieved. Experimentation is essential to understand the value of new technologies, starting with cloud resources or small-scale on-premises servers. Business value is derived from the inference stage, where AI tools generate actionable information for users. Generative AI is like a knowledgeable and well-intentioned intern; someone more senior must ensure AI is given good instructions and check their work. In production, grounding and guard rails are vital to keep your AI an asset, not a liability.
Networks Need Agentic AI with HPE Juniper Networking
Agentic AI is reshaping the IT landscape and networking is no exception. Building upon the previous research into machine learning means we have a head start on harnessing that power. In this episode of the Tech Field Day podcast, brought to you by HPE Juniper Networking, Tom Hollingsworth is joined by Keith Parsons and Sunalini Sankhavaram. They talk about how agentic AI is driving new methods for operating networks and helping humans concentrate on real problems instead of menial tasks. They also discuss how agentic AI can power self-driving networks where configuration and provisioning are done automatically or with a minimum of effort to ensure resiliency and enhance user expectations.
A Look at Mainframe Innovation at Tech Field Day Extra at SHARE Cleveland 25
Tech Field Day Extra at SHARE Cleveland on August 19 promises a deep dive into mainframe innovation, and viewers are encouraged to subscribe to the Tech Field Day YouTube channel and follow the LinkedIn page for ongoing Field Day coverage.
Early Adoption of Generative AI Helps Control Costs with Signal65
If you haven’t already, start working with Generative AI now and make sure to control your ongoing costs. This episode of the Tech Field Day podcast features Russ Fellows, Mitch Lewis, and Brian Martin, all from Signal65, and is hosted by Alastair Cooke. Generative AI is delivering value to businesses of all sizes, but significant evolution in models and technologies remains before maturity is achieved. Experimentation is essential to understand the value of new technologies, starting with cloud resources or small-scale on-premises servers. Business value is derived from the inference stage, where AI tools generate actionable information for users. Generative AI is like a knowledgeable and well-intentioned intern; someone more senior must ensure AI is given good instructions and check their work. In production, grounding and guard rails are vital to keep your AI an asset, not a liability.
Network Engineers are Facing an Identity Crisis
Network engineers are the firefighters and knowledge bases of enterprise IT, however the role of a network engineer is rapidly evolving. With the rise of automation, orchestration, and AI, the familiar image of an engineer hunched over a command-line interface (CLI) is giving way, leading many to question the future of their profession. In this episode, Tom Hollingsworth is joined by Ryan Harris, Chris Grundemann, and Nathan Nielsen as they discuss how the perception of their role has shifted, the continuous need for learning and adaptation, and whether the CLI is truly dead.
The conversation explores the challenges and opportunities presented by these technological advancements, highlighting how network engineers are embracing new tools like chatbots and GUIs for enhanced visualization. While some aspects of the job, like manual CLI work, may be diminishing, the core principles of understanding network functionality remain core to the role of the network engineer. The panel talks about identity crisis in a field where continuous learning is essential, contrasting it with professions like doctors and lawyers who deal with slower-changing fundamentals. They discuss the value of specialization versus being a generalist, the concept of the “pitchfork engineer,” and ultimately, how redefining their identity as lifelong learners can help network engineers thrive in this ever-changing landscape.
The DoJ Just Devalued Juniper Mist
The proposed remedies for the HPE acquisition of Juniper Networks did a real disservice to Juniper Mist. The confusion around what’s going on with the proposed Juniper AIOps for Mist auction have professionals asking a lot of questions. In this episode, recorded on the eve of the close of the acquisition, Tom Hollingsworth sits down with Sam Clements, Jake Snyder, and Ed Weadon to make sense of it all. There are discussions about what exactly is included in the auction and what benefit will come from the license to use Juniper AIOps for Mist. Also discussed is who might be a good bidder for the solution and how long it will take for them to get any real value from it.
Enterprises Shouldn’t Be Outsourcing Their IT Anymore
Enterprise networks are complicated but outsourcing all of the operations team doesn’t lead to better outcomes. It’s important to remember that enterprise covers a wide range of network definitions. In this episode, Ed Weadon, Chris Grundemann, and Jody Lemoine join Tom Hollingsworth as they discuss how businesses see the network and IT in general as a cost center instead of value generation. They also talk about the various sizes of networks and why each of them has issues with the most popular outsourcing methods. They also discuss the human factor and why not all managed providers can give you the same level of service.
MLO is a Lie
One of the most anticipated features of Wi-Fi 7 isn’t ready for the public. Worse yet, it may never deliver on the promise of fast, reliable wireless connectivity. In this episode, Tom Hollingsworth is joined by Allyn Crowe, Peter Mackenzie, and Chris Reed as they discuss the way that multi-link operation (MLO) has been included in the specification for Wi-Fi 7 yet not quite implemented. They highlight the technical difficulties of deploying such a complicated protocol and how vendors are trying to squeeze every drop of performance out of their hardware. They wrap up with advice on whether or not to plan your next deployment around a technology that isn’t quite ready yet.
A Different Type of Datacenter is Needed for AI
AI demands specialized data center designs due to its unique hardware utilization and networking needs, which require a new type of infrastructure. This Tech Field Day Podcast episode features Denise Donohue, Karen Lopez, Lino Telera, and Alastair Cooke. Network design has been a consistent part of the AI infrastructure discussions at Tech Field Day events. The need for a dedicated network to interconnect GPUs differentiates AI training and fine-tuning networks from general-purpose computing. The vast power demand for high-density GPU servers highlights a further need for different data centers with liquid cooling and massive power distribution. Model training is only one part of the AI pipeline; business value is delivered by AI inference with a different set of needs and a closer eye on financial management. Inference will likely require servers with GPUs and high-speed local storage, but not the same networking density as training and fine-tuning. Inference will also need servers adjacent to existing general-purpose infrastructure running existing business applications. Some businesses may be able to fit their AI applications into their existing data centers, but many will need to build or rent new infrastructure.
User-Centric Connectivity Has to Innovate
Modern networking is being disrupted in the data center but user-facing networking has largely stagnated. Users are getting slightly faster connections but everything feels mostly the same. In this episode, Tom Hollingsworth is joined by Sam Clements and Ed Weadon as they discuss innovation in the edge of the network. They talk about how companies like Cisco have been trying to bring users into the modern era. They talk about the centralization of management in the cloud and how competition has driven those moves. They also look ahead to Cisco Live and discuss the releases they would most like to see at the event.
Have A Classy Time with Tech Field Day Extra at Cisco Live US 2025
Hello San Diego! We’re thrilled to be back once again with great content headed your way courtesy of Tech Field Day Extra!. We’re hoping the June Gloom stays away so we can shine a light on some wonderful presenters and get some great questions from our amazing delegates. You’re not going to want to miss […]
Scaling Smarter Optimizes Cloud Costs in the Age of Data Abundance
Keeping every application and every scrap of data on the public cloud becomes very expensive; we need to improve our cloud economics. This episode of the Tech Field Day podcast features Vriti Magee, Mitch Lewis, and Alastair Cooke. The belief that data is the new oil has led many companies to retain every piece of data they generate, often in object storage on public cloud platforms. The continuous growth of this data leads to a growing bill from the cloud provider, often with no clear plan in place for recouping the value of the money spent. Generative AI requires training data, which is another reason to retain everything; again, there needs to be value returned to the business. New designs for cloud applications must include data management and managed retention as key criteria. Sustainable, honest designs that enable business change are vital for delivering value back to the business.
Exploring Cloud Resilience, AI, and Data at Cloud Field Day 23
Cloud Field Day is making its highly anticipated return to San Francisco on June 4th and 5th, bringing together some of the biggest names in cloud technology for two days of in-depth insights and live demos. You can catch every moment of the action live on the Tech Field Day LinkedIn page and Techstrong TV. […]