Tech Talks Daily

I invited Michael Reitblat, CEO and founder of Forter, to unpack a reality many retailers are living with every day. Fraud is no longer a side issue. It shapes conversion rates, customer loyalty, and the bottom line. Michael argues that if you remove the fear of fraud, you unlock growth. That sounds bold, but his lens is practical. Replace guesswork with instant, consistent decisions and you improve both security and the checkout experience. Here’s the thing. False declines feel like fraud in disguise. When good customers get blocked, they do not return. Michael explains how Forter uses real-time signals to say yes or no within the transaction, without adding friction. The promise is simple. If a buyer is genuine, let them through. If it is fraud, stop it and cover the chargeback. It is a clean model that puts accountability on the platform, not the merchant. We also talk about what happens when AI agents start buying on our behalf. If software is placing orders, refunding items, or filing disputes, identity and intent become fluid. Michael walks through how trust platforms need to reason about behavior across accounts, devices, and sessions. The goal is confidence at the moment of purchase without slowing anyone down. Michael shares how Forter’s scope has expanded from blocking bad actors to enabling smart, business-wide decisions about customers. That means recognizing loyal buyers even if they shop across regions and brands, and spotting synthetic identities that mimic human patterns. It also means measuring success by approvals and lifetime value, not only by stopped attacks. Let me explain why this matters. Retailers are caught between two pains. Ease up and you invite chargebacks. Tighten controls and you lose revenue from good customers. Michael’s point is that trust should be a growth lever. If the system is confident, the checkout stays smooth on web and mobile. If the system is unsure, it can ask for the least painful extra step rather than send a blanket decline. We close with practical guidance for leaders. Treat trust as a product. Give teams shared visibility into decisions. Align incentives so fraud, payments, product, and marketing are working from the same truth. Michael’s vision is a world where anyone can transact with ease because fraud has been priced out of the experience. That is a conversation worth having, and one retailers can act on today.

Here’s the thing. “Smart” has been the buzzword for years, but Richard Leurig argues we’re on the cusp of something bolder. In our conversation, the Accruent president drew a clear line between buildings filled with connected systems and buildings that can sense, decide, and act without a person staring at a dashboard all day. Richard shared a retail story that sticks. By wiring refrigeration units with sensors and training models on billions of telemetry points, his team can spot failures 48 to 72 hours before lettuce wilts or milk spoils. That time window turns panic calls at 3 a.m. into planned daytime fixes. It cuts waste, protects revenue, and keeps customers from walking into empty shelves. The bigger idea is a shift from many panes of glass to no pane of glass. Instead of asking people to wrangle alerts, AI agents coordinate HVAC, security, and maintenance, then dispatch the right technician with the right part only when one is truly needed. That is the road to self-healing facilities. Practicalities that matter now Let me explain why this resonates across industries. Whether you run a hospital, a university, a factory, or a grocery chain, you’re wrestling with aging infrastructure and short supply of skilled workers. Richard sees the same pattern everywhere. Teams need guidance at the point of work, not another report. Natural language agents that answer plain questions and walk users through a task are winning hearts because they remove friction. Return-to-office adds another layer. Hybrid work has made space usage lumpy. Richard outlined how linking lease data, occupancy, and booking behavior helps leaders decide what to close, reshape, or scale. It also changes floor plans. When people do come in, they want project rooms and collaboration zones, not endless rows of cubicles. Retrofit is the sleeper story. You don’t need a skyline of brand-new towers to get smarter. Low-cost sensors and targeted integrations are making older buildings more responsive than most people expect. That opens the door for progress without nine-figure capex. Energy, sustainability, and proof Boards want less energy spend and real emissions progress. The quickest wins are often hiding in plain sight. Richard walked through HVAC control that follows people, sunlight, and weather rather than fixed schedules. Lights that turn off when a room is empty are yesterday’s news. Cooling only where teams are actually working is today’s play. He also flagged a coming wave on factory floors. Many legacy motors and line components quietly draw more power than they should. Clip-on sensors can spot out-of-tolerance behavior so maintenance can fix the energy hog instead of replacing an entire line. That is the kind of operational change that lowers bills and supports sustainability targets with data, not slogans. Richard’s timeline is refreshingly near term. He believes a large slice of the built environment will show real autonomy in three to five years. Not theory. Not demos. Everyday operations that quietly handle themselves until a human is truly required. If this conversation sparks an idea for your sites, stores, labs, or campuses, I want to hear how you’re approaching it. What feels possible this quarter, and what still feels out of reach?

What if the biggest weakness in cybersecurity isn’t a missing tool, but a cultural blind spot? That’s the perspective of Dan Jones, Senior Security Advisor at Tanium, who joined me on Tech Talks Daily to share why he believes cybersecurity is fundamentally a people problem dressed up as a technology problem. Dan brings nearly three decades of experience in cyber operations, including leading cyber defence strategy for the UK Ministry of Defence. His career has shown him that technology alone doesn’t secure organisations—it’s the people at the front line, their leadership, and their ability to make the right decisions under pressure. He argues that while new tools flood the market every year, the make-or-break factor remains the same: how teams are led, supported, and empowered. In our conversation, Dan explains why leadership is often the overlooked part of cybersecurity, how culture shapes security outcomes, and why automation should be embraced not as a threat to jobs but as a way to give people time back for higher-value decision making. He shares examples from both military and enterprise contexts, showing how organisations succeed or fail based not on what tools they buy, but on how well they bring their people along for the journey. We also dig into one of today’s hottest debates: the role of AI in cybersecurity. While many fear AI will displace jobs, Dan insists those fears are rooted in culture, not reality. He draws parallels to past industrial shifts, making the case that automation and orchestration are stepping stones that prepare teams for an AI-powered future—one where human judgment still sits firmly at the centre. This is a timely reminder for every leader and practitioner that cybersecurity is about more than firewalls and code. It’s about trust, training, and people working together with the right tools at the right time. And yes, it’s also about taking five minutes to brew a proper cup of tea—a lesson Dan believes says a lot about leadership and reflection. If you’ve ever wondered whether your organisation is focusing too much on tools and not enough on culture, this episode will make you stop and think. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Some interviews stick because they take a noisy topic and bring it back to reality. This was one of them. I spoke with Erin Gajdalo, CEO of Pluralsight, about what it actually takes to upskill a workforce in an AI era that seems to change by the week. We compared boardroom intent with day-to-day practice, and Erin was refreshingly clear about both. Pluralsight began more than twenty years ago in classrooms, moved online as the market shifted, and now supports Fortune 500 teams with expert-led courses, hands-on labs, and the admin tools leaders need to measure progress at scale. The thread running through the whole story is simple: people learn by doing, and companies get value when that learning maps to real work. We talked about AI in her own workflow first. Erin uses it to draft presentations, crunch data, and speed up research, then pushes that mindset across the company through focused sprints where every department experiments and reports back. That culture piece matters. Pluralsight’s latest research found that 61 percent of respondents still think using generative AI is “lazy,” which drives employees to adopt tools in the shadows and exposes the business to avoidable risk. Her answer is clear guidance, safe environments to practice, and permission to test without fear of failure. The payoff shows up in real examples. One financial services firm raised prompt engineering efficiency by 20 percent and saved 1,600 hours in three months by pairing assessments with prescriptive learning paths and hands-on practice. We also explored the fear that keeps people quiet. Layoff headlines travel faster than case studies, and that skews the mood inside many teams. Erin makes a straightforward case. Treat AI as an assistant that improves standard and repetitive tasks, protect the business with clear policies, then invest in education for everyone, not only engineers. Close the confidence gap with data. Baseline skills, prescribe learning, measure proficiency, and tie improvements to actual tasks. When leaders show their own work and give teams room to try things, adoption follows. The conversation finished on the future. Technical skills will keep evolving, but the standout advantage will be a willingness to learn and the soft skills that carry ideas from prototype to production. Erin also shared a personal goal that resonated with me. She would love a private breakfast with Serena Williams to talk about Serena Ventures and backing founders from underrepresented groups. It fit the theme of the episode. Talent is everywhere. Opportunity appears when someone opens a door and stays long enough to help you through it. If you want the full story, including how Pluralsight is updating its platform for scale and how leaders can reduce “shadow AI” without slowing innovation, you can find their research and resources at Pluralsight.com. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Here’s the thing. We have had brilliant ideas in Web3 for years, along with better tooling and plenty of enthusiasm, yet adoption still feels slower than it should be. In my conversation with Maciej Baj, founder of t3rn, we got under the skin of why that is and what it might take to change the pace. His starting point is simple to state and hard to deliver at scale: make cross-chain interactions feel seamless for users and predictable for developers. If you can do that, the door opens to practical products rather than experiments that only the bravest try. Maciej describes t3rn as a universal execution layer for cross-chain smart contracts, and the phrase matters because it changes how we think about interoperability. Instead of stitching together a mess of bridges and oracles, t3rn lets a contract access state and data across multiple chains from one place. Today it is mapped to the EVM for broad compatibility, but the design is chain agnostic by intent. That choice is less about tribal loyalties and more about meeting developers where they already build while keeping the door open to other ecosystems as the market evolves. Trust shows up in the details, and atomic execution is one of those details that changes behavior. If a multi-chain transaction cannot complete in full, it reverts. No half-finished transfers. No manual recovery adventures. This mirrors what smart contracts already offer on a single chain, which means developers can reason about outcomes without inventing fresh playbooks for every hop. It also reassures users, who care less about the plumbing and more about knowing that funds either arrive or return. Cost matters too. t3rn has been engineered for cost-efficient token movement across chains, which sounds mundane until you price a complex strategy that touches multiple venues. Lower friction makes new use cases economical. Maciej outlined a few that caught my eye. Trading algorithms that read and act on signals from multiple chains without duct tape. Simpler asset movement across ecosystems that do not share a wallet culture or UX conventions. Agent-driven executors that can watch for arbitrage or rebalance a portfolio without constant human oversight. The theme is the same throughout. Reduce the number of hoops and you increase the number of people willing to try something new. We also looked ahead. t3rn is preparing an integration with hyperliquid and rolling out a builder program to widen the ecosystem on top of its execution layer. An SDK is on the way so the community can help bring in new chains faster, rather than waiting for a core team to do all the heavy lifting. There is a governance track forming as well, aimed at giving the community more say in integrations and priorities. None of this guarantees success, but it signals a path from protocol to platform. I left the conversation with a clearer view of why interoperability still matters in 2025. The multi-chain world is not going away. Users move between ecosystems. Developers deploy to several environments at once. Liquidity, identity, and logic already live in many places. A universal execution layer that is reliable, cost aware, and easy to build on is the kind of boring-sounding foundation that ends up changing behavior. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

When we think about what separates winning traders from those who struggle, we usually picture strategies, indicators, or a bit of insider know-how. But what if the biggest edge has been sitting on your desk all along? In this episode, I sit down with Eddie Z, also known as Russ Hazelcorn, the founder of EZ Trading Computers and EZBreakouts. With more than 37 years of experience as a trader, stockbroker, technologist, and educator, Eddie has built his career around one mission: helping traders cut through noise, avoid expensive mistakes, and get the tools they need to stay competitive in a fast-moving market. Eddie breaks down the specs that actually matter when building a trading setup, from RAM to CPUs to data feeds, and exposes which so-called “upgrades” are nothing more than overpriced fluff. We also dig into the rise of AI-powered trading platforms and bots, and what traders can do today to prepare their machines for the next wave. As Eddie points out, a lagging system or a missed feed isn’t just an inconvenience—it can be the difference between a profitable trade and a costly loss. Beyond the hardware, we explore the broader picture. Rising tariffs and global supply chain disruptions are already reshaping the way traders access technology, and Eddie shares practical steps to avoid being caught short. He also explains why many experienced traders overlook their machines as a “secret weapon” and how quick, targeted fixes can transform reliability and performance in under an hour. This conversation goes deeper than specs and gadgets. Eddie opens up about the philosophy behind the EZ-Factor, his unique approach that blends decades of Wall Street expertise with cutting-edge technology to simplify trading and help people succeed. We talk about his ventures, including EZ Trading Computers, trusted by over 12,000 traders, and EZBreakouts, which delivers actionable daily and weekly picks backed by years of experience. For traders looking to level up—whether you’re just starting out or managing multiple screens in a professional setting—this episode is packed with insights that can help you sharpen your edge. Eddie’s perspective is clear: the right machine, the right mindset, and the right knowledge can make trading not only more profitable, but, as he likes to put it, as “EZ” as possible. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Most conversations about AI are still caught up in the spectacle. We see demos, marvel at copilots, and argue about the latest big model. But what happens when you strip away the hype and focus on AI that simply works? That is exactly the perspective Olga Lagunova brings to this episode. As Chief Product and Technology Officer at GoTo, she has one goal in mind: make AI useful, practical, and almost invisible. Olga believes the real test of AI is whether it integrates seamlessly into workflows. In her view, the most powerful AI is the kind that feels almost boring because it is just part of how work gets done. During our conversation she explains how GoTo is embedding AI into its platform so that small and midsize businesses can benefit without needing data scientists on staff or large budgets to experiment. We explore the difference between AI for SMBs and AI for enterprises, and why simplicity and trust matter more than shiny features. Our discussion also goes deeper into agentic AI, where tools are no longer just assistants but are taking on tasks in the background. Olga highlights how GoTo balances this shift with guardrails, governance, and human-in-the-loop oversight to ensure that efficiency never comes at the cost of security. We also unpack the classic build versus buy dilemma, why shadow AI is becoming a real risk for companies, and how leaders can measure ROI in a way that proves value both immediately and over time. If you are tired of the hype and want to understand how AI is quietly reshaping the backbone of business operations, this episode with Olga Lagunova will give you a grounded and forward-looking perspective.

I wanted this conversation to do two things at once. First, ground the hype in real practice. Second, show how a small country can punch well above its weight by connecting industry, academia, and government with purpose. With Chantelle Kiernan from IDA Ireland and Stephen Flannagan from Eli Lilly and Company, we explored what digital transformation really looks like on the factory floor in Ireland, why talent is the engine behind it, and how cross-sector collaboration is turning ideas into measurable outcomes. Ireland’s manufacturing base employs hundreds of thousands and fuels exports, yet what stands out is the shared mindset. The shift toward Industry 5.0 puts people at the center while using digital, disruptive, and sustainable technologies to rethink production. Eli Lilly’s experience shows how a digital-first culture changes everything. New sites start paperless by default. Established plants raise their game through micro-learning, data-driven problem solving, and champions who model the behavior. The message is simple. Technology only sticks when people see clear value and have the skills to act on it. From pilots to site-wide change Here’s the thing. The strongest wins come from a strategic, site-wide approach rather than isolated pilots. Maturity assessments across pharma sites in Ireland revealed common patterns, shared bottlenecks, and repeatable opportunities. That insight helps teams justify investment, sharpen ROI arguments, and accelerate adoption without slowing production. Reinvestment in legacy facilities becomes a long-term advantage when you connect equipment, data, and people with a clear plan. This is where Ireland’s ecosystem shows its class. Purpose-built centers like Digital Manufacturing Ireland, NIBRT, IMR, and I-FORM give teams a place to test before they invest. Indigenous tech SMEs sit at the same table as global pharma leaders and large tech firms, which means collaboration moves faster. When 50 percent or more of new R&D projects cite academic partnerships, you know something healthy is happening. Skills, STEM, and the mindset shift Upskilling came through as the decisive enabler. IDA Ireland supports companies with skills needs analysis and access to training. Universities co-create relevant courses. Micro-credentials and immersive apprenticeships build confidence on the shop floor. Stephen’s point about micro-learning hit home. People learn best when they can apply knowledge to a problem they care about, right now. That keeps momentum high and spreads digital competence across teams without waiting on giant projects. Barriers still exist. Defining ROI, coping with regulatory complexity, and balancing change with daily production are real challenges. Culture is the swing factor. Leaders who set the tone, create space for experiments, and reward progress see faster results. GenAI is already shifting attitudes by improving personal productivity, which naturally opens minds to operational use cases like predictive maintenance, knowledge capture, and quality improvements. What comes next If the last decade was about connecting machines, the next decade will be about connecting knowledge. Expect smarter, greener, and more multidisciplinary manufacturing. AI will sit alongside advanced materials and sustainable design. The most resilient sites will combine agile infrastructure with strong learning cultures, so they can absorb change rather than resist it. Ireland’s model of collaboration gives a useful signal. When industry, government, and academia align around shared outcomes, the runway gets longer and the takeoff gets smoother. This episode is about the practical choices that make transformation real. Strategic assessments. Shared R&D spaces. Cohorts of digital champions. And a relentless commitment to skills. It is a story of steady progress that scales, and a reminder that the future belongs to teams who can learn faster together. ********* Visit the Sponsor of Tech Talks...

What does it really mean to future-proof financial data? That’s the question at the heart of my conversation with George Rosenberger, General Manager of NYFIX at Broadridge. George has spent his career moving through every corner of the capital markets, from trading desks to broker-dealers, and now into the software side where he oversees order routing, post-trade matching, and the adoption of new AI tools. His perspective is uniquely positioned between the history of financial markets and their rapidly accelerating future. This discussion takes inspiration from Broadridge’s fifth annual Digital Transformation and Next Gen Technology study, which collected insights from more than five hundred technology and operations leaders across financial services. The survey highlights both the progress and the pressure points facing the industry. Forty-one percent of leaders still cite data security as a major hurdle, and while cloud, AI, and cybersecurity dominate the technology stack, a third of firms still lack security built into their core systems. George explains why this gap persists, how legacy platforms complicate modernization, and what steps firms can take to extract value from old infrastructure while preparing for what’s next. We also explore the irony that many organizations overestimate their digital maturity. Generative AI adoption has surged from forty to seventy-two percent in a year, but governance, compliance, and data quality concerns remain. George stresses the importance of measuring outcomes, not just intentions, and shares how Broadridge is approaching AI responsibly through initiatives like its Algo Copilot, which helps traders make sharper decisions. If you’re curious about how financial services can strengthen cybersecurity, reduce technical debt, and rethink data strategy as a true engine of innovation, this episode offers both a candid reality check and a roadmap. The speed of change is staggering, but with the right strategy, leaders can build resilience and stay ahead in a digital-first world.

What does it take to deliver personalized financial guidance to more than 140 million people every single day? That is the question I put to Wan Agus, Head of Engineering at Intuit Credit Karma, in this episode of Tech Talks Daily. Most of us open the Credit Karma app to check our credit score, look at a loan option, or browse for a better credit card. What we rarely consider is the technology running behind the curtain. Wan revealed that his teams are powering more than 60 billion daily AI predictions to understand members’ needs, protect their privacy, and guide them toward the right financial choices. He explained why accuracy is everything in fintech. A misplaced recommendation can mean more than a poor customer experience; it can damage someone’s credit score and hold back their progress. Our conversation also looked at what happened after Intuit acquired Credit Karma. Two very different tech stacks had to be brought together, and identity systems had to be unified so members could move seamlessly between Credit Karma and products like TurboTax. Wan compared the process to playing two complex board games at once, where success depends on strategy and collaboration. We also explored how Credit Karma is blending traditional AI with generative AI. From early chatbot experiments to today’s Wallet Analyzer and Tax Advisor, Wan shared how his teams decide when to push forward with new tools and when to slow down to ensure safety and trust. He also gave us a glimpse into the future, where agent-to-agent technology could bring open banking-style transparency to the U.S. So how do you scale personalization without losing trust? And what can every business leader learn from Credit Karma’s balance between speed, culture, and responsibility? I would love to hear your thoughts after listening.

AI is quickly moving from boardroom buzzword to boardroom headache. Enterprises are waking up to the fact that bringing large language models in-house is not just about performance or cost, but about control, accountability, and trust. In this episode of Tech Talks Daily, I sit down with Octavian Tanase, Chief Product Officer at Hitachi Vantara, to unpack what this shift really means for business and technology leaders. Octavian explains why governance has become the defining challenge of the AI era. Companies are under pressure not only to innovate but also to meet new regulatory demands and maintain trust with customers. That requires more than patching together tools or hoping for transparency from public AI providers. It means creating governance frameworks that deliver traceability, auditability, and explainability as standard practice, not as afterthoughts. We explore why vector databases may need something like a time-machine capability to document when and how information is added, giving enterprises a provable audit trail. This level of accountability supports both internal oversight and external compliance, turning abstract AI ethics debates into real operational requirements. Our conversation also turns to the role of infrastructure. Hitachi Vantara’s VSP One, with its tagline “One Data Platform, No Limits,” has been built to simplify data complexity across block, file, and object storage while providing a unified foundation for AI workloads. Octavian shares how this unified approach helps enterprises run compliant, explainable, and efficient AI across hybrid environments that span both on-premises and the cloud. This isn’t just a story about technology, but about the future of trust in digital business. If AI remains a black box, its value will always be limited. If it becomes explainable, traceable, and accountable, it can transform not only efficiency but also relationships with customers, regulators, and partners. So, how can leaders strike the right balance between governance and innovation without slowing down progress? Octavian leaves listeners with a forward-looking perspective on what the next few years of enterprise AI will demand, and why those who build on strong governance today may end up with the most resilient advantage tomorrow. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

What if the way we store data is shaping the planet’s future? That thought has been on my mind ever since attending the IT Press Tour in Amsterdam, where I first connected with today’s guest. With global data creation forecast to hit 510 zettabytes by 2030, and data centers already consuming staggering amounts of power, the conversation is no longer about whether change is needed but about how we approach it. Joining me on the podcast is Nicholas Stavrinou, co-founder of CompressionX, a company rethinking lossless compression. Nicholas shares how a mathematical paradox in a university notebook grew into a technology that promises faster, cheaper, and more sustainable data storage. His story takes us from leather-bound journals and napkin sketches to a working product that is already helping users cut their digital footprints by more than 90 percent. In our discussion, Nicholas explains why compression deserves a seat at the sustainability table, especially as AI and enterprise workloads generate unprecedented volumes of cold data that simply sit idle in storage. We talk about the real costs of data growth, from spiraling cloud bills to the hidden environmental toll of cooling data centers, and we explore whether smarter compression could give businesses an edge while also reducing emissions. Nicholas and his team are also taking this message beyond theory. After the IT Press Tour, they are heading to Big Data LDN at Olympia London, where Compression X will be presenting in the Data for Good theatre at 2:40pm on Wednesday, September 24, and welcoming visitors at stand G58. It’s a reminder that sustainable infrastructure isn’t just about grand new facilities or green energy projects; sometimes it starts with rethinking something as humble as a file format. As you listen, ask yourself: could compression be one of the simplest yet most overlooked ways to make digital life more efficient, affordable, and sustainable? ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

I recorded this episode at Barracuda TechSummit25 in Alpbach, Austria, a mountain village that looks like a postcard and hosts some of the most grounded security conversations you will hear all year. My guest is Richard Flanders, Commercial Director at Aura Technology, a managed service provider on the south coast of England that supports public sector organisations and tightly regulated commercial clients. Richard arrived as part of Barracuda’s Partner Advisory Board, which means he spends as much time feeding customer reality back into product teams as he does comparing notes with peers in the hallway. We talk through his first TechSummit experience and why the event’s focus on hands-on engineering matters for MSPs who live in the weeds of configuration, policy, and response. Richard shares early thoughts on Barracuda’s secure edge service and the continued maturation of XDR, but the heart of our chat is the pressure he sees on customers. Compliance is no longer a side quest. ISO 27001, Cyber Essentials Plus, supply chain reporting, and new European rules are shaping budgets and expectations. Boards want proof. Auditors want evidence. Buyers want to know a supplier chose fit-for-purpose tools. That makes documentation, contracts, and the ability to show your working as important as the tech itself. We also get into the human side. In a world that loves point solutions, many teams are tired of alert noise and tool sprawl. Richard explains why a single, coherent view helps his engineers move faster and train better, and why MSPs are leaning into prevention-focused workflows rather than waiting for the next fire. He is candid about the conversations no one enjoys, like end-of-life systems that keep a legacy app alive, and the need for tougher stances when risk sits outside an acceptable boundary. AI comes up too, without the hype. Aura is hiring a Head of AI and Automation, standing up a private AI platform, and committing to ship a handful of small, useful apps for customers in the year ahead. The lens is productivity and safety, with an emphasis on teaching teams how to question outputs and rethink everyday tasks. Add in security awareness training, phishing simulations, and tabletop exercises, and you start to see a culture shift from annual tick-boxes to regular, lived practice. There is a lovely moment of serendipity in here as well. Richard’s first conversation on day one was with another partner from Pune, the same city where Aura runs its network operations. They swapped ideas on automation and integration that might never have surfaced on a video call. That is the value of getting people in a room together, especially when the room happens to be carved into the side of a mountain. If you work with an MSP, this episode will help you ask better questions. If you are an MSP, you will recognise the balance Richard describes. Pick the right controls for the risks you actually face. Prove what you do. Keep training. And give your teams a single place to see what matters, so the next incident stays small. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

I recorded this conversation at Barracuda TechSummit25 in Alpbach, Austria, where the mountains feel close enough to touch and the discussions get very real very quickly. My guests are Adam Khan, VP of Global Security Operations at Barracuda XDR, and Eric Russo, Director of SOC Defensive Security. Together they run the teams that watch, interpret, and act when attacks move across email, identity, network, cloud, and endpoints. Their keynote used the language of sport to make sense of modern defense, and it worked. You will hear why football tactics map cleanly to security, how roles and formations translate to controls and playbooks, and why a strong back line matters when the opposition moves the ball quickly. Here is the thing that stood out for me. Integrated defense is not a slogan. When Adam and Eric talk about Extended Detection and Response, they are describing a practical way to join signals, add context, and trigger action without waiting for a human to click through ten consoles. XDR gives analysts one source of truth, connects events that would otherwise sit in separate tools, and shortens the time between a suspicious signal and an action that contains it. That is how you turn alert fatigue into something manageable, and it is how small teams hold their own against fast, multi-step attacks. The analogies make it easier to picture. In football, a defense tracks runners, closes passing lanes, and communicates constantly. In security, that means correlating identity with network flows and endpoint behavior, then deciding who picks up the threat and how to press. The Home Alone reference takes it further. Imagine Kevin’s improvised defenses as point tools scattered around a house. Now add a single screen that shows every door, every window, and which trap fires next. That is the plain-English version of XDR that anyone can understand. We also unpack real incidents that their teams have faced, without naming names. You will hear how attackers chain steps across layers, and how automated responses isolate systems, lock accounts, and cut off command and control before damage spreads. The lesson is simple. Visibility gives you options. Automation buys you time. People make the right calls when they can see the whole pitch. If you work in security, this episode gives you a clear view of what good looks like. If you are a business leader, it offers a way to measure progress that goes beyond tool counts and budget lines. And if you enjoy a metaphor that lands, football and Home Alone might be the clearest explanation of XDR you will hear all year.

I recorded this conversation in Alpbach, Austria, a village that looks like a postcard and hosts a very serious tech gathering. TechSummit25 is Barracuda’s deeply technical event, and it shows. The rooms are packed with solution architects, product managers, and engineers comparing notes with customers who run these systems every day. It is the kind of environment where product direction and real-world pain points meet over a coffee, then head straight into a lab to test an idea. My guest today is Neal Bradbury, Chief Product Officer at Barracuda, who leads engineering, product management, and the operations teams that keep services running around the clock. Fresh from a session titled “Secured today, secured tomorrow,” Neal breaks down what that promise means in practice. We explore why Barracuda is doubling down on a platform approach with Barracuda One, how a single dashboard helps teams see posture and value in one place, and why consolidation matters when alerts and tools pile up faster than teams can respond. We also talk about the balance between immediate protection and longer-term planning. Neil explains how quarterly releases and shared services underpin the roadmap, how zero trust network access moves from theory to deployment as VPNs fade, and how managed vulnerability services help organizations find risks they did not know they had. He shares why service providers are shifting toward vCIO and vCISO models, how value reporting answers the board’s simplest question about where the budget goes, and why response time is the measure that keeps coming up in every conversation. Secured today, secured tomorrow The headline theme is simple enough. Know where you stand right now, then set a clear plan for the next year. Barracuda One aims to cut noise and show whether tools are configured properly. The same view rolls up alerts across email, network, and application security, and for MSPs it stretches across all customers. That single source of truth is designed to reduce swivel-chair work and make decisions faster. We dig into the reality of tool sprawl and alert fatigue. A recent study Barracuda commissioned points to teams carrying too many point solutions, with slower responses and misconfigurations as the cost. Neal’s answer is convergence without ignoring specialist depth. Product groups keep shipping, while shared AI and threat protection services raise the floor across the portfolio. That approach feeds directly into XDR, where integrations with tenants, firewalls, and endpoints help shrink the gap between detection and action. AI sits in the background of all of this. Neal describes it as a reckless intern that needs guardrails. In practice that means setup wizards that cut deployment time, incident response that can pull a bad message from twenty tenants in one sweep, and ML-driven triggers that fire automated remediation when patterns line up. The aim is clear. Let machines handle the routine work at machine speed, so people can focus on decisions and the weird edge cases attackers love to try. What listeners will take away If you run security day to day, you will hear practical direction rather than slogans. Consolidated dashboards exist to show posture, not just counts. Value reporting exists to explain outcomes to a board, not to pad a slide deck. Managed services rise in importance because many organizations need strategy as much as tools, and that includes smaller enterprises that outsource large parts of their stack. For leaders planning the next quarter, the emphasis on zero trust and managed vulnerability services will stand out. For operators, the XDR and SOAR focus is about shaving minutes into seconds, connecting identity with network and endpoint events, and giving analysts room to breathe. And for anyone curious about how product roadmaps form, conferences like this one offer a candid loop between feedback and action that you rarely see on a press release. By the time we wrap, Alpbach’s...

In this episode of Tech Talks Daily, I’m joined by Todd Grabowski from Johnson Controls to unpack the physics, products, and design choices shaping the next generation of data center cooling. It’s a practical conversation that moves from chips and compressors to water, power, and land constraints, and what it really takes to keep modern infrastructure reliable at scale. Todd brings three decades of experience to the table and a front-row view of how Johnson Controls and the York brand have kept their focus on energy efficiency, reliability, and sustainability for more than a century. That longevity matters when the market is moving fast. He explains why cooling now sits alongside power as the defining constraint for data centers, and why roughly forty percent of a facility’s energy can be spent on cooling rather than computation. If you lead technology, finance, or facilities, that single number should focus the mind. Todd walks through Johnson Controls’ YVAM platform and the York magnetic bearing centrifugal compressor at its core, with real numbers on what that means in practice. Consuming around forty percent less energy than typical cooling devices of the past five years and operating in ambient conditions up to fifty-five degrees Celsius, it is designed for the reality of hotter climates and denser loads. The naval pedigree of the driveline is a nice twist, since it was originally built for quiet and high-reliability conditions long before hyperscale data centers needed the same. Sustainability threads through the entire discussion. Todd lays out how the company holds itself to internal targets while engineering solutions that reduce customer resource use. We talk about closed-loop designs that do not consume water, careful refrigerant choices with ultra-low global warming potential, and product footprints that consider carbon impact from the start. It is a useful reminder that sustainability is a systems problem, not a single feature on a spec sheet. I was especially interested in the three resources Todd says every modern cooling strategy must balance. Land, because you need somewhere to reject heat. Power, because every watt pulled into cooling is a watt not used for compute. Water, because many regions are already under stress and consumption cannot be the answer. Good design weighs these factors against the climate, the workload profile, and the operational model, then standardizes wherever possible so the same unit can run efficiently in Scandinavia or Dubai without special tweaks. We also dig into what AI means internally for Johnson Controls. It is showing up in manufacturing lines, speeding up design cycles, and improving the fidelity of compressor and heat transfer models. That translates into quicker time to market and more confidence in performance envelopes. On the market side, Todd is clear that demand has not softened. If anything, efficiencies tend to unlock more use cases, and the net effect is more workloads and continued pressure on facilities to cool them well. If your team is wrestling with when to adopt liquid cooling, how to reduce PUE through smarter chiller choices, or how to plan for climate variability across a global footprint, this episode offers an honest, grounded view from someone who has shipped the hardware and lived with its trade-offs. It also doubles as a quiet celebration of engineering craft. The kind that rarely makes headlines, yet underpins everything we build in the AI age. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

I’ve spent years talking about endpoint security, yet printers rarely enter the conversation. Today, that blind spot takes center stage. I’m joined by Jim LaRoe, CEO of Symphion, to unpack why printers now represent one of the most exposed corners of the enterprise and what can be done about it. Jim’s team protects fleets that range from a few hundred devices to tens of thousands, and the picture he paints is stark. In many organizations, printers make up 20 to 30 percent of endpoints, and almost all of them are left in a factory default state. That means open ports, default passwords, and little to no monitoring. Pair that with the sensitive data printers receive, process, and store, plus the privileged connections they hold to email and file servers, and you start to see why attackers love them. We trace Symphion’s path from a configuration management roots story in 1999 to a pivot in 2015 when a major printer manufacturer invited the company behind the curtain. What they found was a parallel universe to mainstream IT. Brand silos, disparate operating systems, and a culture that treated printers as cost items rather than connected computers. Add in the human factor, where technicians reset devices to factory defaults after service as second nature, and you have a recipe for recurring vulnerabilities that never make it into a SOC dashboard. Jim explains how Symphion’s Print Fleet Cybersecurity as a Service tackles this mess with cross-brand software, professional operations, and proven processes delivered for a simple per-device price. The model is designed to remove operational burden from IT teams. Automated daily monitoring detects drift, same-day remediation resets hardened controls, and comprehensive reporting supports regulatory needs in sectors like healthcare where compliance is non-negotiable. The goal is steady cyber hygiene for printers that mirrors what enterprises already expect for servers and PCs, without cobbling together multiple vendor tools, licenses, and extra headcount to operate them. We also talk about the hidden costs of DIY printer security. Licensing multiple management platforms for different brands, training staff who already have full plates, and outages caused by misconfigurations all add up. Jim shares real-world perspectives from organizations that tried to patch together a solution before calling in help. The pattern is familiar. Costs creep. Vulnerabilities reappear. Incidents push the topic onto the CISO’s agenda. Symphion’s pitch is straightforward. Treat print fleets like any other class of critical infrastructure in the enterprise, and measure outcomes in risk reduction, time saved, and fewer surprises. If you are commuting while listening and now hearing alarm bells, you are not alone. Think about the printers scattered across your offices and clinics. Consider the data that passes through them every day. Then picture an attacker who finds default credentials in minutes and uses a printer to move across your network. Tune in for a fast, practical look at a risk hiding in plain sight, and learn how Symphion’s Print Fleet Cybersecurity as a Service can help you close a gap that attackers know too well. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

Marketing teams used to have a simple enough job: follow the click, count the conversions, and shift the budget accordingly. But that world is gone. GDPR, iOS restrictions, and browser-level changes have left most attribution models broken or unreliable. So what now? In this episode, I sat down with Fredrik Skansen, CEO of Funnel, to unpack how marketing intelligence actually works in a world where data is partial, journeys are fragmented, and the old models don’t hold. Since founding Funnel in 2014, Fredrik has grown the company into a platform that supports over 2,600 brands and handles reporting on more than 80 billion dollars in annual digital spend. That scale gives him a front-row seat to the questions every CMO and CFO are asking right now. Fredrik explains why last-click attribution didn’t just become inaccurate. It became misleading. With tracking capabilities stripped down and user signals disappearing, the industry has had to move toward modeled attribution and real-time optimisation. That only works if your data is clean, aligned, and ready for analysis. Funnel’s platform helps structure campaigns upfront, pull data into a unified model, apply intelligence, push learnings back into the platforms, and produce reporting that makes sense to the wider business. This isn’t about dashboards. It’s about decisions. We also talk about budget mix. Performance channels may feel safe, but Fredrik points out they are also getting more expensive. When teams bring brand and mid-funnel activity back into the measurement framework, the picture often changes. He shares how Swedish retailer Gina Tricot grew from 100 million to 300 million dollars in three years, in part by shifting spend to brand and driving demand earlier in the customer journey. That move only felt safe because the data supported it. AI adds another layer. With tools like Perplexity reshaping search behavior and the web shifting from links to answers, click-throughs are drying up. But it’s not the end of visibility. Content still matters. So does structure. The difference is that now your reader might be an AI model, not a human. That requires a rethink in how brands approach discoverability, authority, and engagement. What makes Funnel interesting is that it doesn’t stop at analytics. The platform feeds insight back into action, reducing waste and creating tighter loops between teams. It also works for agencies, which is why groups like Havas use it across 40 offices through a global agreement. If you're tired of attribution theatre and want to understand what marketing measurement looks like when it’s built for reality, this episode gives you a clear, usable view. Listen in, then tell me which decision you're still guessing on. Because marketing can be measured. Just not the way it used to be. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

I invited Egil Østhus to unpack a simple idea that tends to get lost in release day pressure. DevOps gets code to production quickly, but users experience features, not pipelines. Egil is the founder of Unleash, an open source feature management platform with close to 30 million downloads, and he argues that the next step is FeatureOps. It is a mindset and a set of practices that separate deployment from release, so teams can place code in production, light it up for a small cohort, learn, and only then scale out with confidence. Here is the thing. Controlled rollouts, clear telemetry, and fast rollback reduce risk without slowing teams down. Egil explains how FeatureOps connects engineering effort to business outcomes through gradual exposure, full stack experimentation, and what he calls surgical rollback. Instead of ripping out an entire release when one part misbehaves, teams can disable the offending capability and keep the rest of the value in place. It sounds straightforward because it is, and that is the point. Less drama, more learning, better results. We also talk about culture. When releases repeatedly disappoint, trust between product and engineering frays. Egil shares examples where Unleash helped a hardware and software company move from blame to shared ownership by making rollout plans visible and collaborative. Another client, an ERP vendor, discovered that early feedback from a small group of users allowed them to ship a leaner version that met the need without months of extra scope. That is how FeatureOps saves money and tempers expectations while still delighting customers. AI enters the story too. Code is shipping faster, but reliability can wobble when autogenerated changes move through pipelines. Egil sees feature management as a practical control plane for this new reality. Feature flags provide a real time safety net and, if needed, a kill switch for AI powered functionality. Teams can keep experimenting while protecting users and brand equity. If you want to move beyond release day roulette, this episode offers a practical playbook. We cover privacy first design, open source flexibility, and why metadata from FeatureOps will help leaders study how their organizations truly build. To learn more, visit getunleash.io or search for Unleash in your favorite tool, then tell me how you plan to measure your next rollout’s impact. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA

I invited Atalia Horenshtien to unpack a topic many leaders are wrestling with right now. Everyone is talking about AI agents, yet most teams are still living with rule based bots, brittle scripts, and a fair bit of anxiety about handing decisions to software. Atalia has lived through the full arc, from early machine learning and automated pipelines to today’s agent frameworks inside large enterprises. She is an AI and data strategist, a former data scientist and software engineer, and has just joined Hakoda, an IBM company, to help global brands move from experiments to outcomes. The timing matters. She starts on the 18th, and this conversation captures how she thinks about responsible progress at exactly the moment she steps into that new role. Here’s the thing. Words like autonomy sound glamorous until an agent faces a messy real world task. Atalia draws a clear line between scripted bots and agents with goals, memory, and the ability to learn from feedback. Her advice is refreshingly grounded. Start internal where you can observe behavior. Put human in the loop review where it counts. Use role based access rather than feeding an LLM everything you own. Build an observability layer so you can see what the model did, why it did it, and what it cost. We also get into measurements that matter. Time saved, cycle time reduction, adoption, before and after comparisons, and a sober look at LLM costs against any reduction in FTE hours. She shares how custom cost tracking for agents prevents surprises, and why version one should ship even if it is imperfect. Culture shows up as a recurring theme. Leaders need to talk openly about reskilling, coach managers through change, and invite teams to be co creators. Her story about Hakoda’s internal AI Lab is a good example. What began as an engineer’s idea for ETL schema matching grew into agent powered tools that won a CIO 100 award and now help deliver faster, better outcomes for clients. There are lighter moments too. Atalia explains how she taught an ex NFL player the basics of time series forecasting using football tactics. Then she takes us behind the scenes with McLaren Racing, where data and strategy collide on the F1 circuit, and admits she has become a committed fan because of that work. If you want a practical playbook for moving from shiny demos to dependable agents, this episode will help you think clearly about scope, safeguards, and speed. Connect with Atalia on LinkedIn, explore Hakoda’s work at hakoda.io, and then tell me how you plan to measure your first agent’s value. ********* Visit the Sponsor of Tech Talks Network: Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA