Tenable On Agentic AI, Exposure Gaps, And The Next Big Security Risk

What happens when AI starts moving faster than the people meant to control it? In this episode, I'm joined by Bernard Montel, Field CTO EMEA at Tenable, for a timely conversation about the AI risks many organizations may be underestimating. Bernard believes we are heading toward a defining AI accident and that the first major incident may come through speed, scale, and unintended consequences rather than a malicious attack. We talk about why so many companies feel pressure to adopt AI at pace, while visibility, governance, and control struggle to keep up. Bernard describes this moment as "driving faster than we can steer," and explains why shadow AI, overprivileged identities, cloud misconfigurations, and exposed AI projects are already creating real business risk. The conversation also looks at agentic AI and why giving systems the ability to take action changes the security equation. A chatbot giving a wrong answer is one problem. An AI agent making flawed decisions, leaking data, or interacting with industrial systems is something very different. Bernard also shares why AI can become a distraction from the security basics that still matter, including cloud security, identity, exposure management, and vulnerability remediation. Attackers may be using AI to move faster, but many of the weaknesses they exploit remain painfully familiar. We also discuss Tenable's new agentic AI framework, announced during RSA, and how the company is using AI to help security teams respond at machine speed while reducing exposure across IT, cloud, OT, identity, and AI environments. For business and security leaders, this episode offers a clear warning and a practical takeaway. AI adoption is no longer a future conversation, but control, governance, and exposure management need to move with it. How prepared is your organization for an AI incident caused by accident rather than attack? Share your thoughts. Useful Links Connect with Bernard Montel, Field CTO EMEA at Tenable Learn More About Tenable Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

What if the smartest climate technology strategy isn't about inventing something new, but rethinking the buildings we already spend 90% of our lives in? In this episode of Tech Talks Daily, I sit down with Ben Stapleton, Executive Director of US Green Building Council California or USGBC California, to talk about why buildings sit at the center of sustainability, resilience, and community well-being. From energy use and air quality to wildfire resilience and climate justice, Ben makes a compelling case that the built environment may be one of the most practical places to create real change. Ben and his team launched the California Building Performance Hub, a platform designed to help building owners, operators, and policymakers understand how to improve building performance through policy guidance, technical resources, rebates, and even an AI-powered assistant trained on building codes and compliance pathways. We discuss how this platform is helping accelerate California's move toward healthier, lower-energy, high-performance buildings and why AI is becoming a useful sidekick rather than a replacement for human expertise. Our conversation also moves beyond technology and into something far more human: community. Ben shares how sustainability only works when people feel they have both awareness and agency. From helping low-income communities understand electrification and indoor air quality, to taking a "BuildSMART Trailer" filled with real building materials into neighborhoods so people can touch and understand the future of their homes, this episode is a reminder that climate progress starts with education and trust. We also talk about wildfire resilience in California, where simple low-cost building decisions can dramatically reduce fire risk while also improving energy efficiency and health outcomes. Ben explains why many of the solutions already exist, and why the challenge is often less about invention and more about implementation, policy, and long-term thinking. For business leaders, public sector teams, and anyone thinking about the future of cities, this episode offers a fresh perspective on sustainability as both a financial and human opportunity. Healthier buildings create healthier people, and healthier people create stronger businesses. Is the future of climate action already built around us, and are we finally ready to look up and see it? I'd love to hear your thoughts. Useful Links Connect with Ben on LinkedIn CA Building Hub USGBC California Follow USGBC California on LinkedIn   Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.  

How is AI really changing professional services work today, beyond the demos, predictions, and LinkedIn hype? In today's episode, I'm joined by DJ Paoni, CEO of Certinia, and Jay Laabs, CEO of Spaulding Ridge, to discuss how AI is already being used inside services organizations to improve project delivery, resource planning, workforce optimization, and client outcomes. DJ shares what a hybrid workforce of people and AI agents looks like in practice. Rather than thinking of AI as a search bar, he explains why services firms should think of agents as specialized colleagues that can handle repeatable tasks, draft project blueprints, support configuration work, and help teams deliver faster without losing the human judgment clients still rely on. Jay brings the adoption reality from the consulting front line. He explains why the biggest barrier is rarely the technology itself, but the processes, incentives, data models, and cultural habits wrapped around it. The most successful firms are moving away from broad experimentation and focusing on specific business problems where AI can deliver clear ROI. We also discuss the risks of rushing in without a plan. From disconnected AI agents creating a "spaghetti web" across the enterprise to teams automating broken workflows, DJ and Jay share practical warnings for leaders who want AI to create value without adding another layer of complexity. This episode offers a clear look at what is working, what is failing, and what needs to change as professional services firms rethink billable hours, project economics, and the role of human expertise in an AI-enabled workplace. Are services firms ready to measure success by outcomes rather than hours, and what will that mean for the future of consulting?

How much revenue is lost because the systems behind pricing, quoting, billing, and finance still do not talk to each other properly? In today's episode, I'm joined by Tina Kung, CTO and Co-Founder of Nue, the quote-to-revenue platform helping AI and SaaS companies rethink how they sell, bill, and grow. Tina brings more than two decades of experience across enterprise software, CPQ, billing, and revenue operations, with previous roles at Oracle, Zuora, SteelBrick, and Salesforce. Tina shares the story behind Nue and why she saw a growing gap between the systems that handle selling and the systems that manage revenue. As SaaS companies move from traditional subscriptions into usage-based pricing, credit burn-down models, product-led growth, partner channels, and enterprise sales, the old way of stitching together tools with manual work and spreadsheets starts to break down. We discuss how AI is changing go-to-market operations and why transaction-level intelligence matters. Tina explains how Nue connects quoting, billing, usage, and revenue data into a single system, then applies AI so teams can understand what is happening, spot opportunities, and take action faster. One of the standout stories is OpenAI, which rolled out Nue in just eight weeks to support the rapid growth of its ChatGPT Enterprise business. Tina shares what that process revealed about the speed of modern AI companies and why flexible revenue infrastructure is now a serious advantage. We also talk about the rise of agentic AI in revenue operations, from creating quotes and orders to handling subscription changes and surfacing upsell opportunities. As the SaaS model comes under pressure from AI, Tina offers a practical view of what needs to change behind the scenes for companies to stay competitive. If SaaS is entering a new chapter, are your revenue systems ready for how customers now buy, use, and pay for software?  

Can AI really remove emotion from investing, or does human judgment still matter most when money is on the line? In today's episode, I'm joined by Jack Fu, Founder and CEO of Draco Evolution, a company using AI, quantitative models, and decades of market experience to help investors make smarter and more disciplined decisions. Jack's journey began during the 2008 financial crisis while working as a financial advisor at Union Bank of California, where watching investors lose life-changing amounts of money completely reshaped how he thought about risk, discipline, and long-term wealth creation. That experience led him to focus on one simple principle: avoiding big losses matters just as much as chasing returns. From managing assets for family offices and institutional clients to leading major investment operations across the Asia-Pacific region, Jack built his career around protecting capital first and helping investors stay in the market long enough to benefit from long-term growth. We explore how Draco Evolution is bringing institutional-level investment tools to everyday investors through AI-powered ETFs and a more dynamic approach to portfolio management. Jack explains how ETFs actually work, why they have become such a popular choice for investors, and the important difference between investing in AI companies and using AI itself to manage investment decisions. We also discuss the future of robo-advisors and why the next generation will move far beyond static questionnaires and occasional portfolio rebalancing. Jack shares why he believes the future lies in systems that adapt continuously to market conditions and investor behavior, creating something far more personal and responsive. From algorithmic trading and AlphaGo to today's world of agentic AI, Jack offers a practical perspective on how technology is changing finance without replacing human oversight. He also shares why investors should treat AI as an enhancement tool rather than blindly trusting every recommendation. If you've ever wondered how AI is changing investing, what makes AI-driven ETFs different, or how to stay disciplined in unpredictable markets, this conversation offers plenty of insight. How much would you trust AI to help manage your financial future, and where would you still want a human in the loop?

What does it actually take to move from AI experiments and pilot projects to real business outcomes that customers can feel? At Adobe Summit in Las Vegas, I sat down with Neil Letchford, Vice President of Digital Engineering at Virgin Atlantic, to talk about how the airline is doing exactly that. While many organizations are still debating ROI, governance, and where agentic AI fits into the customer journey, Virgin Atlantic has already launched an AI concierge that is actively helping customers book holidays, find answers faster, and create a smoother travel experience from the first search to stepping onboard the aircraft. Neil shared how a proof of concept built in just two months evolved into a live multi-agent system that now helps customers plan trips, book holidays, and move seamlessly between digital channels and human support when needed. We talked about the importance of "knowledge over data," why observability and model evaluation matter when deploying AI at scale, and how the team built trust internally by focusing on real customer pain points rather than chasing shiny technology trends. What stood out most was how Virgin Atlantic has kept its famously human customer experience at the center of every decision. This is not automation for the sake of efficiency. It is about using AI to strengthen relationships, preserve brand personality, and create better outcomes for both customers and the business. From personalized holiday planning to agent-to-agent interactions that may soon redefine travel booking, this conversation offers a practical look at what happens when AI moves beyond theory and starts delivering value today. If you want to understand what agentic AI looks like in the real world, and why the companies moving early may gain a serious advantage, this is an episode you do not want to miss. What would AI need to do in your business before you would trust it to take the lead? Useful LInks Connect with Neil Letchford Learn more about Adobe Brand Concierge Check out Virgin AI Concierge Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

How has streaming changed from simply delivering video to becoming one of the most important business engines behind sports, media, and customer engagement? In this episode of Tech Talks Daily, I sit down with Filippo de Salazar, who leads the Brightcove team following its acquisition by Bending Spoons, to talk about how the company is evolving and where the future of streaming is heading next. With more than 20 years in the industry and powering over a billion streams every week, Brightcove has become the invisible backbone behind many of the broadcasters, publishers, sports networks, and enterprise video experiences we all rely on without ever thinking about the technology behind them. Filippo shares how the past year has accelerated Brightcove's product velocity, with major releases including AI capabilities, live 4K, live DRM, and automation tools that help customers move faster without compromising reliability. While the business has gained speed, he explains that Brightcove's focus on stability and customer obsession remains unchanged, especially when customers depend on mission-critical video workflows that leave no room for failure. We also unpack how AI is moving beyond hype and creating measurable value for broadcasters today. From automatically detecting live sports highlights and clipping them for instant social sharing, to improving ad placement relevance, generating live captions, and translating content into more than 70 languages, AI is reshaping both operational efficiency and revenue generation. Filippo explains how tools like Brightcove's Universal Translator and Metadata Optimizer are helping broadcasters unlock ROI that simply was not possible before. Our conversation also covers personalized streaming, fan engagement, cloud-native automation, and the rise of FAST channels. We discuss why sports audiences now expect low latency, instant highlights, and highly personalized viewing experiences, and how broadcasters must balance those expectations with the realities of infrastructure costs and monetization pressure. Filippo also shares why discoverability has become one of the biggest battlegrounds in streaming, with some viewers spending more time searching for content than actually watching it. Looking ahead, Filippo outlines the three trends he believes will define the next phase of streaming: intelligent automation, stronger monetization discipline, and managing fragmented viewing behaviors across live, subscription, ad-supported, and FAST environments. As media companies try to unify these experiences without adding complexity, platforms like Brightcove are becoming increasingly central to how modern video businesses operate. What does the future of streaming really look like when AI, automation, and personalization all collide, and are broadcasters ready for what comes next? Useful Links Connect with Filippo de Salazar Learn more about Brightcove following its acquisition by Bending Spoons Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

What happens when the biggest breakthrough in AI isn't a flashy new tool, but finally getting rid of 450 spreadsheets? Recording live from Qlik Connect, I sat down with Ed Dunger from HelloFresh to talk about what operational transformation actually looks like inside one of the world's most complex supply chain environments. Because when your business depends on forecasting demand, managing perishable food, coordinating deliveries, and making sure customers receive the right box at the right time, small inefficiencies quickly become expensive problems. Ed leads operational technology and analytics enablement across global teams at HelloFresh, covering everything from forecasting through to final-mile logistics. In this conversation, he shares how the company moved away from hundreds of disconnected Google Sheets and manual processes toward a near real-time, data-driven operating model that gives teams faster, clearer, and more reliable decision-making. We talk about the practical reality of replacing over 450 spreadsheets, building trust in the data, and creating systems that operational teams actually want to use. Ed explains why this was a two to three year journey rather than an overnight transformation, and how early wins, like predicting waste before it happened, helped build confidence across the business. We also explore how HelloFresh is using predictive AI to improve exception management when deliveries fail. From triggering recovery boxes faster to improving customer communication when something goes wrong, the focus is not on AI for the sake of AI, but on solving real problems that directly affect customer experience. There is also a valuable lesson here for any business trying to move from experimentation to operational reality. Start small, build trust gradually, and focus on solving one problem well before trying to transform everything at once. So as more organizations race to adopt AI, are we sometimes overlooking the simple operational fixes that create the biggest impact? And is real transformation less about the technology itself, and more about how people learn to trust it? Join me for a practical and honest conversation from Qlik Connect, and let me know your thoughts. Are you still managing around old processes, or are you building systems people can truly rely on? Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

How do you turn complex regulatory data into something customers can actually use, trust, and act on? Recording live from Qlik Connect, I sat down with Robin Astle, Head of Qlik Analytics at Reconomy Group, to explore how data is becoming far more than an internal reporting tool. In Robin's world, it has become a product in its own right, helping some of the world's largest retailers manage compliance, reduce costs, and make smarter sustainability decisions. Robin works across Valpak, a business at the center of environmental compliance and packaging regulation, supporting over 100 enterprise customers across the UK, Europe, and the US. From packaging taxes and recycling targets to government submissions and sustainability reporting, the amount of data involved is enormous, and the stakes are high. In our conversation, Robin shares how the Valpak Insight Platform evolved from manual SQL extracts and spreadsheets into a fully scaled cloud-based analytics platform ingesting millions of rows of data every day. We discuss how that transformation helped reduce onboarding from weeks to days, created up to 90% time savings on CSR and analytics requests, and helped customers reduce compliance costs by up to 15%. We also explore the launch of PackChat, which uses natural language queries to help customers interact with compliance and packaging data without needing deep technical knowledge. Robin explains why context is everything when dealing with environmental regulations, and why building trust in the data model is essential before AI can deliver real value. There is also a bigger conversation here around how businesses can use data to serve customers directly, not just support internal teams. From OEM partnerships and cloud automation to scaling AI-powered services across global markets, Robin shares what it takes to turn data into a revenue-generating service. So as more organizations look to unlock value from the information they already hold, are we still thinking too narrowly about what data can do? And could your greatest untapped product actually be the data sitting inside your business today? Join me for a fascinating conversation from Qlik Connect, and let me know your thoughts. Are you still using data for reporting, or are you starting to think about it as a product? Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

How do you turn powerful AI technology into something customers actually trust, adopt, and use? Recording live from Qlik Connect, I sat down with Mary Kern, Vice President of Analytics Product Go-To-Market at Qlik, to explore one of the most overlooked challenges in enterprise AI today. Not building the technology, but making it real for the people expected to use it every day. Because while AI innovation is moving at incredible speed, many organizations are still struggling with a much more practical question. How do you move from exciting product announcements and pilot projects to real adoption, measurable outcomes, and business value? In our conversation, Mary shares how Qlik is approaching that challenge by shifting the focus away from shiny features and toward outcomes that matter. We discuss why agentic AI is creating so much excitement, why customers are often much closer to operationalizing it than they realize, and how years of investment in data quality, governance, and analytics are now becoming the foundation for what comes next. We also talk about the growing importance of trusted data and context, especially as AI moves from generating insights to influencing decisions and actions. Mary explains why simply adding a large language model on top of existing systems rarely works, and why organizations need to think more carefully about how AI is trained, governed, and integrated into the environments where people already work. There is also a refreshingly honest conversation around cost, experimentation, and imperfection. Mary makes the case that organizations should start now, even if the data is not perfect, because using AI often reveals where the real gaps are and what needs to improve next. So as businesses look ahead to the next 12 months, what will separate those who successfully scale AI from those still stuck in pilot mode? And are we spending too much time talking about the technology, and not enough time understanding how people will actually use it? Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Is your organization closing the gap between AI capability and real adoption, or is that still the biggest challenge?