3461: AI in Schools and the Cybersecurity Risks Behind It

What happens when classrooms become laboratories for artificial intelligence? As AI tools find their way into schools, from lesson planning to student assessments, educators and parents are wrestling with how to balance innovation and security. In this conversation, I sit down with Jurgita Lapienytė, Chief Editor at Cybernews, to unpack how AI adoption in education is reshaping learning, privacy, and the safety of our youngest digital citizens. Jurgita brings a rare dual perspective as both a technology journalist and a mother. We explore how AI’s growing influence could improve access to knowledge while eroding fundamental cognitive skills if introduced too early or without balance. She compares today’s reliance on AI to the way GPS changed navigation, convenient but potentially disorienting when overused.  Together, we look at how schools can encourage analog learning before turning to technological shortcuts and why teacher training is crucial for building true tech readiness. But beneath the excitement lies a darker reality. With 82 percent of K-12 schools hit by cyber incidents in the last 18 months, education is fast becoming one of the most targeted sectors. Jurgita explains how AI is supercharging attacks from phishing to deepfakes and why schools must view data protection as an essential part of innovation rather than an afterthought. We discuss the growing risks around student data, the ease with which even innocent photos can be exploited, and why privacy policies need a complete rethink before more AI tools enter the classroom. This episode isn’t about rejecting technology, it’s about using it responsibly. Jurgita’s insights remind us that AI’s value in education depends on how thoughtfully it’s implemented and how prepared we are to protect the people it’s meant to serve. So what does a secure classroom really look like in the age of AI, and how can schools, policymakers, and parents work together to create one? I’d love to hear your thoughts, how should we balance innovation with safety in our children’s digital future?

This week has reminded me why I love what I do. I have spoken with people from the US, China, Dubai, Bulgaria, and South Africa, and even discovered that one of this show’s regular listeners had made the journey from the Netherlands to be here at GITEX Global. Over five sessions on the AI Stage, I have covered everything from autonomous cars to how AI could help the UN World Food Programme tackle hunger. We have explored how Serbia achieved tenfold growth through AI and how new tools can now verify misinformation simply by checking a video link. But beneath all the tech talk, what has stood out most to me is how technology connects people. In a world that often feels divided, it is refreshing to see how collaboration and shared curiosity can still bridge cultures and spark ideas. It is those moments, when technology brings people together, that remind me why this podcast exists. That spirit of connection is exactly what inspired my quick chat with Iancho Dimitrov from Living Homes, a company based in Dubai but shaped by global perspectives. Originally from Bulgaria, Iancho and his team are building what they call an “AI-native intelligent home,” a home that does not just automate switches but truly understands its inhabitants. From monitoring wellbeing and improving sleep to creating safe, supportive spaces for families across generations, Living Homes is redefining what it means to live smart. So while this conversation might be short, it captures something powerful. It is proof that innovation is not only about hardware or software, it is about empathy, understanding, and the shared drive to build a better way of living. In a week where the world gathered in one city to imagine the future, Iancho’s vision is a reminder that technology works best when it feels human.

What if the next generation of computing was not something you held or wore, but something you looked through? In this special episode recorded live from GITEX Global in Dubai, I speak with Roman Axelrod, founder of EXPANCEO, a deep tech company creating AI-powered smart contact lenses designed to merge augmented reality, biosensing, and what he calls digital superpowers. Roman explains how his company moved from an ambitious idea to becoming the first deep tech unicorn in the Gulf region, now valued at more than 1.3 billion dollars. Over the past five years, his team of physicists and engineers in Dubai has built more than fifteen prototypes and secured a wide range of patents, all aimed at developing what they see as the ultimate interface for AI-driven computing. These lenses can display digital images, measure biological signals such as glucose and intraocular pressure, and may one day eliminate the need for screens altogether. He reflects on the early days of disbelief, when even friends told him to give up, and how perseverance became the deciding factor. For Roman, success meant proving that deep tech innovation is possible outside Silicon Valley. He shares how Dubai’s ecosystem, low taxation, and access to world-class talent helped make that vision real. His story offers practical insight for founders who are told their ideas are impossible until they can show a working prototype. We also explore what this means for the future of human-computer interaction. Roman believes these lenses will help us communicate directly with intelligent systems, turning science fiction into everyday life. His message to entrepreneurs is simple: be stubborn, stay curious, and keep building. Could AI contact lenses redefine computing itself? Listen to the conversation and share your thoughts.

What if the real breakthrough in AI isn’t the model itself, but the data that gives it knowledge? In this episode of Tech Talks Daily, I sit down with Edo Liberty, founder and Chief Scientist of Pinecone, to unpack how vector databases have quietly become the backbone of modern AI infrastructure. We explore why retrieval-augmented generation (RAG) works so effectively out of the box, and why fine-tuning large models often adds complexity without real-world value. Edo shares how Pinecone’s research revealed that different models—from OpenAI to Anthropic—require differently structured context to perform well, a discovery that’s reshaping how enterprises think about AI implementation. As the former Director of Research at Yahoo and AWS, Edo offers a grounded perspective on where the real innovation is happening. He explains how the shift from traditional data structures to vector representations is redefining how machines “know” and retrieve information, creating smarter, context-aware systems. We also touch on his recent transition to Chief Scientist, his excitement for returning to hands-on research, and why he believes the convergence of AI and data represents the defining technological shift of our lifetime. So, what does it mean for developers, business leaders, and anyone building with AI when knowledge becomes an accessible layer of infrastructure? Can we build systems that truly “know” as humans do? Join the conversation, and after listening, I’d love to hear your thoughts—do you think the future of AI lies in the models or in the data that feeds them?

What happens when an AI strategy meets the real-world complexity of healthcare, law, and finance? That’s the challenge at the heart of my conversation with Mark Sherwood, CIO of Wolters Kluwer, a global leader in professional information services. With over three decades in technology leadership across Microsoft, Symantec, and Nuance, Mark brings a rare combination of enterprise depth and hands-on pragmatism to the AI discussion. Mark explains why cloud-native architecture and data governance are the twin foundations of trustworthy AI. He shares how Wolters Kluwer is embedding AI across highly regulated industries—from helping doctors access life-saving insights through natural language queries to giving tax and legal professionals faster, more accurate guidance on complex regulations. Behind the innovation lies a disciplined approach: governing data, managing risk, and building confidence in AI systems that must meet the highest standards of accuracy and compliance. We also explore how to build high-trust, low-friction partnerships between IT and business teams to prevent shadow IT while accelerating digital transformation. Mark offers candid insights into the rise of AI agents, the emerging risks of quantum security, and why he believes that high-quality data is the most valuable currency in digital transformation. His philosophy is simple: speed means nothing without trust, and trust starts with clean, well-governed data. From cloud transformation to the future of AI regulation, this episode offers a grounded look at how global enterprises can scale responsibly in an era where innovation often outruns policy. So as AI becomes inseparable from how professionals think and work, how do we balance speed with stewardship? And are we truly ready for the ethical, technical, and quantum frontiers ahead? Share your thoughts after the episode.

What does it take to build AI that enterprises can actually trust? That’s the question I explored with Nirankush “Kush” Panchbhai, Senior Vice President of Platform Fundamentals at ServiceNow, in a conversation about AI governance, human-centered design, and how the company’s AI Control Tower is reshaping enterprise adoption. Kush describes the AI Control Tower as an “air traffic controller” for AI agents, a central command center that provides visibility, accountability, and governance across every part of an organization’s AI ecosystem. It embeds compliance, legal, and risk workflows directly into the development process, replacing endless approval cycles with automated guardrails that accelerate innovation rather than slow it down. The result is a system where humans remain firmly in control, supported by transparent, explainable AI that acts as a teammate rather than a tool. We also discuss how ServiceNow is helping enterprises move beyond the “POC palooza” of pilot projects that never scale. By treating AI agents as members of a digital workforce—with performance tracking, retraining, and measurable ROI—companies can finally connect investment to real outcomes. Governance, in this context, isn’t a constraint; it’s a catalyst for confidence and adoption. At its core, ServiceNow’s philosophy is about taking the work out of work, not the human out of work. From password resets to process automation, AI is freeing employees to focus on creative, high-value problem-solving while building trust through transparency and accountability. As organizations begin managing both human and digital workforces, one question lingers: can AI governance truly become the accelerator that turns trust into enterprise-scale transformation? And what does it take to ensure AI always serves people, not the other way around? Share your thoughts after the episode.

What if the biggest sustainability challenge in tech isn’t hardware or cloud emissions, but the invisible mountain of unused data sitting in storage? That’s the question driving my conversation with Piero Gallucci, Vice President and General Manager for NetApp UK and Ireland, as we discuss how single-use data is quietly shaping the environmental and financial footprint of enterprise IT. Piero explains that 38 percent of stored data is never used again, yet it continues to consume energy and resources indefinitely. He describes how this digital hoarding—often driven by regulatory caution and the overvaluation of data—has become one of the most overlooked contributors to emissions in modern infrastructure. With the rise of AI accelerating data growth by an estimated 50 percent, the challenge is no longer simply about capacity but responsibility. Through examples such as Aston Martin Formula One and the NFL, Piero outlines how NetApp is helping organizations identify unused data, automate lifecycle policies, and design intelligent, energy-efficient infrastructure that supports both innovation and sustainability. We also explore the tension between AI adoption and environmental impact. As enterprises rush to train new models, Piero argues that smarter data governance, not bigger datasets, is the key to sustainable AI. He highlights the importance of educating teams on the true cost of data—both financial and environmental—and why leaders must build intentional strategies that align performance with purpose. NetApp’s vision is clear: make data management as sustainable as it is powerful. But as AI reshapes how we store and use information, can the tech industry finally balance digital growth with environmental stewardship? And what would your company look like if every byte of data had to justify its existence? Share your thoughts after the episode.

What if the next big leap in business AI isn’t generative at all, but predictive? That’s the question at the heart of my conversation with Zohar Bronfman, CEO and co-founder of Pecan AI, a company helping business teams forecast outcomes with precision and turn historical data into future insights. Zohar explains why he believes predictive AI will deliver far greater enterprise value than the generative models dominating headlines. He points to research showing that most generative AI projects fail to produce ROI, while predictive systems built on a company’s own data can directly improve revenue, reduce churn, and guide smarter decisions. With Pecan’s no-code platform, marketing and operations teams can now create predictive models without needing data scientists—bridging the gap between technical expertise and business execution. Through stories like Little Spoon’s, a direct-to-consumer baby food brand that used Pecan AI to identify and retain at-risk customers, Zohar illustrates how predictive analytics turns data into real business impact. He also shares common mistakes companies make when implementing AI—starting with unclear objectives and misaligned resources—and why success depends on defining the problem before choosing the tool. Looking ahead, Zohar envisions predictive AI as the backbone of every organization, shifting business intelligence from reactive analysis to proactive action. As companies move beyond dashboards and toward dynamic decision-making, predictive insights may soon become as fundamental as spreadsheets. So, if your company could anticipate every challenge before it happened, how different would your strategy look? And are business leaders finally ready to treat predictive AI as core infrastructure rather than a passing trend? Share your thoughts after the episode.

What happens when artificial intelligence meets the everyday heroes of local government? That’s the question driving my conversation with Justin Dennis, co-founder and COO of Urban SDK, a geospatial AI company helping more than 250 North American cities make faster, safer, and smarter decisions. Justin shares how a Smart Cities Challenge from the U.S. Department of Transportation inspired him to co-found Urban SDK in 2018, and why he believes the future of public safety depends on replacing manual data collection with real-time intelligence. From traffic fatalities to hurricane recovery, he explains how the company’s HALO platform gives local leaders and emergency responders the insights they need to act before crises escalate. In a single platform, they can identify dangerous road zones, predict high-risk intersections, coordinate clean-up operations, and rebuild infrastructure based on data rather than guesswork. We also explore how AI is quietly reshaping government operations, from disaster management to traffic enforcement. Justin discusses the challenges of introducing cutting-edge technology into systems that still rely on spreadsheets and siloed workflows. Yet his optimism is clear. He believes governments are beginning to embrace AI not as a buzzword but as a practical tool to save time, resources, and lives. As one Florida community recently reported a 40 percent drop in traffic fatalities, the impact is already measurable. Urban SDK’s story is about technology meeting public service with purpose. So as we enter another year of rapid AI progress, how can data-driven insights continue to empower local leaders to protect citizens and improve quality of life? And what could your city achieve if every decision were powered by real-time intelligence? Share your thoughts after the episode.

Agentic AI is only interesting when it leaves the lab and takes responsibility for real outcomes. In this episode, I reconnect with Raj Koneru, Founder and CEO of Kore.ai, to talk about what that shift looks like inside large enterprises. Raj has been building conversational systems long before chatbots became dinner table conversation, and he is clear about where the action is now. Understanding intent is table stakes. The next frontier is planning, reasoning, and executing tasks through agents that can work across departments, respect policies, and prove their value in minutes, not months. We walk through how Kore.ai frames the stack. There is a layer below the line, where chips, data centers, clouds, and model providers keep advancing at speed. Above the line is where business value shows up. That is where companies design, deploy, and manage agents for customer service, employee productivity, and process automation. Raj describes Kore.ai as the operating system for that upper layer. The goal is simple to say and hard to deliver. Let teams build agents without writing code, plug into whichever models and clouds they prefer, and keep control through governance, security, and measurement. Interoperability runs through this conversation. Kore.ai partners with Microsoft, AWS, and G42’s Inception so customers can pick the environment that suits them. Vendor lock-in is a real anxiety for leaders. Raj’s answer is to be model agnostic, data agnostic, and cloud agnostic, while conforming to open standards. That way, enterprises spend their energy on the agents themselves rather than on maintaining a platform. It is a pragmatic view that reflects what I keep hearing on conference floors. Scale brings its own lessons. At Kore.ai’s volume, latency, accuracy, and security are non-negotiable, and governance is a daily practice rather than a slide in a deck. Raj talks candidly about no-code democratization, where a business user can assemble an agent for a focused task, then graduate to more complex workflows when ready. We also touch on the rise of on-device models in smartphones and where the boundary sits between quick local tasks and heavier actions that still rely on hosted models. If you care about agentic AI that does real work, this one is worth your time. Raj shares a thoughtful take on collaboration, standards, and why leaders should read widely, question everything, and start building with what is available today. Links are in the show notes.