Tech Talks Daily

What happens when the biggest AI security risk isn't the technology itself, but the people using it?
In this episode, I sit down with Cormac Whelan, CEO of Nitro Software, to discuss why organizations need to rethink their approach to AI adoption. With research showing that 68% of C-suite executives are bypassing approved AI tools in favor of their own, we explore how an "ask forgiveness, not permission" culture is creating new security and compliance challenges for businesses around the world.
Cormac shares why successful AI adoption begins with business outcomes rather than the latest model or headline-grabbing announcement. Drawing on his experience leading Nitro and previously building an AI company acquired by Apple, he explains why AI should be an enabler rather than the destination, and why organizations that focus on trust, transparency, and practical business value will ultimately pull ahead.
Our conversation also looks at why documents, contracts, PDFs, and e-signatures have become some of the most overlooked parts of the enterprise AI conversation. As AI systems increasingly interact with sensitive business information, protecting document workflows is becoming just as important as securing networks and endpoints.
We also discuss how European privacy standards are becoming a competitive advantage rather than simply another compliance requirement, how to separate genuine AI innovation from expensive security theater, and why AI should quietly improve the way people work instead of becoming the center of attention.
If you're trying to balance AI innovation with security, governance, and business value, this conversation offers practical advice without getting lost in the hype. After listening, I'd love to hear your thoughts. Is your organization focusing on outcomes first, or is it still chasing the latest AI headline?

What if the biggest weakness in cybersecurity isn't the technology, but the way defenders communicate with each other?
Cybercriminals openly exchange techniques, vulnerabilities, and attack methods, constantly learning from one another. Meanwhile, many organizations remain reluctant to share details of breaches, investigations, or lessons learned. In this episode, I sit down with Lee Sult, Chief Investigator at Binalyze, to discuss why that imbalance is creating an advantage for attackers and what the industry can do to change it.
Drawing on almost two decades in digital forensics and incident response, including work with Palantir, law enforcement agencies, and government organizations, Lee explains why cybersecurity should learn from industries such as aviation and emergency services, where every major incident becomes an opportunity for everyone to improve. We discuss why incident response needs to move beyond reacting to alerts, how proactive threat hunting can reduce attacker dwell time, and why repeatable investigation processes are becoming just as important as the security tools themselves.
We also explore the growing influence of AI, not because it is making attackers dramatically smarter, but because it is lowering the barrier to entry and increasing the volume of attacks security teams must handle. Lee shares why automation is becoming essential for investigators, how organizations can move from hours to minutes when responding to threats, and why cybersecurity is steadily becoming a boardroom issue rather than simply an IT concern.
If cybersecurity is truly an information war, what would happen if defenders became just as collaborative as the attackers they face every day? After listening, I'd love to hear your thoughts. Should organizations be more open about cyber incidents if it helps strengthen security for everyone?

What happens when a consulting company decides that adding AI to existing workflows isn't enough?
In this episode, Mal Vivek, CEO and co-founder of zeb, joins me to discuss the launch of Substrate, an AI-native operating architecture that challenges many assumptions about enterprise consulting, software delivery, and AI adoption.
Rather than layering AI onto legacy processes, zeb made the bold decision to scrap its own operating model and rebuild the company from the ground up with AI at its core. The result is Substrate, a system designed to learn from every project it completes, continuously improving through a Plan, Execute, Evaluate operating loop while helping organizations move from experimentation to measurable business outcomes.
Our conversation goes far beyond another AI product announcement. Mal explains why so many organizations remain trapped in what she calls "pilot purgatory," investing heavily in AI without producing measurable returns. We discuss why treating AI as an assistant often limits its potential, and why businesses may need to rethink their organizational structures, workflows, and even leadership models if they want AI to become part of their operational foundation.
We also explore one of the most talked-about aspects of zeb's business model: a 100 percent outcome guarantee. Instead of charging for time or software licenses, zeb only gets paid when agreed business outcomes have been delivered. That raises interesting questions about accountability, risk, and whether the traditional consulting model still makes sense in an era where AI can dramatically compress delivery timelines.
Mal also shares why zeb gives customers ownership of their own version of the Substrate engine instead of locking them into a traditional SaaS subscription, how AI changes the relationship between technology vendors and their customers, and why she believes future organizations will become flatter, faster, and increasingly focused on builders rather than management layers.
If you're a technology leader trying to move beyond AI proofs of concept, or a business executive searching for a practical path to measurable AI value, this conversation offers plenty of fresh thinking on what AI-native organizations could look like over the next few years.
Can businesses continue adapting yesterday's operating models for tomorrow's technology, or is it time to rebuild from the ground up? I'd love to hear where you stand after listening to this episode.

Can organizations really call themselves AI-ready if their data foundations still have gaps?
In this episode of Tech Talks Daily, I sit down with Dave Shuman, Chief Data Officer at Precisely, to discuss the findings from the company's latest State of Data Integrity and AI Readiness Report. Drawing on insights from more than 500 senior IT leaders across the US and Europe, Dave explains why many organizations are confident in their AI readiness while simultaneously identifying infrastructure, data quality, and governance as their biggest obstacles.
Our conversation focuses on what Dave describes as the AI data integrity gap, the growing disconnect between ambitious AI initiatives and the quality, consistency, and context of the data powering them. We explore why successful AI projects often perform well in controlled pilot environments before struggling when deployed at scale, and why many organizations continue to underestimate the importance of data lineage, semantic layers, governance, and observability.
Dave also shares why he believes data governance and AI governance should be treated as a single discipline rather than separate initiatives. We discuss how businesses can move beyond vanity metrics such as token usage and agent counts to focus on outcomes that genuinely matter, including revenue growth, cost reduction, customer experience, and risk management.
As the conversation turns to the future of agentic AI, Dave offers a practical perspective on what autonomous systems will require of organizations and why trust in data will become increasingly important as AI assumes greater responsibility behind the scenes.
If your organization is investing heavily in AI and looking for measurable business value, this episode offers a timely reminder that successful AI strategies begin long before the first model is deployed. They begin with data integrity. Based on Precisely's latest research, Dave explains why companies making progress are focusing less on the latest AI tools and more on laying the foundations that enable those tools to deliver reliable outcomes.
What role does data integrity play in your organization's AI strategy, and are you confident your data is truly AI-ready?

How prepared are businesses for a new wave of attacks targeting the apps, APIs, and AI systems now powering digital growth?
In this episode, I speak with Richard Meeus from Akamai Technologies about the latest findings from Akamai's State of the Internet report, with a focus on apps, APIs, and DDoS activity across EMEA.
Richard explains why APIs have become such an attractive target for attackers, especially as AI adoption accelerates. We discuss the sharp rise in API abuse, the growing use of automation to industrialize attacks, and why many organizations still lack visibility into the APIs exposing sensitive data.
We also examine the rise in layer 7 DDoS attacks, how attackers are combining multiple techniques to distract defenders, and why sectors such as retail and manufacturing are facing growing pressure. Richard also shares his view on the geopolitical forces shaping DDoS activity and why hacktivist groups continue to use these attacks as a public statement.
Another major theme is the security risk around AI chatbots. As more organizations deploy chatbots to improve customer service, Richard explains how overly helpful AI systems can expose data, respond to prompt injection attempts, or create new blind spots if the right controls are missing.
But this conversation is not all about risk. Richard also explains why AI can help defenders strengthen visibility, improve testing, analyze logs faster, and support more proactive security strategies.
So, as businesses race to adopt AI and modern digital services, are they paying enough attention to the APIs and infrastructure sitting underneath it all? Share your thoughts.