1. Much of the early conversation about AI in cardiac imaging focused on producing sharper images. Where do you see AI’s role going from here? Sharper images still matter, but the bigger shift is in what clinicians can do with them. AI-based imaging is helping cardiology practices move away from identifying isolated blockages and toward…
Q1. The H1 2026 data shows network attack volume fell 79% while novel endpoint malware rose 2,065%. On the surface that looks like good news on one front and a serious problem on another. What is happening in attacker behaviour that produces both numbers simultaneously? Attackers have shifted from spraying to aiming. Mass campaigns generate…
1. Organizations that have stalled on AI pilots often go back and buy better models or different tools rather than fix the data layer underneath. Who in the organization is positioned to make the most accurate diagnosis, though, when the data team and the AI team are separate functions with separate incentives? There are teams…
1. Analytics, forecasting, and measurement have been the core of data investment for twenty years. You’re describing a layer that sits above all of that. What broke in the existing layer that created the need for something beyond it? A decision layer is the part of the stack that weighs the options in front of…
1.MiCa’s deployment at Tallinn Airport marks a shift from testing autonomous mobility to using it in day-to-day airport operations. What made this transition possible, and what were the most significant challenges along the way? The transition was possible because we approached it as a gradual operational and safety-validation process rather than a single technological demonstration….
1. There is a difference between a model that performs well on a benchmark dataset and one that can be trusted to inform a clinical decision. Where does that gap show up and what did you have to change about how you were measuring performance? In radiology AI, standard benchmark datasets typically measure how well…
1. You’ve worked with organizations across different industries. Are you seeing common obstacles to AI adoption regardless of sector, or do the biggest challenges vary from industry to industry? I do see some common obstacles across industries. The biggest one is that companies often start with the technology rather than the problem they’re trying to…
1. Context without governance produces confident answers built on the wrong information. Context with governance that is too restrictive produces a system that can’t do useful work. Where is that line and who in an organization should be drawing it? Ans : The line is drawn by the same rules that already govern the work….
1. Security teams know their tooling is disconnected but can’t fix it quickly because of procurement cycles, legacy contracts, and organizational silos. What’s your honest assessment of how long most enterprises can afford to operate that way given where the threat landscape is heading? The exposure window has closed. Unit 42 has observed attackers weaponizing…
