Financial networks once defined by castle-and-moat defenses now resemble airports with countless gates, where every badge, kiosk, and jet bridge becomes a potential entry point that adversaries can quietly exploit without triggering alarms. As cloud services spread across trading, risk modeling,
Boardrooms are louder now as AI PC pilots give way to rollouts that promise faster work, lower latency, and tighter data control while forcing hard choices on budgets, skills, and governance. That shift has pushed the conversation from curiosity to execution: who gains, how fast, and at what cost.
A breached database rarely began with sophisticated malware but with a rushed upload, a misaddressed share, or a copied token from a trusted user in a hurry, and the most consequential risks emerged when everyday work collided with fragmented tools, blurred boundaries, and AI woven into routine
For enterprises that have stretched chatbots to their limit and still lack reliable, governed automation, the unveiling of a full-stack platform for autonomous agents landed less like a demo and more like a blueprint for production systems built to survive real traffic, real policies, and real
Marcus, thanks for having me. I live at the intersection of cloud platforms and real-world engineering, which means I’ve seen the upside of AI—faster code, tighter feedback loops—alongside the rising tide of shadow AI. With half of workers already using unapproved AI and over 70% in the UK doing
Regulators did not wait for collaboration vendors to catch up, and UK enterprises with cross-border exposure increasingly demanded unambiguous proof that meeting recordings, chat logs, call metadata, and AI outputs stayed within national boundaries. That pressure culminated in a notable change:
