PodChats for FutureCISO: Defending against the invisible

Before Stuxnet, there was fast16: a state-grade sabotage framework that didn’t steal data or crash systems. It silently altered engineering simulations and calculations—poisoning digital twins while everything appeared normal. Now imagine AI giving attackers the power to find such weaknesses in hours. As Singapore expands protections beyond core CII, fast16 is a warning: tomorrow’s breach won’t hold your data hostage. It will corrupt the simulations and AI models you trust to run your plant, bridge, or refinery. And you won’t know until something breaks.In this PodChats for FutureCISO, Vitaly Kumluk, cybersecurity researcher at SentinelOne Lab, SentinelOne, sheds light on what cybersecurity teams need to know and be prepared against this new breed of cyberthreats.1.       What is the role of a researcher in the cybersecurity space?2.       In a nutshell, what is fast16 and what makes it different from other categories of cyber threats?3.       How do we identify which of our engineering simulations, digital twins, and AI training pipelines are most vulnerable to silent output manipulation—and do we have any validation layer that checks results against physical or independent models?4.       How do we detect an attack that changes calculations but leaves systems running normally?5.       What stops an AI-powered attacker from finding a hidden weakness in our simulation software?6.       How do we assess and continuously monitor the integrity of simulation outputs from external partners, cloud-hosted digital twins, or legacy OT environments we cannot directly instrument?7.       Our incident plan covers ransomware. Do they cover a scenario where a state-grade actor has been quietly corrupting our engineering decisions for six months? How do we roll back trust in our own data?8.       If an attacker manipulates a supporting system’s simulation to cause a real-world failure, will current cyber insurance or legal framework treat that as a “breach” or as a “design error”?9.       How do we differentiate between adversarial attacks on the AI’s availability (denial) vs. subtle corruption of its reasoning or output distribution—and which defensive architectures apply?10.   Given what we now understand about fast16, what is your recommendation for moving forward?