Claroty Nexus

When ChatGPT dropped more than three and a half years ago, there was a gasp of excitement from many, while for others, there was a healthy dose of trepidation. Most alarmingly, the release raised concerns over loss of control—the risk that an AI system diverges from its authorized constraints.

ICS Advisory Project founder Dan Ricci is the guest for Episode 4 of Nexus Digest guest. Dan discusses a recent article he wrote for Nexus titled “From Inventory to Insight: Turning OT Visibility into Concrete Risk Reduction.” Dan describes the need to distinguish between asset lists and actual asset inventories, what those differences are, and how to make the most of the information made available.

Providence's cybersecurity team protects 51 hospitals and more than 1,000 clinics. At that scale, cybersecurity has to be threat-informed, risk-driven, and operated as a business enabler: measurable, adaptive, and increasingly augmented by artificial intelligence (AI). That last part, AI-augmented, is where a lot of the conversation is right now.

We’re well into the journey of a rapid modernization of manufacturing. Smart sensors and integrated systems permeate factories and, along with other internet-of-things (IoT) assets, they feed large data lakes that can be parsed in seconds by artificial intelligence for predictive analytics, optimization and other efficiencies that mark the current industrial revolution.

The threat landscape is intensifying: The latest Swisscom Cybersecurity Threat Radar 2026 shows how AI-driven attacks, targeted supply chain manipulation, and hybrid attacks are dissolving the boundaries between digital and physical security. This development requires a holistic view of cybersecurity. True operational resilience is only achieved through the combination of IT, OT, and physical protection.

When I talk to chief information security officers (CISOs) and engineering leaders about operational technology (OT) asset visibility, the first misconception I address is the idea that an asset list equals an asset inventory. Many organizations believe they see the complete picture because a passive monitoring tool produced a list, or their SCADA database, historian, or engineering documentation shows what should be present.

Jon Holzbauer, OT systems manager at Silgan Containers, is featured in Episode 3 of Nexus Digest. Jon discusses an article he wrote about the skills gap between IT security teams and OT asset operators as these two distinct operational disciplines converge. Jon describes the challenges and conflicting priorities that emerge in converged environments, and how to navigate those.

The phrase "predictive resilience" is used loosely. In an operational technology (OT) context, it means something specific: the ability to anticipate where the next disruption is likely to land — by sector, by asset class, by attack pattern — early enough to change posture before it arrives. It is not prediction in the sense of forecasting individual incidents. It is prediction in the sense that an experienced operator looks at the weather and decides which units to run hot.

Let’s inject some nuance into the upheaval over Project Glasswing. There’s no denying that autonomous vulnerability discovery and exploit development at this scale is a true inflection point for cybersecurity. Throughout Anthropic’s report on Project Glasswing and the private Claude Mythos preview, there is evidence that this advancement in artificial intelligence has uncovered an unprecedented volume of vulnerabilities—some decades old in ubiquitous software libraries and applications.

Organizations across critical infrastructure, manufacturing, and other industrial sectors face a widening operational technology (OT) cybersecurity skills gap. A challenge driven by the rapid convergence of IT and OT environments. While digital transformation has connected industrial systems to enterprise networks and the cloud, the workforce has not kept pace.