AI is the hottest topic in technology and is already having a huge impact on our economy and people’s daily lives. As its impact continues to grow, it’s essential to explore how AI intersects with railway cybersecurity, a domain where safety, resilience, and operational continuity are essential. This blog post examines the evolving role of AI in the rail sector.

The Changing Balance of Power

In the past, the power of a threat actor was measured by access to resources and time. Nation-states and highly funded groups had the advantage, while smaller actors struggled to gain expertise or build specialized tools. That gap is closing fast. AI gives anyone access to capabilities that once required years of training, insider knowledge, or deep technical skills instantly.

What does this mean for railways? It means attackers no longer need to understand your systems the way they used to. They don’t need to know your protocols, your architecture, or your tools. AI models can fill in the gaps for them, faster than ever.

What Can Be Done with AI Against Rail?

As AI capabilities become more accessible, threat actors can now exploit railway systems in ways that were previously unimaginable, reshaping the attack surface across every layer of the rail ecosystem. Here are a few examples of how AI is already affecting the rail sector.

Understanding Complex Systems

Railways rely on some of the most complex and fragmented systems in the world. Where once significant expertise was required to even understand the basics of how railway networks operate, AI can now explain these systems in detail to anyone who asks the right questions. From signaling to onboard networks, from remote maintenance systems to legacy infrastructure, AI lowers the bar to entry for understanding potential weak points.

Crafting Tools and Scripts

Historically, leveraging rail-specific technologies required access to specialized knowledge. Today, someone with no coding background can use AI to generate scripts, tools, and even attack scenarios against proprietary technologies. AI can write code to manipulate communication protocols, craft payloads, or fuzz poorly documented interfaces. In short, the technical barriers that once protected these systems are disappearing.

Expanding Reconnaissance with AI

Understanding railway operations used to require extensive time spent studying technical manuals, insider documentation, and real-world reconnaissance. With AI, a threat actor can now piece together a comprehensive picture of a network’s structure, vendors, and technologies simply by feeding it public data: LinkedIn job posts, vendor press releases, procurement databases, rail enthusiast forums, and even images from rail fan videos. AI can rapidly analyze train schedules, SCADA terminology, signaling conventions, and map out potential attack surfaces, making what used to take weeks now doable in hours.

Using AI for Defense: A Bridge Between Worlds

To be effective, rail operators must understand how to leverage AI for defense. This spans everything from simplifying governance and compliance processes to improving asset visibility and detection capabilities across fragmented networks. AI is not just a tool for automating security — it’s a bridge between disciplines, helping defenders learn faster and adapt more effectively.

AI can assist rail/OT specialists in ramping up their understanding of IT security practices, while also helping IT teams become more fluent in the nuances of railway technologies. This convergence is critical. Attackers already benefit from AI’s ability to synthesize knowledge across domains. Defenders must do the same.

Used correctly, AI can accelerate the maturity of security programs, highlight unseen vulnerabilities, and strengthen detection where traditional methods fall short. It can help break down silos and ensure that both sides of the organization, OT and IT,  operate with a shared understanding of risk.

So, how has AI impacted your defensive models?