Eric Schoedon is a certified forensic expert with nearly 30 years of experience in advanced technology systems, including cybersecurity, artificial intelligence, and automotive engineering. Holding dual ISO/IEC 17024 certifications—an internationally recognized credential for individual expertise—he has worked across sectors where safety, compliance, and trust in digital systems are mission-critical.
Eric was awarded a Doctor Honoris Causa in AI Management in recognition of his interdisciplinary contributions to secure technology deployment. His work bridges technical precision with real-world accountability, especially in the context of connected vehicles and emerging infrastructure. He has provided expert insight in legal proceedings and contributes regularly to discussions around forensic reliability in complex technical environments.
In this interview, he offers insight into the evolving role of automotive forensics and why deep domain expertise is essential in navigating today’s connected and autonomous vehicle landscape.
How did your path from IT and manufacturing lead you into automotive forensics?
I’ve always felt more at home in systems than in titles. My work began in the early 1990s, building and programming computers before most households even had one. From there, I moved into industrial engineering, developing custom technologies for micronization—at a time when precision manufacturing still relied heavily on mechanical control. But digital systems were already changing the landscape, and I found myself more and more involved in the IT backbone behind the machines.
The transition into automotive forensics wasn’t something I planned—it evolved naturally. Modern vehicles are no longer just mechanical products; they are highly integrated, software-driven systems operating in mission-critical environments. From diagnostics to cybersecurity, these systems require a deep understanding of both the code and the context. That intersection—where hardware, software, and safety meet—is where I’ve spent the last several years of my work.
Today, as a certified expert in both IT and automotive engineering, I don’t just analyze what went wrong in a system—I help determine whether digital evidence is reliable, whether a component behaved as expected, and whether a vehicle can be trusted in a legal or regulatory context. That’s the core of automotive forensics: not just understanding the data, but understanding what it means in the real world.
Why should DFIR practitioners care about connected vehicle forensics today?
Because the car is no longer just a car. It’s a data source, a network node, a mobile sensor platform—and increasingly, a target.
Digital Forensics and Incident Response (DFIR) professionals are already trained to think critically about compromised systems, but connected vehicles introduce a different layer of complexity. These machines move through the physical world while continuously generating, storing, and transmitting sensitive information—about the driver, the route, the behavior, and even the environment. Understanding how that data is created, manipulated, and interpreted is becoming essential to many kinds of investigations.
In forensics, the question is never just what happened—it’s how reliable is the evidence? With connected vehicles, the chain of custody begins inside systems that are often proprietary, sometimes opaque, and rarely designed with forensic transparency in mind. If we can’t verify the integrity of that data, we risk drawing conclusions from digital artifacts we don’t fully understand.
So yes—this is a domain that DFIR can’t afford to ignore. Whether you’re investigating a crash, a fraud case, or a cyberattack, vehicle forensics is rapidly becoming part of the digital evidence landscape. Those who understand it will be ahead of the curve.
What types of data are most valuable in vehicle investigations?
That depends entirely on the case—but generally, it’s the data that tells a story with context, not just numbers.
In my experience, the most valuable data is what I call correlated digital behavior: vehicle speed, steering angle, braking patterns, GPS logs, sensor feedback from assistance systems, and the sequence of system alerts or driver warnings. Taken alone, each data point may seem minor. But when analyzed together, they can reconstruct a precise, time-stamped narrative—sometimes even down to the second.
In criminal cases, I’ve seen brake pressure logs help establish intent. In civil litigation, the activation timing of a lane assist system helped resolve liability. And increasingly, metadata from infotainment units or telematics systems—like Bluetooth pairings or navigation history—can be surprisingly revealing.
What makes this field especially challenging is that every manufacturer stores and structures this data differently. There is no true standard yet. That’s why expertise matters: it takes technical depth and a forensic mindset to extract, decode, and validate vehicle data in a way that holds up in court.
What challenges do courts face in accepting vehicle data as evidence?
The biggest challenge is trust.
Vehicle data isn’t just technical—it becomes legal the moment it enters a courtroom. But to be admissible, it must meet strict standards: Was the data collected properly? Is it complete? Was it tampered with? Can the expert explain it in plain terms?
Unfortunately, many courts are not yet fully familiar with how modern vehicle systems work. Unlike traditional forensics, this field lacks uniform procedures. Most vehicle platforms are proprietary and closed-source. That means investigators often rely on manufacturer tools—or reverse-engineered ones—to extract and interpret the data.
This raises two concerns: authenticity and transparency. Judges want to know not just what the data says, but how it was obtained and whether the expert is truly independent.
That’s why I believe the role of the expert is not just technical—it’s translational. We must turn complex telemetry into clear, courtroom-ready insights. And we must do so with a level of neutrality and precision that stands up to cross-examination.
How does standardization, like ISO/IEC 17024, build trust in forensic results?
Standardization provides something the legal system needs more than anything: confidence.
In forensic work—especially when it comes to technology—judges and lawyers often lack the technical background to independently assess expertise. That’s where internationally recognized certifications like ISO/IEC 17024 come into play.
Unlike company-based certifications like ISO 9001, which assess business processes, ISO/IEC 17024 is a personal credential. It can’t be bought or transferred. It requires an individual to prove—not only deep technical knowledge—but also the ability to apply that knowledge impartially and ethically in real-world scenarios. The examination process is rigorous, peer-reviewed, and renews periodically.
In my case, certification was granted in both information technology and automotive engineering—two areas that rarely overlap. These dual credentials signal more than specialization: they show that I’ve been independently verified to evaluate highly complex systems and, when necessary, to assess the work of fellow professionals in contentious proceedings.
In court, that matters. Standardization bridges the gap between specialist knowledge and legal reliability. It allows experts to speak with authority, not just opinion.
How might AI tools help—or complicate—vehicle forensic work?
AI holds promise in many areas of digital forensics, including connected vehicle analysis—but it’s a double-edged sword. I’ve worked with AI-assisted analysis models in select cases, particularly where large volumes of sensor data or event sequences need to be structured and interpreted quickly. When used carefully, AI can speed up pattern recognition, detect anomalies, or even predict possible error chains.
But there’s a caveat: AI is only as trustworthy as the input it receives and the context in which it’s used. Most vehicle systems are still closed environments, and the underlying data often lacks transparency or standardization. This makes it difficult to ensure that AI-generated insights are forensically sound or even reproducible.
Another complication is the potential for AI models to introduce interpretation bias or to overfit conclusions that a human expert would challenge. That’s why, in my own work, AI is used more as an auxiliary tool than a decision-maker. It can support the forensic process, but it cannot replace sound technical judgment, especially in court-admissible contexts.
How do you see automotive forensics evolving with autonomous and connected vehicles?
We are entering an era in which vehicles operate less like mechanical machines and more like decision-making systems. With this shift, the role of forensics expands beyond the traditional scope of defect analysis or user error. Investigations increasingly involve interpreting complex interactions between software logic, sensor input, and real-world outcomes.
As these vehicles grow more autonomous, responsibility becomes harder to define. It’s not just a question of what failed — but what the system “thought” was happening, and why it responded the way it did. That demands a new level of technical and analytical expertise, paired with the ability to present findings in a clear, structured, and legally sound way.
I see automotive forensics becoming a vital tool for accountability — not just in courtrooms, but also in shaping how we regulate and trust emerging technologies. As we integrate more of these systems into our public infrastructure, we need independent voices capable of bridging engineering depth with public responsibility. That, to me, is where this field is headed.
And finally, what do you enjoy in your spare time?
In my limited free time, I enjoy visiting classic car shows — especially those that showcase the engineering beauty of earlier decades. There’s something rewarding about seeing how mechanical precision used to work without today’s layers of sensors and software.
I also play a bit of golf. I won’t claim to be particularly skilled — but at least I manage to look the part, which seems to count for something!
Sharing a good meal with friends and colleagues is another thing I value. I enjoy trying out new restaurants and engaging in conversations that range from the technical to the entirely trivial. After a day of analyzing complex systems, there’s nothing better than connecting with people face-to-face.
Lastly, I spend time staying current — reading technical papers, watching in-depth industry content, and following developments in cybersecurity, AI, and vehicle systems. For me, learning isn’t something separate from work or leisure. It’s just how I’m wired.
