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were doing something and what we should be doing next, and that pulled me toward innovation. I think of it as an in-house‘ innovation office’: a place to chase moonshot ideas while staying tightly connected to the business. That combination, being a scientist who’ s still oriented around what the market and customers need, is how I ended up in my current role.
2. How have your experiences shaped how you apply AI tools to security problems? Honestly, AI today is so different from the AI of my PhD or Army years that the technical specifics don’ t carry over directly. What does carry over is discipline. First, making sure things are built correctly: asking the right requirements questions and holding AI systems to a high bar for robustness. Second, knowing where the genuinely hard problems live, and closely monitoring and verifying what the AI produces there. I saw this constantly in research. If you hand a model a hard problem and you don’ t already know the answer, it will confidently give you a wrong one, and if you don’ t know better, you’ ll just use it. That instinct for spotting where AI needs close supervision is probably the biggest carryover from my research background.
3. What initially attracted you to Cye? I joined Cye as a data scientist doing handson engineering work, building out the data science function essentially from scratch, using the scale and specificity of the cybersecurity data Cye had already amassed. Over time, that work naturally expanded beyond pure engineering into product and strategy questions, which is what eventually led to my current innovation-focused role.
4. What does a day in the life of a Chief AI & Innovation Scientist at Cye look like?
It’ s still very hands-on. A lot of my time is spent writing code, often with AI, and looking at issues, bugs, and new features or models. I stay close to that work myself and with my team. Beyond that, a big part of the role is conversations, with R & D, product, marketing, and customer success, getting their feedback on what we’ re doing and what’ s missing, and keeping an eye on what’ s happening in the industry and with AI more broadly. Sometimes that’ s targeted, like testing an idea with people and hearing their reactions, but it can be a completely different conversation that surfaces a new opportunity. So it’ s really research and execution in tandem: research through those stakeholder conversations, and execution on whatever meets the business need.
The one thing I’ ll stress is that innovation only counts if it reaches production. It doesn’ t matter how‘ moonshot’ a feature is, if customers can’ t touch it, I don’ t count it. I’ ve seen plenty of research efforts that sound impressive but never leave the whiteboard or the lab, and to me that’ s not real innovation. You can’ t innovate if users can’ t see it, touch it, or consume it, even in a small way.
5. Considering your position at the intersection of AI capability and cyber threats, what is your view on how these two concepts interact? How do you decide which emerging AI developments require Cye’ s research attention? Attackers are getting enhanced and accelerated, but so are defenders. The whole cycle just moves faster now. If anything, I’ d say attackers currently have an edge, because it’ s harder to build and maintain something robust than it is to find ways to break it, especially as more development means a bigger attack surface. That’ s connected to what we internally call the exploitability gap: attackers have a larger
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