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~/about

The short version

I work at an unusual intersection: a Ph.D. in rhetoric applied to the engineering, teaching, and governance of cybersecurity. Most people arrive at security through computer science, compliance, or the military. I arrived through the study of how language, technology, and trust function as systems to shape one another. That background isn’t irrelevant: it’s my practice. It’s why I treat security less as a technical checklist and more as an ongoing argument about what we value and what we’re willing to defend.

My recurring argument is that as cybersecurity professionalized through boardrooms, frameworks, and certification bodies it gained rigor but risks losing the culture of genuine inquiry that produced the field in the first place. Curiosity, critical thinking, and a willingness to question authority aren’t “soft skills” secondary to the real work. They are the work.

Borrowing a distinction from Bruno Latour: I’d rather we treat security as a thing — an unsettled matter of concern we keep returning to — than as a settled object we can check off and forget.

That’s also why I study the history and the rhetoric of hacking. Phone phreaking, the zines (Phrack, 2600), the Legion of Doom and the Masters of Deception: This lineage isn’t empty nostalgia. It’s a usable ethics and adaptable mindset providing engaging case studies in curiosity, consequence, and where the line between exploration and harm actually falls. Students who understand where the field came from make better judgments about where it’s going and the type of cybersecurity practitioner they want to be.

In practice, I consult on cybersecurity governance, cloud, and AI security for government and commercial clients as an Associate of Triple Point Security, where I also build and lead an award-winning cybersecurity internship program and direct our R&D initatives. I have taught cybersecurity, data science, writing, and design at Virginia Tech, on and off, from 2012 through 2024. And I’ve been thinking about algorithmic trust for a while. I wrote about deep learning’s “inscrutability” and the problem of explainable AI back in 2018, several years before it became an industry-wide concern. If any of this resonates, I'd love to hear from you.

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