Two pieces.
Read in either order.

CyberCLI exists to give every person, business, and lean technical team access to their own AI-assisted cyber operations capability.

For too long, real cybersecurity has been locked behind enterprise budgets, complex tools, and teams most organizations cannot afford. Meanwhile, small businesses, families, executives, creators, and independent operators are facing the same digital threat environment as large enterprises.

CyberCLI was built to close that gap.

Our mission is simple: make cyber operations more accessible, more practical, and more sovereign by placing AI-assisted security workflows directly in the hands of the people who need them.

CyberCLI is not another bloated dashboard. It is not a toy. It is not a generic AI wrapper with a cybersecurity label pasted on top.

It is a command-line inspired security platform built around real operational needs: monitoring, triage, investigation, OSINT, cyber hygiene, reporting, and response.

It was designed from the perspective of an operator, not a software theorist.

CyberCLI comes from more than two decades of work across cybersecurity, digital forensics, robotics, artificial intelligence, OSINT, red teaming, incident response, and security operations. It was built by someone who has investigated cybercrime, built forensic labs, taught global partners, protected high-risk environments, run security companies, and seen firsthand how wide the gap is between the people who need cyber protection and the people who can actually afford it.

That gap should not exist.

AI changes the equation.

It should help one good operator do the work of many. It should help small teams move faster, investigate deeper, explain risk clearly, and respond with better context. It should give non-enterprise users access to capabilities that were previously reserved for large organizations with dedicated SOCs, expensive platforms, and around-the-clock staffing.

CyberCLI was built on that belief.

The goal is not to automate judgment away. The goal is to put better tools in more hands.

CyberCLI is for the builder who wants to understand their attack surface.

It is for the small business owner who cannot afford a full security team.
It is for the MSP trying to scale better service without burning out analysts.
It is for the investigator who needs faster triage and better context.
It is for the family office, executive, creator, or entrepreneur who has real risk but no practical path to enterprise-grade security.

It is for the solo operator who believes AI can make one person dramatically more capable.

CyberCLI was built because I wanted this tool for myself.

Every part of CyberCLI is being shaped around practical value, operational usefulness, and real-world cyber workflows. It is built for people who want capability, not complexity. It is built for users who want security that feels powerful, understandable, and within reach.

The future of cybersecurity will not belong only to the biggest companies with the largest teams.

It will belong to the people who can combine human judgment, machine speed, and operational discipline.

CyberCLI is our contribution to that future.

First, I should say this plainly: I am not a traditional software founder.

I have built things for years. I have written scripts, created tools, automated workflows, experimented with AI, built labs, investigated cybercrime, and spent most of my life trying to understand how technology can be applied to real-world problems. But CyberCLI is the first product I have tried to release publicly, sell openly, and put in the hands of real users.

That is both exciting and terrifying.

What I do have is more than two decades of experience across cybersecurity, digital forensics, robotics, security, intelligence, OSINT, and applied technology. And a 24-year obsession with what the world now calls artificial intelligence.

Before ChatGPT, before copilots, before AI agents, before every product had an AI label, I was experimenting with artificial linguistics, chatbots, communication patterns, AOL Instant Messenger, and the strange idea that software could learn enough about a person's language to imitate how they respond.

That obsession started early.

I grew up during the first real wave of the internet. I was fortunate to have a Compaq computer in my house at a time when that meant access to an entirely new world. Online gaming, forums, Photoshop tutorials, Yahoo chat rooms, early web communities. It all felt like a frontier. I did not know it at the time, but that frontier shaped the way I would think for the rest of my life.

In high school, I learned C, C++, HTML, and the basics of programming. Like a lot of students, most of those projects were simple assignments that disappeared as soon as the grade was entered. But the seed was planted.

College changed everything.

I attended Rice University, where I explored electrical engineering, computer science, linguistics, philosophy, robotics, and athletics. I played football and basketball at Rice, and I also served as a robotics lab teaching assistant for four years. That robotics lab was one of the best learning environments I have ever experienced. It combined mechanical engineering, electrical engineering, computer science, sensors, machine learning, strategy, failure, iteration, and teamwork into one practical arena. Thank you, Dr. Young and Dr. Bennette.

That experience taught me something important: technology becomes powerful when it leaves theory and enters the physical world.

But robotics was only part of the story.

Deep in the humanities side of campus, I found something that changed the trajectory of my life: linguistics.

The linguistics department opened my mind to the connection between language, thought, identity, and behavior. I became fascinated by the idea that the way people write and speak is unique to them. Language carries patterns, habits, timing, emotion, and intent. Long before large language models became mainstream, I was already thinking about how human communication could be mapped, analyzed, and simulated. Thank you, Dr. Barlow.

Through that work, I was introduced to A.L.I.C.E., the Artificial Linguistic Internet Computer Entity, and the early world of chatbot development. I spent hours building and tuning bots for friends, gaming communities, and personal experiments. I studied corpus analysis and became fascinated by how much could be learned from the way people naturally communicate.

In one senior-level linguistics course, a lab partner and I built a project using tools from the A.L.I.C.E. foundation. The project analyzed emails and AOL Instant Messenger history from a friend and used those patterns to create a bot that could respond like him. It was not a modern AI model. It was not GPT. It did not have transformers, massive datasets, or cloud-scale compute.

But it worked.

The bot used his phrases. It responded with his tendencies. It recognized recurring social patterns. When people messaged him, it replied in ways that felt familiar enough that most people did not realize they were talking to software. Only a couple of people close to him became suspicious.

That was more than 20 years ago. Here's the paper, untouched.

After college, my football dream went dead in the water pretty fast. I had a decent college career, but I played for Rice. We were not an SEC powerhouse, and the NFL was never going to be the next stop. What I did get out of it was some of the best friends I will ever have. To my 2003 O-line: I bought offensiveline.com as a tribute to you guys. Yeah, I collect domain names. It is my love language.

I should also confess: I was probably the worst NCAA basketball player ever to run down the court. I blame the Freshman 50 I gained the moment I was introduced to unlimited breakfast sandwiches and Chick-fil-A.

So I joined the United States Secret Service as a Special Agent.

The government learned that I could turn on a computer, so naturally I found my way into cybercrime, digital forensics, and technical investigations. That experience changed my life. I worked complex cyber cases, helped build forensic labs, and focused on improving the way digital evidence was collected, analyzed, and understood.

I also had the opportunity to travel internationally and teach cybercrime investigations to global partners. That work exposed me to a hard truth: the best intelligence and the best technical capabilities were no longer held only by governments. The private sector was moving faster. Open-source data was exploding. The internet had become both a battlefield and an intelligence platform.

During that period, I returned to coding and automation. I started building Python scrapers and tools to collect, structure, and analyze information. Some of it was for work. Some of it was curiosity. Most of it came from the same place that drove me as a teenager on that old Compaq computer: I wanted to know what was possible.

The more I worked in cyber, the more I saw another problem.

Large organizations had access to tools, teams, analysts, monitoring platforms, incident response firms, intelligence feeds, and dedicated security operations centers. But small businesses, families, executives, creators, and independent operators were increasingly facing the same threat environment without anything close to the same resources.

That gap is one of the reasons I eventually left government service and started my own firm.

In 2017, I founded BladeOne. We specialize in penetration testing, red teaming, incident response, OSINT, digital risk, managed security, and security operations. We built services for businesses, families, high-risk individuals, and organizations that needed real cyber capability but did not always fit neatly into the enterprise security model.

Running a business has been one of the most humbling experiences of my life. It has also been one of the most exciting. Every day brings a new problem, a new client need, a new threat, or a new opportunity to build something useful.

CyberCLI came from that same place.

In early 2024, I felt frustrated with the state of AI. The tools were impressive, but they were not giving me what I wanted. I could open a code editor, use AI assistance, and make interesting things, but it still felt like something was missing. The models were powerful, but the workflows were not operational enough. The intelligence was there, but the execution layer was weak.

So I went local.

I upgraded servers, built AI rigs, tested models, explored open-source tools, experimented with voice, images, scraping, agents, and sovereign AI. I spent time and money trying to find a way to make AI useful for real cyber operations, not just demos and chat windows.

For a while, it still felt flat.

Then the agentic AI ecosystem started to mature. Local models improved. Tool use improved. Frameworks improved. Fine-tuning became more accessible. Projects like Hermes Agent (thank you, Nous Research) and Unsloth helped open my eyes to what was possible. For the first time, I felt like AI could move beyond answering questions and start helping with structured operational work.

That changed everything.

I began building frameworks around the tools and models I had access to. I started thinking about what an AI-assisted cyber operations platform should actually look like if it were built by someone who needed to use it every day.

Not as a theory. Not as a pitch deck. Not as another dashboard.

As a working cyber command layer.

That became CyberCLI.

The idea was simple: what if every person, small business, MSP, family office, creator, executive, and lean technical team could have access to their own AI-assisted SOC?

Not a replacement for human judgment. Not a magic button. Not an overhyped security toy.

A practical, operator-built platform that helps with monitoring, triage, investigation, OSINT, cyber hygiene, reporting, and response.

I had one rule from the beginning:

CyberCLI exists because I wanted this capability for myself, my company, my clients, my friends, and the people who are too often left out of serious cybersecurity conversations. I wanted something that made cyber operations more accessible. I wanted something that helped one good operator do more. I wanted something that gave smaller teams a fighting chance.

The interface may be command-line inspired. The mission, however, is much larger.

CyberCLI is about giving people access to capability.

It is about turning AI into an operational force multiplier.
It is about helping defenders scale.
It is about making cyber tools more sovereign, more practical, and more useful.
It is about proving that one person with the right tools, the right discipline, and the right AI support can build and operate at a level that used to require entire teams.

I do not know exactly where this journey ends. No honest founder does.

But I know why it started.

CyberCLI started because I believe the future of cybersecurity will belong to the people who combine human judgment, machine speed, and operational discipline.

It started because I believe AI should give more people access to serious capability.

It started because I have spent most of my life chasing the same question:

CyberCLI is my latest answer.

And we are just getting started.