My Next Chapter: Securing the Agentic Future with Cyberhaven

For the past two decades, my career has been a continuous journey through the evolution of data.

Looking back at the companies I've been lucky enough to be a part of, there is a clear thread connecting them. I've always been drawn to the hardest engineering challenges where data is the protagonist.

At Terracotta, we tackled the challenge of scale, building an in-memory data store capable of scaling to terabytes of transactional data and supporting millions of transactions per second.

At Quid, we worked on understanding the meaning. We indexed massive repositories of long-form text, and used pre-LLM machine learning models to extract semantic insights that helped analysts understand the world.

At Sysdig, the challenge shifted to speed and observability. We processed trillions of time-series data points per day, enabling real-time queries and alerts, that allowed enterprises to see inside their containerized infrastructure as it happened.

And most recently at Komodo Health, we tackled complexity and impact. We processed petabytes of real-world healthcare data every month, turning messy, disparate records into lifesaving insights using agentic workflows.

So, why Cyberhaven? And why now?

Throughout this journey, I've watched the data landscape shift. We are no longer just storing, analyzing, or observing data. We are entering an era where data is the fuel for autonomous action.

In the emerging world of AI Agents, data doesn't just sit in a database waiting for a query. It travels. It is fed into LLMs, transformed by agents, and moved across endpoints, SaaS platforms, and cloud environments at machine speed.

I've learned that category-defining companies usually sit at the intersection of three things:

1. A Pain Point That Has Become Ubiquitous: ‍

Data protection is no longer a compliance checklist; it is the number one existential risk for the enterprise. In an agentic world, traditional security perimeters are obsolete. Data is fluid. It lives in the browser, in the prompt, and in the background processes of AI agents. Current solutions are siloed, they see the endpoint or the cloud, but never the full journey. The industry is desperate for a solution that sees the whole picture.

2. A Differentiated Solution (The Secret Sauce):

While others are trying to retrofit legacy DLP for the AI era, Cyberhaven built a platform based on data lineage.

With the unified platform, spanning DLP, DSPM, IRM, and AI Security, we aren't just scanning content; we are tracing the data through its journey. And with Linea AI, we are applying that same agentic power to understand risk, perform as an analyst but at machine scale. It is a fundamentally different architectural approach to a problem that cannot be solved with rules and regex.

3. A Massive Tailwind:

The explosion of GenAI and agentic workflows is the greatest accelerant of personal productivity and enterprise risk at the same time. Cyberhaven is perfectly positioned to be the enabler of this revolution, giving companies the confidence to adopt AI without risking their IP.

The Final Ingredient:

Technology and market timing are critical, but they mean nothing without the right Team. In my conversations with the team, I found a group of Tracers, curious thinkers, deep technologists, and security experts who are obsessed with first principles.

I am thrilled to join Cyberhaven to head Engineering to help build the platform that secures the AI economy. The opportunity is massive, the tech is differentiated, and the team is ready.

Let's go! If you are interested in this journey, we are hiring!