From Security to Data and AI Governance

on accountability, measurement, and enterprise decision support

My career began in electrical engineering and expanded into the CISO role as accountability moved from systems to enterprise risk, regulatory exposure, and operational resilience. The work naturally shifted toward governance, measurement, and board-level decision support.

As a CISO, my accountability centered around data protection. Risk quantification, compliance, third-party oversight, and incident response depended on how data was classified, governed, measured, and trusted across the enterprise.

I have been writing a standard/book for two years now(whew 😒) to formalize a proper cyber metrics reality and extend it. It focuses on building enterprise data models, metrics, and measurement frameworks that convert technical and operational complexity into proper decision-grade insight. The objective is accountability: enabling leadership to govern risk, allocate capital, and evaluate outcomes based on evidence as analytics and automation scale. The same governance and measurement mechanics apply to AI, where model behavior, training data, and operational impact require defined guardrails and executive oversight.

I am excited to announce that I will be engaging with the prestigious Carnegie Mellon University’s Chief Data and AI Officer (CDAIO) program to deepen this work and formalize the operating models required to govern data and AI at enterprise scale. 🎩 🚀

My career has come full circle from securing systems, to governing data, to overseeing how analytics and automation shape enterprise outcomes. Expanding into data and AI leadership broadens the scope and the failure modes are already familiar; the surface area is just a bit larger. That is why it feels so exciting. It calls for continued learning, operational rigor and restraint, and it is work I am ecstatic to step into.

Wish me luck. Cheers!