I Used to Forecast Storms. Now I Forecast Threats: The Unexpected Path That Led Me into Cybersecurity

What Got Me Into Cybersecurity

Threats are everywhere.

A suspicious log at 2 am. An arsonist in a forest. A robber casing a bank. The shape of a threat changes — but the need to detect it, understand it, and stop it never does.

That instinct to find what doesn't belong has followed me my entire career. I just didn't always call it cybersecurity.

It started with the atmosphere.

I graduated from Howard University with a Master's in Atmospheric Chemistry. My thesis research focused on evaluating the Economic Value of Air Quality Forecasting Models — essentially asking: are these models still accurate, and are they needed?

So I put on my detective hat, analyzed logs of weather data and air quality ratings, searched documented evidence of model performance, and built my thesis. The answer was yes — the models were accurate, and they were needed. Those air quality ratings exist to protect people. That mattered to me.

Before Howard, I was a Meteorology major at Kean University — writing code in Fortran to predict weather outcomes, getting introduced to Computer Science and Java, giving live weather reports on campus radio, and recording TV segments. I also wrote for the school newsletter — pieces like how to stay safe in floods and the Turn Around Don't Drown campaign. Every single thing I did was about taking complex data and translating it into something that protected people.

The forecaster's mindset — and why it matters in cybersecurity.

Here's something most people don't think about when they hear "meteorology": forecasting isn't about knowing exactly what will happen. It's about reading current conditions, understanding how systems behave, and making the most informed prediction possible about what comes next — before it arrives.

A good forecaster doesn't wait for the storm to be overhead. They see the pressure dropping, the wind shifting, the humidity rising, and report on their in-depth findings. The goal is always the same: get ahead of the threat.

Cybersecurity demands exactly that same mindset.

Threat intelligence analysts don't wait for a breach to happen. They study attacker behavior, monitor for early indicators of compromise, and try to predict where the next attack is coming from before it lands. Security operations teams don't just react — they look for things like the anomalous login at an unusual hour, and the subtle shift in traffic patterns that tells you something is wrong before anything is confirmed.

That is forecasting. Just in a different atmosphere.

I spent years learning to read systems that most people couldn't see coming. Weather systems first. Then data systems. Now I'm learning to read threat systems — and the instinct that built itself in meteorology classrooms and weather labs is the same one I'm bringing to cybersecurity. The variables changed. The discipline didn't.

A storm tracker who learned to predict threats before they form is exactly the kind of analyst a SOC needs on their team.

Then came the fraud.

At AOL, my job was to evaluate website quality against policy. I used tools like SimilarWeb to identify fraudulent websites — analyzing traffic patterns, behavioral signals, and the look and feel of the site itself. It was quality control with a detective edge.

I saw something that stuck with me: how easy it is to build something false. A convincing website filled with misinformation, designed to deceive. The threat wasn't always a virus or a hack. Sometimes it was just a very convincing lie.

Then came the data.

I took my analytical instincts into healthcare, becoming a data analyst querying healthcare claims — some of the most sensitive, most protected data that exists. HIPAA. PHI. Real patients. Real impact.

I learned SQL, Python, Databricks, and Tableau. I tracked patterns. I visualized results for stakeholders and department leaders. I found anomalies — things in the dataset that didn't belong — and worked to correct them so the data could be trusted.

Finding things that don't belong. Protecting data that matters. Making systems more accurate and more secure.

Sound familiar?

This is where it all connects.

I spent years doing cybersecurity thinking without the cybersecurity title. Weather modeling. Forecasting. Fraud detection. Anomaly analysis. Policy compliance. Protected health data.

Every role has built my knowledge. Every skill sharpened the instinct.

Now I want to take it further — to the front line of the growing cyber threat landscape. I've seen server configurations as an analyst. I've sat through awareness trainings with my detective ears fully up. I've watched AI blur the line between what's real and what's manufactured — fake videos, false information, deepfakes designed to deceive at scale.

The cyber threat is bigger than any one of those things. And that's exactly what pulls me in.

I'm not starting from zero. I'm starting from everywhere I've already been.

This is DataSec Chronicles. Here's to more of the story and the climb ahead!