A few weeks ago, I saw a LinkedIn post that stopped me in my tracks. It asked, "Where did all the cyber analyst jobs go?" and talked about how automation, SOAR, and LLMs are fundamentally changing the landscape. It argued that the work isn't disappearing, but evolving. For aspiring tech professionals, the message was clear: you have to evolve with it.
That post hit home because I was living that evolution. During my internship, I saw firsthand how much of an analyst's day can be consumed by repetitive, manual tasks. My journey started there, not just as an analyst, but as a builder who decided to automate his way out of a problem.
The Grind: Drowning in Open-Source Intelligence
My role involved Open-Source Intelligence (OSINT), which is a fancy way of saying I had to read the internet—a lot of it. Every morning, my team and I would manually sift through dozens of cybersecurity news sites, blogs, and alerts. We were looking for mentions of new data breaches, malware strains, or vulnerabilities. The goal was to find the signal in the noise.
The process was tedious and inefficient. We’d spend hours copying links, summarizing articles, and sharing our findings in a group chat. It felt like we were always a step behind, and there was always the nagging fear that we were missing something critical because we simply couldn't read fast enough. The volume was, as the LinkedIn post said, "wild."
It was a classic case of a problem begging for an automated solution. I knew the theory—learn Python, learn automation—but this was my chance to apply it to a real-world pain point I was experiencing every single day.
The Solution: Building My Own Backend Pipeline
Instead of just accepting the daily grind, I decided to build a tool to do the heavy lifting. This became Project Synapse, but it wasn't just a "cybersecurity tool." It was my first real dive into backend development and building a complete software pipeline.
My goal was to create a system that could automatically:
Collect the Data: I started by hooking into the Google Gmail API to parse my Google Alerts and used libraries like feedparser to pull from various RSS feeds. This was the first step in replacing manual browsing.
Scrape the Content: Getting links is one thing; getting the full article text is another. I quickly ran into anti-bot measures. This forced me to level up my skills, building a resilient web scraping engine with tools like curl_cffi and Selenium to handle even the most stubborn JavaScript-heavy sites.
Process and Summarize with AI: This is where it got really interesting. Inspired by the rise of LLMs, I integrated Google's Gemini API. My script would feed the full text of each article to the AI and ask for a concise, two-sentence summary. This replaced the most time-consuming manual task.
Store and De-conflict: To avoid duplicates and keep a clean record, I used a simple SQLite database. The most crucial feature, however, was integrating the Slack API. Before processing an article, the script would check our team's Slack channel to see if a human had already covered it. This "human-in-the-loop" approach ensured my automation supported the team, rather than spamming them.
More Than a Script: A Lesson in Software Development
What started as a simple Python script quickly evolved into a full-fledged backend application. I wasn't just solving a cybersecurity problem; I was learning the fundamentals of software engineering. I was dealing with API rate limits, handling network errors, managing data integrity in a database, and learning how to secure API keys instead of foolishly pasting them into my code.
This project became my answer to the question posed by that LinkedIn post. The value wasn't just in doing the analysis anymore; it was in building the engine that empowers the analysis.
The roles in tech aren't vanishing. They are shifting from manual operators to creators, integrators, and automators. My journey with Project Synapse taught me that the most valuable skill you can develop is the ability to see a repetitive, frustrating process and have the curiosity to ask, "Could I build something to fix this?"
The answer, I’ve learned, is almost always yes.
