“Accumulate information” means gathering and consuming content (watching YouTube videos, signing up for courses…) without reflecting on what’s being said. That’s pure entertainment. And hey, it’s fine; I do it all the time. But if you want to accumulate knowledge, that doesn’t work.

Learning means working with information: breaking it down, applying it, making mistakes, and fixing them.

Here are a few tips that help turn information into knowledge:

• Don’t read more than you can apply.

• Study with a notebook (or similar). Write, sketch, create mind maps. Our brain works in networks, not straight lines, so mapping concepts helps. But mind maps are only useful if you create them yourself. I’ll try to attach mine in each post, but you should do your own.

• Practice. If we don’t practice in real life, nothing sticks. Look for side projects at work, start a small project on your own, try free vendor labs, or create your own challenges. At some point I’ll share labs that help understand foundational AI models and types.

• Explain. After studying and practicing, explain what you’ve learned. Write summaries, talk to colleagues, or start a blog. That’s why I’m writing this project: to consolidate my own knowledge.

• Don’t rush ahead without the basics. Revisit your notes and mind maps until the foundations are solid. At first you’ll need to reread the words and concepts, but later, a single glance at the map will trigger all that knowledge in your head.

• Limit distractions. Don’t try to learn with 20 tabs open, your phone buzzing, and chats popping. For me, going to the library has always helped. Presence leads to flow, where time disappears and learning becomes less of a struggle and more of a ride. Focus on one thing, not many.

• Lean into discomfort. At first, opening the AWS console, setting up infrastructure, or running a model will feel uncomfortable. But that’s where learning happens. What feels hard today will be your comfort zone tomorrow.

• Motivation shouldn’t depend on volatile enthusiasm. It comes from action, focusing on what you can control: your attitude and your effort.

Stop gathering. Start learning.

Here’s the mind map I created for this post.

I use AI every day in my work as a Security Engineer. But does that mean I understand how AI actually works inside? Does it mean I know how to secure it? Do I know all the areas where an AI system could be vulnerable? What are the risks when creating AI services in an organization? Or am I just learning how to use a tool?

The truth is: using AI doesn’t teach you how it works, nor what its security implications are. That’s the main reason for starting this blog:

1. A way to consolidate knowledge. Writing down what you learn helps you understand it better.

2. A place to go back and review notes.

3. A space to share analysis of new reports, explain attacks, and continue documenting what I’m learning.

I hope this becomes valuable for someone else who wants to start in AI Security, especially if you already have a background in security.

The journey starts now.