Ideas on AI, data and security.

What AI implementation costs and what drives the price

Which costs you see up front, which ones show up later, and how to judge the budget before you sign anything.

How to tell whether AI is worth it for your process

Five numbers and one simple calculation that tell you more than any presentation. Before you spend the first euro.

AI on the systems you already have: ERP, CRM and documents

Where AI connects, what has to be ready first and what usually breaks. A practical map for integration.

Is it safe to give company documents to AI?

The real risks are rarely where people look. Personal accounts, broad permissions and contracts: what to check.

The AI Act in practice: what every company using AI has to do

Most companies are deployers, not developers. What already applies, what was postponed in 2026 and what to do now.

NIS2 in Bulgaria: is your company in scope and what to do now

The Cybersecurity Act now applies NIS2. Who is in scope, what management must do, the incident deadlines and the fines.

From pilot to production: how to implement AI that works

Many AI projects stop at the demo. The steps that separate a prototype from a solution your business can rely on.

MLOps basics: data your models can rely on

A good model starts with good data. A short introduction to data pipelines, traceability and quality control.

5 common cloud security mistakes and how to avoid them

Over-broad permissions, publicly accessible data, forgotten keys. Common mistakes and simple ways to avoid them systematically.