Month: July 2025

Look, I’ve been in cybersecurity for over a decade, and I’m tired of seeing the same preventable disasters over and over again. Cloud security breaches aren’t happening because of some sophisticated nation-state actor using a zero-day exploit. They’re happening because someone forgot to flip a switch or left a door unlocked. The numbers are frankly

Cloud platforms have become prime targets for ransomware and malware attacks, which can paralyze businesses by encrypting data or exfiltrating sensitive information. Traditional security tools such as signature-based antivirus and rule-based systems often struggle to detect advanced threats that mutate or exploit unknown vulnerabilities. Organizations are increasingly turning to artificial intelligence (AI) and machine learning

Speed kills. In software development, that axiom has never been more literal. DevOps pipelines surge through modern enterprises like digital bloodstreams — pumping code, configurations, and deployments at breakneck velocity. Continuous integration and continuous delivery are the promises of rapid iteration and market responsiveness that transformed how we build, test, and ship software. Yet beneath

It’s no secret that technology is evolving much faster than our traditional Identity and Access Management systems can handle. These legacy systems were designed for simpler times, when everything was hosted locally and security was perimeter-based. So, in an era where most enterprises, if not all, are moving their workloads to hybrid, multi-cloud, and AI-driven

Emerging technologies like homomorphic encryption and zero-knowledge proofs can definitely help organizations approach zero-trace personal data anonymization. These and similar techniques can bring datasets to a near-zero-trace status, even achieving it in limited cases. There’s a major force that’s acting against efforts at implementing the zero-trace paradigm, though, and it’s difficult to discuss this paradigm

Visual tracking systems are essential for applications ranging from surveillance to autonomous navigation. However, these systems have a significant Achilles’ heel: they rely heavily on large, labeled datasets for training. This reliance makes it challenging to deploy them in real-world situations where labeled data is scarce or expensive to obtain. In this article, we will

The world of AI, especially with Large Language Models (LLMs) and Generative AI, is changing the game. It’s like we’ve unlocked a superpower for creating content, automating tasks, and solving tricky problems. But, as with any new superpower, there are new ways things can go wrong. Open Worldwide Application Security Project (OWASP) experts have put

If you’ve ever been nervous about deploying code on a Friday, trust me — you’re not alone. A few years ago, I was leading a team at a major e-commerce company, wrangling a monolithic beast that could break in a hundred creative ways. The idea of microservices was everywhere, but nobody really tells you about

Microservice is the false belief that adding a message broker to your app will somehow magically make it faster and more scalable. Ignoring the fact that this is, in itself, an oxymoron—and that your app quite literally becomes two billion times slower—the absolute dumbest argument I’ve ever heard in favor of microservices is:

Recently, I explored how the Model Context Protocol (MCP) is gaining traction in the Java ecosystem, with frameworks like Spring AI, Quarkus, and LangChain4j starting to adopt it for integrating language models via standardized interfaces. It was also time to start experimenting with writing an MCP Server myself (well maybe not the first time). Certainly,