Ondrej Kubovič

A single protective technology means a single point of failure. A company aiming to build reliable and strong cybersecurity defenses should opt for a solution offering multiple complementary technologies.

Machine learning alone is not enough to protect endpoints and predicting an attacker's next moves. Other security solutions and human input are needed.

Machine learning (ML) is routinely cited by post-truth vendors as their biggest selling point, their main advantage. But ML – if it’s done properly – comes with problems and limitations.

There is no magic in machine learning. It's a field of computer science that gives computers the ability to find patterns in huge amounts of data.

Welcome to the beginning of a new series of short articles focused on the currents state of AI, all the ins and outs of machine learning, and how it affects cybersecurity.

Three years ago, Microsoft ended its extended support for Windows XP. Today, almost 8% of desktop users worldwide are still run the operating system.

Mac OS X is still secure 16 years after its creation, but increasingly being targeted by cybercriminals. No operating system is 100% malware-proof.

2016 saw interesting developments within the Android ransomware scene. Ransomware emerged as one of the most pressing cybersecurity issues on the mobile platform.

As Data Privacy Day is upon us, we thought it apt to put together some top tips on how to manage and secure your online information.