From SOCs to smart cameras, AI-driven systems are transforming security from a reactive to a predictive approach. This ...

Here are some of the ways in which machine learning has contributed to cybersecurity: 1. Malware detection: Machine learning algorithms can analyze large volumes of data to identify patterns that are ...

Why has machine learning become so vital in cybersecurity? This article answers that and explores several challenges that are inherent when applying machine learning. Machine learning (ML) is a ...

Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and ...

This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It responds to ...

Malware continues to be one of the most effective attack vectors in use today, and it is often combatted with machine learning-powered security tools for intrusion detection and prevention systems.

Contrary to what you may have read, machine learning (ML) isn't magic pixie dust. In general, ML is good for narrowly scoped problems with huge datasets available, and where the patterns of interest ...

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...

ML-enhanced endpoint protection can keep schools safe from cyberattacks. Here are three benefits district leaders will find when investing in this advanced technology. Long before the pandemic, K–12 ...

Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%. Cloud Security, Data Security, and Infrastructure ...