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 ...

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.

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 ...

Nobody has ever responded to a threat they could not detect. Just ask Capital One that is facing public scrutiny over one of the largest data breaches ever: one that persisted in the company’s ...

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 ...

Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users’ behavior, creating notifications of risky activity in real time, ...