Traditional security and log-management tools attempt to provide insight into the chaos, but they typically require users to write rules to detect anomalies. Writing those rules requires pre-existing ...

Anomaly detection in the context of data science is detecting a data sample that is out of the ordinary and does not fit into the general data pattern (or an outlier). This deviation can result from a ...

I am the VP of Engineering at Apriorit, a software development company that provides engineering services globally to tech companies. Social media is an indispensable tool for businesses to engage ...

Violations of security policies within a computer or network are symbolic of the need for robust intrusion detection. From attackers accessing systems from the internet or authorized users conducting ...

In today’s digital world, fraud has become more complex, which means we need smarter ways to detect and prevent it. Generative AI helps with this by looking at large amounts of data in real-time, ...

Unlike pattern-matching, which is about spotting connections and relationships, when we detect anomalies we are seeing disconnections—things that do not fit together. Anomalies get much less attention ...

We might be witnessing the start of a new computing era where AI, cloud and quantum begin to converge in ways that redefine ...