Everything that makes employees’ lives easier, makes yours harder. Detecting insider threats — both employees and cybercriminals pretending to be employees — has never been more difficult or more important. The cloud technologies that make everyone else more efficient make security less efficient. They’re noisy. They send a lot of alerts. You’re tired. You’re overworked. You’re overloaded.

The large amounts of behavioral data being generated today necessitate accurate labels for machine learning classifiers. In an earlier blog post, Large-Scale Endpoint Security MOLD Remediation, we discussed how to remediate labeling noise. In this blog post, we experiment with an unsupervised approach that eliminates the need for learning from labeled data.

We’re excited to share several recent user experience improvements we’ve made across the platform, including multivariate anomaly detection and other new features aimed at improving content governance. Continue reading to learn about some of our top product releases for October.

Securing your environment requires being able to quickly detect abnormal activity that could represent a threat. But today’s modern cloud infrastructure is large, complex, and can generate vast volumes of logs. This makes it difficult to determine what activity is normal and harder to identify anomalous behavior. Now, in addition to threshold and new term –based Threat Detection Rules , Datadog Security Monitoring provides the ability to create anomaly

One of Bearer's super powers is anomaly detection. Anomalies are unexpected issues that happen when making an API call. These could be high error rates, unexpected response codes, latency spikes, and more. By monitoring APIs with anomaly detection, we can identify problems with an API or within your application. Anomaly detection makes debugging easier and can help you identify API performance issues that affect your end users.