We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. The latest release includes: Let’s get started with the new operational overview dashboard which was built using Splunk’s brand new dashboard studio functionality which I highly recommend checking out. You can learn more about it in this recent tech talk which you can watch on demand.

The Splunk Threat Research Team has assessed several samples of Trickbot, a popular crimeware carrier that allows malicious actors to deliver multiple types of payloads.

As we start to see big moves from monolith deployments to microservices, the adoption of Kubernetes has become top of mind for many SREs. Organizations can leverage the open-source system to automate deployments, scale, and manage containers, making Kubernetes one of the primary solutions for delivering workloads. However, maintaining the system can be difficult and, in some cases, overwhelming.

Splunk Cloud Architect Paul Davies recently authored and released the GCP Application Template, a blueprint of visualizations, reports, and searches focused on Google Cloud use cases. Many of the reports included in his application require Google Cloud asset inventory data to be periodically generated and sent into Splunk. But HOW exactly do you craft that inventory generation pipeline so you can "light-up" Paul's application dashboards and reports?

Rapid digital transformation partnered with increased cloud adoption have resulted in organizations generating unprecedentedly large volumes of data. This data is stored in disparate data repositories due to organizational boundaries, data protection, and privacy laws (e.g. GDPR). Additionally, it is stored across environment types with some kept in the cloud and often historical data and other sensitive data types are kept in on-premise environments contributing to more data silos.