Big data security is a term used for all collective measures taken to protect both data and analytical processes from theft, attacks or all other malicious activities. Just like other forms of cybersecurity, big data security is about attacks originating from every online or offline sphere. Companies operating on the cloud face multiple challenges including online information theft, DDoS attacks and ransomware.

Let’s talk visibility for a moment. Security visibility is a data-at-scale problem. Searching, analyzing, and processing across all your relevant data at speed is critical to the success of your team’s ability to stop threats at scale. Elastic Security can help you drive holistic visibility for your security team, and operationalize that visibility to solve SIEM use cases, strengthen your threat hunting practice with machine learning and automated detection, and more.

As companies responded to the COVID-19 pandemic with remote work, cybercriminals increased their social engineering and ransomware attack methodologies. Ransomware, malicious code that automatically downloads to a user’s device and locks it from further use, has been rampant since the beginning of March 2020. According to a 2020 report by Bitdefender, ransomware attacks increased by seven times when compared year-over-year to 2019.

While working with customers over the years, I've noticed a pattern with questions they have around operationalizing machine learning: “How can I use Machine Learning (ML) for threat detection with my data?”, “What are the best practices around model re-training and updates?”, and “Am I going to need to hire a data scientist to support this workflow in my security operations center (SOC)?” Well, we are excited to announce that the SplunkWorks team launched a new add-