Elastic launches Workplace Search

Elastic N.V., creators of Elasticsearch, has announced the release of Elastic Stack 7.6.0, the latest version of the all-in-one datastore, search engine, and analytics platform. This release streamlines automated threat detection and brings performance improvements to Elasticsearch, which was launched as a beta in May 2019 and is now known as Elastic Workplace Search.

Elastic has “up-levelled” Elastic Enterprise Search as the new “umbrella” solution name that encompasses its suite of search products. Workplace Search allows teams and organizations to search and discover all the content scattered across the many tools that power the modern workforce. It’s the single search box for all your work data. Learn about meta engines and other enhancements in the Elastic Enterprise Search update.

Elastic Stack 7.6.0 includes the launch of a new SIEM detection engine and a curated set of detection rules aligned to the MITRE ATT&CK knowledge base. It makes supervised machine learning more turnkey with inference-on-ingest features and deepens cloud observability and security with the launch of new data integrations. Version 7.6 is available now on Elasticsearch Service on Elastic Cloud - the only hosted Elasticsearch offering to include these new features. Or you can download the Elastic Stack for a self-managed experience.

Managing the search experience across multiple sites and business units can be a difficult undertaking for large companies. Elastic App Search 7.6 introduces meta engines, document-less engines that query a set of engines. With meta engines, organizations get the ability to unify search across multiple engines from a single search bar, while still allowing admins complete control over the behaviour of each individual sub-engine.

This feature will be available for App Search on Elastic Cloud and the self-managed version.

Elastic has dramatically improved the performance of queries that are sorted by date or other long values by applying the block-max WAND optimization to sorted queries — a clever way to stop counting new results when they're clearly not going to change the results.

Sorting on time is one of the most common tasks in observability and security use cases. Chasing down an error in the Elastic Logs app or investigating a threat in Discover are just a few of the many things that will be faster by simply upgrading to 7.6.

Elastic’s goal with machine learning in the Elastic Stack has always been to make it so easy that anyone in an organization can use it. With the first release in 5.4, Elastic has made detecting anomalies as easy as building a visualization in Kibana — making this accessible to a broader audience and making data science teams even more efficient.

With 7.6, Elastic brings end-to-end supervised machine learning capabilities to the stack, from training a model to using the model for inference at ingest time. The goal is to make supervised machine learning methods like classification and regression in Elasticsearch even more turnkey for practitioners across observability, security, and enterprise search use cases. For instance, a security analyst can now build a bot detection model using classification and then use the new inference ingest processor to infer and label new traffic as a bot (or not a bot) at ingest time - all natively within Elasticsearch.

As with unsupervised learning and anomaly detection, the goal here is to make supervised machine learning easy and accessible to everyone. So, instead of building a generic data science toolkit or providing integration to external machine learning libraries that require users to cobble together and maintain complex workflows that move data across multiple tools, Elastic has focused on simplifying common use cases. With this approach, Elastic is unlocking new use cases and keeping the operational side of things simple.

Elastic is also including a language identification model that can be used in the inference ingest processor to label the language on documents at ingest time. Language identification is key to so many use cases. For example, a support centre can use this feature to route an incoming question to the right support agent or support location based on the language, and you can use it to make sure incoming text is indexed properly in Elasticsearch.

“As the team responsible for the Wi-Fi subway network on public transit systems in New York City and Toronto, we are acutely aware of the need to detect system issues and connectivity anomalies. This ensures we can provide quality connections for millions of daily commuters. In 2017, we turned to anomaly detection powered by unsupervised machine learning from Elastic to detect issues that may have been otherwise missed in real time, minimizing impact on network performance,” said Jeremy Foran, Technology Specialist at BAI Communications.

“As we look to the future and the onboarding of more transit systems across the world, we will continue to leverage the supervised machine learning features in Elastic Stack 7.6 to bring new networks online.”

For more details on all of these features and more, check out the Elasticsearch 7.6 blog post and Kibana 7.6 blog post.

Elastic Security

Elastic Security version 7.6 introduces a new SIEM detection engine to automate threat detection and minimize mean time to detect (MTTD). With Elasticsearch at its core, Elastic SIEM already reduces security investigation time from hours to minutes. With this new automated detection capability, Elastic is reducing dwell time by surfacing threats that would otherwise be missed.

Elastic is also launching nearly 100 out-of-the-box rules aligned with the ATT&CK knowledge base that can help surface threat signals that are often missed by other tools. Created and maintained by the security experts at Elastic, these rules are designed to automatically detect the tools, tactics, and procedures indicative of threat activity. Risk and severity scores of signals generated by the detection engine help analysts with efficient triage and to focus on the things that matter most.

The detection engine and the prepackaged rules are included in the free Basic tier of Elastic Security — making automated analysis at scale openly accessible to security practitioners everywhere.

Windows systems are a major attack target due to their popularity and lenient user permissions model. This release deepens visibility into Windows activity, collecting and enriching data from locations that are traditionally vulnerable to the evasion techniques of advanced threats. New out-of-the-box detections leverage this data to detect attempts to capture keyboard inputs, load malicious code into other processes, and more. Practitioners can pair events generated by these detection rules with automated responses (e.g., kill a process) to achieve layered prevention.

With this functionality, Elastic Security is bringing unprecedented levels of visibility and protection to enterprises with large Windows footprints, and at a price point accessible to every analyst.

Elastic SIEM is generally available in 7.6. In addition to the detection engine, it includes a redesigned Overview page and a slew of UX improvements to help speed up threat hunting, triage, and investigation. New integrations into Amazon Web Services (AWS) and Google Cloud Platform (GCP) logs enable stronger cloud security. Get all the details in the Elastic Security 7.6 announcement post.