Latest Videos

Keeping Your CMDB Up To Date in Distributed Times

The configuration management database (CMDB) is meant to be a single source of truth to link IT elements with the application processes that underlie the business services. In the age of ITIL, a common repository to store information about your hardware and software assets, made sense. But with today's dynamic and distributed hybrid IT infrastructure, how do you keep your CMDB up to date? Should you even try?

Introduction into Eland - DataFrames and Machine Learning backed by Elasticsearch

Introduction into Eland, a Python package to analyse, explore and manipulate Elasticsearch data. In this talk, Seth Larson introduces us to Eland. Eland is a Python Elasticsearch client for exploring and analyzing data residing in Elasticsearch with a familiar Pandas-compatible API.

Intelligence Driven Threat Hunting with SOAR

Most security teams face the same challenges when it comes to their ability to be proactive: skills shortages, lack of visibility into weaknesses and the incapacity of internal resources to detect and eliminate threats. Cyberint’s new solution uncovers existing compromises, malicious activity, persistence, and residuals from past breaches with an intelligence-driven approach to hunt down threats. When managed threat hunting is combined with the power of security orchestration, automation and response (SOAR), organizations can obtain critical context about attacks in real-time, streamlining the response process. How managed threat hunting helps businesses be proactive about their security. Why it’s critical to onboard managed threat hunting service at a time when global challenges like COVID-19 create business disruption and change organizations’ digital environments for months or years to come The types of threats that can be discovered during a threat hunt – from active attacks to the remnants of past intrusions. How leveraging SOAR technology can help automate hunts and better manage security incidents, from identification to remediation, through custom playbooks. Presented By Adi Perez - VP Technology, CyberInt Nimmy Reichenberg - Cheif Marketing Officer, Siemplify

Creating a Scalable and Repeatable Threat Hunting Program with Carbon Black and Siemplify

According to SANS, 82% of all SOCs are investing in advanced Threat Hunting programs, but that is no simple task. Many organizations struggle with incorporating threat hunting into their security operations efforts due to a lack of expertise. Creating an effective threat hunting program requires a combination of the right tools and the right processes. The combination of flexibility and automation opens up the ability for anyone in the security operations center to perform threat hunting at scale.

Kublr, enterprise-grade Kubernetes -- an overview (short demo)

Centrally deploy, run, and manage Kubernetes clusters across all of your environments with a comprehensive container orchestration platform that finally delivers on the Kubernetes promise. Optimized for large enterprises, Kublr is designed to provide multi-cluster deployments and observability. We made it easy, so your team can focus on what really matters: innovation and value generation.

Make Your Data Fabrics Work Better

To gain the full benefits of the DataOps strategy, your data lakes must change. The traditional concept of bringing all data to one place, whether on-premises or in the cloud, raises questions of timing, scale, organization and budget. The answer? Data fabric. It replaces traditional data lake organization concepts with a more flexible and economical architecture. In this session, we'll define what a data fabric is, show you how you can begin organizing around the concept, and discuss how to align it to your business objectives.

A Dose Of Data Science Demystification

Join two data engineers and analysts in pulling back the curtain on real customer engagements, showing how to select and implement advanced data science and analytic techniques. In this session we will discuss our implementation of two data science models at a large agricultural products manufacturer: a propensity-to-buy model and a recommendation engine. We will discuss how each of these models works and how they were implemented for our client.