Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As more workloads migrate to the cloud, the attack surface continues to grow, and cybercriminals become more sophisticated, security teams are struggling to monitor and analyze an ever-increasing volume of data so they can quickly distinguish real threats from numerous false positives. Join this webinar to learn how Devo Security Operations, our next-gen cloud-native SIEM, can empower your SOC team.

Modern enterprises face evolving threat vectors, combination attacks, and coordinated campaigns, all contributing to an ever-expanding defense surface. Mature SecOps teams are shifting their strategy to tackle these challenges with proactive threat hunting a high priority. Join our Threat Hunting Showcase session to learn how Devo delivers blistering performance and 400 days historical data, allowing you to proactively identify IOCs across all your data.

People create risk every day. They're on the frontline of phishing attacks. They make mistakes and they also break the rules. Insider risk is the most complex challenge organizations face. In this live webinar, cybersecurity expert Lisa Forte will explain the psychology behind the breach, and how organizations can flip insider risk on its head to make people your greatest defense.

Cybersecurity specialists treat remote employees as a threat, and they're right to do so. However, remote work isn't a temporary trend - it's here to stay. Are you ready to ensure effective remote employee monitoring and secure remote access to your corporate systems? How can you make sure your employees stay productive and don't get distracted by domestic chores? We'll discuss all these questions during our webinar.

SIEM is continuously evolving and today's SIEM software, with forensic capabilities to piece together events after the fact, can support big data and provide credible risk assessments. Security analytics must adapt to changing threats while integrating with new technologies and increasing flexibility. The webinar discusses the major trends in SIEM and how Elastic Security addresses and adopts these trends.

Unsupervised machine learning (ML) is a core capability for most security operations teams looking to implement an advanced threat detection or insider threat program. However, the deployment of ML can present adoption challenges for security teams. Unless they have in-house data scientists to develop and tune threat models and skilled threat hunters to investigate alerts and manually follow up on interpreting anomalous behaviors, teams may find themselves struggling to gain useful insights and operational value out of ML tools.

Get your Elastic skill set up to speed with Thorben Jändling, Senior Solutions Architect in Elastic's global security specialists group.

Unsupervised machine learning (ML) is a core capability for most security operations teams looking to implement an advanced threat detection or insider threat program. However, the deployment of ML can present adoption challenges for security teams. Unless they have in-house data scientists to develop and tune threat models and skilled threat hunters to investigate alerts and manually follow up on interpreting anomalous behaviors, teams may find themselves struggling to gain useful insights and operational value out of ML tools.

Threat hunting - the proactive pursuit and elimination of adversaries before they cause damage and loss - can help analysts and security teams better understand where important assets reside, such as privileged accounts and sensitive business systems, and provide deeper visibility into activities associated with those assets.

Unsupervised machine learning (ML) is a core capability for most security operations teams looking to implement an advanced threat detection or insider threat program. However, the deployment of ML can present adoption challenges for security teams. Unless they have in-house data scientists to develop and tune threat models and skilled threat hunters to investigate alerts and manually follow up on interpreting anomalous behaviors, teams may find themselves struggling to gain useful insights and operational value out of ML tools.