Prelert automates analysis of your IT and security log data, finds anomalies for you, links them together, and presents you with your data story, so not only will you understand what happened, but how it happened.
IT Security professionals can protect from advanced threats with security analytics. Machine learning anomaly detection finds the fingerprints of criminal activity in real-time so that you can detect problems early and act fast.
IT Ops, APM and DevOps professionals no longer need to use antiquated technology. With machine learning anomaly detection you can automate data analysis and detect problems early so that you can act fast - before users detect an issue.
Dr. Larry Ponemon joins our VP of Security Products, Mike Paquette, to discuss the results of this Ponemon Institute study on advanced threat detection.
Our VP of Security Products, Mike Paquette, demonstrates use cases that show how to know normal and detect abnormal by using machine learning anomaly detection.
Rich Collier shares real use cases to demonstrate how businesses have transformed their IT Operations with machine learning anomaly detection.
At VentureBeat's 2014 big data conference, DataBeat, Prelert participated in the Innovation Showcase. At the event we discussed the importance of analytics, the significance of anomaly detection for end users, and the future of anomaly detection and machine learning.
Prelert's Product Manager provides an overview of our Anomaly Detective engine solving a difficult security case leveraging the Elasticsearch ELK stack.
Originally delivered at Data Science London, this talk looks at the problem of anomaly detection in large scale IT environments. Rule-based approaches are unmanageable and can't handle the complexity and scale of the data. Advanced Data Mining techniques are useful for improving the quality and scale of anomaly detection.
Length: 30:25, 22:44
In this two-part video from the recent IEEE conference keynote, Prelert's Sr. Director of Cybersecurity, Oleg Kolesnikov discusses practical applications of Machine Learning-based Anomaly Detection (MLAD) in the area of Information Security/Cybersecurity, including detection of Advanced Cyberthreats. The full abstract of the talk is available here
Prelert's Director of Research and Development discusses anomaly detection from a more technical perspective, points out some of the data characteristics which make anomaly detection for real world problems challenging and describes some of the techniques we use for anomaly detection.
In this short video Prelert's CTO, Steve Dodson, explains the approach and analytics that make real-time anomaly detection possible on all Splunk data.
Responsys' Sr. Security Architect shows how he is detecting advanced threats and reducing thousands of IDS alerts to an actionable few with Prelert's Anomaly Detective.
SNAP Interactive deploys dozens of production changes a day making traditional monitoring based on thresholds pretty much useless. Their Principal Architect, Nick DiSanto, explains why the say "Prelert + Splunk = Winning Combination."
Director of Product Management, Rich Collier explains Prelert's mission, 'data science packaged for everyday decisions,' and how that manifests in our automated anomaly detection analytics product for Splunk.