Insider Threat Detection
Whether actions that are either mistaken or malicious, insiders remain a source of risk
that can expose organizations to an onslaught of novel and previously unseen attacks.
Minimizing or preventing sensitive data loss within enterprises requires close monitoring for deliberate acts of exfiltration.
Security teams face challenges in meeting these needs, including:
- Access to event data
- Limited knowledge of advanced analytics
- Mean time to mitigation (MTTR)
- Lack of tools intended for security analysts
Most of today’s SIEM tools cannot fully identify behaviors that are indicative of data exfiltration
Elysium’s Cognitive SIEM uses existing open source big data technology to collect data in real-time to detect and score anomalous behavior with unsupervised and supervised machine learning models.
Our Cognitive SIEM keeps a baseline for every user in the organization then calculates
the deviation from the baseline of each user’s own history to aid in threat detection.Additional uses cases such as data exfiltration are available.
Legacy SIEMs and other solutions cannot detect this with certainty.
- Automatic data exfiltration detection and remediation before a breach becomes front-page news
- Big Data technology with unsupervised and supervised machine learning models to quickly identify threats that humans will miss
- User baselines to automatically detect deviations, reducing false/positives for analysts