Conventional SIEM technologies focus on Events, providing filtering, rules, and basic analytics to display events. Unfortunately, most SIEM products still deluge the Security Operations team with a flood of information, which makes it hard to prioritize events by their actual risk. The conventional SIEM paradigm is to present Events and Incidents, as identified by rules-based analytics, without context.
An analytics-driven SIEM takes a different approach. Attend this webinar to understand how leveraging Artificial Intelligence and Machine Learning on massive volumes of data brings context to an otherwise siloed security picture and highlights risks in ways conventional SIEM’s can’t. Detect unknown attack chains via machine learning and advanced analytics:
• Establish baselines of normal activity and detect anomalies with machine learning
• Provide situational awareness with real-time analytics, identifying risks before they develop into incidents
• Correlate and analyze events across the entire environment to deliver a consolidated view of the entire threat situation
• Link events and related data into security incidents, threats, or forensic findings
• Enrich the context of security alerts to make it easier to investigate and detect elusive threats
• Prioritize incidents to understand which incidents are particularly abnormal or dangerous