Relationships between the data elements of an attack can be as important as the individual data elements themselves. Uplevel preserves complete context of each alert by applying graph theory to all incoming cyber security event data. It then enriches the data with all relevant internal and external intelligence, performs the workflow enrichments necessary for response, and stores all of the data maintaining all of the relationships.
Uplevel then applies proprietary graph analysis algorithms to uncover patterns within the data which can be used to surface indirect relationships between incidents and attacks, identify the major classes of attack vectors, discover the distances between malicious entities, and identify threat actor TTPs for potential attribution.
When new alerts come in, direct relationships between past incidents and current indicators are automatically apparent as well as indirect relationships surfaced through advanced analysis. Teams immediately understand the context associated with an individual alert or security event, so they can take immediate action.
By having an aggregated, contextualized set of incident and threat data, organizations can automatically create and monitor the customized metrics they need to fully understand their cyber risk landscape and adapt to today’s dynamic persistent attacks.