What type of method does the Asset and Identity frameworks utilize in Splunk?

The Asset and Identity frameworks in Splunk primarily utilize statistical modeling to analyze data effectively. This approach allows for the identification of patterns and anomalies in asset and identity behaviors across the network. Statistical modeling helps in quantifying risks and determining the likelihood of various security incidents. By leveraging statistical methods, Splunk can contextualize the data, enabling security analysts to make informed decisions based on insights derived from data patterns rather than relying solely on anecdotal information or manual processes.

This method is essential for dynamic environments, where the analysis of large volumes of data can reveal underlying issues that may not be immediately apparent through manual entry or simplistic tagging frameworks. Though other options may involve aspects of data management, they do not offer the analytical depth that statistical modeling provides in assessing assets and identities effectively within the context of cybersecurity.