Which SIEM platforms provide user behavior analytics to detect insider threats?

By monitoring actions such as file access, login attempts, and application usage, UBA identifies anomalies that could indicate malicious behavior or compromised accounts. These platforms leverage machine learning algorithms to establish baseline user behavior, allowing them to flag deviations that may signify a security risk. Additionally, UBA in SIEM platforms correlates disparate data sources to provide comprehensive insights into user actions, enabling security teams to swiftly investigate and respond to potential threats. This proactive approach helps organizations mitigate the risks posed by insider threats and safeguard sensitive data.

SIEM and UBA do not mix well. Most UBA products and platforms started as standalone or pureplay vendors, such as Caspida (acquired by Splunk in 2015) One of the main issues with Caspida integrating to Splunk ES was that the data architectures do not interoperate and cannot be condensed. Splunk's SIEM core, built on a modified Hadoop, is hinged on the Lambda data architecture, while Caspida's UBA is built on a modified Kafka which is steeped in the competing Kappa data architecture. Said differently and oversimplified, SIEMs want data query via non-volatile recordkeeping and UBA wants data collection via volatile memory stores

SIEM platforms leverage user behavior analytics to detect insider threats, offering real-time monitoring and anomaly detection. By analyzing user activities across systems, they identify unauthorized access and unusual behavior. Machine learning improves detection accuracy over time, while risk scoring prioritizes alerts for efficient response. Ultimately, these analytics enhance security by swiftly identifying and mitigating insider threats, safeguarding valuable data and assets.

In a majority of use cases for UBA, other non-UBA platforms which keyed into these use cases provided stronger correlation, alerts, notables, and collection by comparison. For example, take the Cmd (@cmd_security) acquisition by Elastic -- which is a platform built for identification of trusted insider threats by looking at behaviors and activities (much like UBA!) but as sets, tagsets, and across infrastructure enclaves -- especially to include devops, cloud-mixed, cloud-native, and various workload architectures Cmd is capable of good SIEM integration because of a focused data collection and consolidation of otherwise-mismatched artifacts. Great for mixed trusted and untrusted insider alerting as well

A powerful tool for detecting suspicious activities within an organization's network. UBA analyzes user actions, such as logins, file accesses, and data transfers, to establish a baseline of normal behavior for each user. Any deviations from this baseline, such as unusual access patterns or unauthorized data downloads, trigger alerts for further investigation. By correlating diverse data sources and employing advanced machine learning algorithms, UBA can identify potential insider threats more accurately and efficiently than traditional methods. This proactive approach enables organizations to mitigate risks posed by malicious insiders or compromised accounts, safeguarding sensitive data and maintaining the integrity of their systems.

Much as I shared the Caspida-to Splunk and Cmd-to Elastic journeys in my above answers, I can share an expert-led anecdote as a commentary on Insider Threats here and now Insider threats are primarily unintentional insiders. Others are trivial non-insider events, such as what most UBA platforms flag as Flight-risk users (e.g., end users who upload their resumes while looking for jobs on company time and dime) What a cybersecurity manager or architect is ideally looking for when solving for UBA use cases is to understand trusted vs unintentional insiders and where these associations collide

A lot of modern SIEMs such as Microsoft Sentinel externalize UEBA to a specific Kusto-query cluster (BehaviorAnalytics) and an otherwise-separate product, i.e., Microsoft Defender for Cloud This is not uncommon because of the systemic issues forcefully merging UBA with SIEM. This is compounded by attempting to solve use cases with the wrong product (UBA) into the wrong endstate (SIEM). Go back to the use cases and see if even SIEM is a fit: a CIEM or SIEMLake might be better options for cloud and data-championed environs Best of all might be a high-viz, guided-hunt platform that mirrors all of the SIEM and XDR concepts for answering user and entity questions in-hunt instead of in-SIEM