User and Entity Behavior Analytics (UEBA) establishes behavioral baselines for users and entities across your environment — detecting anomalous activity that indicates insider threats, compromised credentials, privilege escalation, and lateral movement that rules-based detection misses.
UEBA catches the threats that signature-based detection can't: the legitimate user account doing something it's never done, the service account accessing unusual systems, the executive downloading large volumes of sensitive data at 2 AM. RLM advises on UEBA platform selection and integration with your detection program.
A structured advisory process — from security posture assessment and market evaluation to vendor selection, contract negotiation, and post-deployment validation — tailored to your risk profile and compliance obligations.
We define the insider threat and compromised account scenarios most relevant to your organization — data theft, privilege escalation, account takeover, lateral movement — and prioritize the UEBA use cases with the highest risk-reduction value.
We evaluate UEBA platforms — Exabeam, Microsoft Sentinel UEBA, Securonix, Splunk UBA, and UEBA capabilities within broader XDR platforms — against your data sources, detection requirements, and integration with existing security tooling.
UEBA effectiveness depends on quality behavioral baselines. We design the baseline configuration approach — peer group definition, entity categorization, and the risk scoring model that surfaces genuinely anomalous behavior.
UEBA generates risk scores that require analyst investigation. We design the triage workflow — risk threshold alerting, investigation playbooks, and escalation criteria — that converts UEBA signals into security outcomes.
These are the dimensions that consistently separate effective security programs from expensive ones — and the questions RLM will help you answer before any vendor commitment.
UEBA accuracy improves with more behavioral data — authentication logs, DLP events, email activity, cloud application usage, and endpoint telemetry all contribute. Evaluate the platform's data source coverage for your specific environment.
UEBA systems that generate excessive false positives create alert fatigue. Evaluate the tuning mechanisms — peer group refinement, exception management, and risk threshold configuration — that maintain detection fidelity.
UEBA requires a baseline learning period — typically 30-90 days — before reliable detection is possible. Evaluate the onboarding timeline and the data requirements for building accurate behavioral baselines in your environment.
UEBA risk scores are most valuable when integrated into your SIEM investigation workflow. Evaluate native SIEM integration quality and the data model that enriches alert context with behavioral risk scores.
High-risk accounts — administrators, service accounts, executives — should receive enhanced UEBA coverage. Evaluate the platform's ability to apply differential monitoring intensity based on account risk classification.
Modern UEBA should model service accounts, endpoints, and applications — not just human users. Evaluate the non-human entity coverage that detects compromised service accounts and lateral movement by automated processes.
"RLM helped us build a security program that satisfied our board and our auditors — without locking us into a single vendor's roadmap. Their independence is the whole point."
"We had three overlapping security tools doing the same job. RLM helped us rationalize the stack, cut spend by 30%, and actually improve our detection coverage in the process."
Start with a no-cost conversation with an RLM security advisor — vendor neutral, no agenda, just clarity on where your gaps are and the right path to close them.
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