Security behavioral analytics establishes statistical baselines for normal activity across users, endpoints, applications, and networks — detecting deviations that indicate compromised accounts, insider threats, malware behavior, and lateral movement through anomaly detection that doesn't require prior knowledge of the attack.
Signature-based detection fails against novel attacks, living-off-the-land techniques, and slow-burn insider threats. Behavioral analytics provides detection capability that scales with attack sophistication — any behavior that deviates significantly from established patterns triggers investigation.
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 assess the behavioral telemetry available in your environment — authentication events, endpoint telemetry, network flows, application logs, and DLP events — and identify the data gaps that limit behavioral analytics coverage.
We evaluate security behavioral analytics platforms — Exabeam, Microsoft Sentinel UEBA, Securonix, Splunk UBA, and integrated behavioral analytics in XDR and NDR platforms — against your data sources and the specific behaviors you need to detect.
We design the baseline configuration — entity categorization, peer grouping, feature selection, and the weighting model — that accurately represents normal behavior in your specific environment.
We design the risk score integration with your SIEM and SOAR — ensuring behavioral risk scores enrich analyst investigations, trigger automated enrichment, and appear in the right place in the analyst workflow.
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.
Behavioral analytics is only as good as the behavioral baselines established. Evaluate the baseline learning period required and the data quality needed — poor-quality or insufficient behavioral data produces unreliable baselines and high false positive rates.
Behavioral analytics compares users to their peers. Evaluate the peer group definition quality — an executive being compared to general employees will generate excessive anomalies; comparison to other executives provides meaningful deviation detection.
Service accounts, privileged users, and contractors represent disproportionate risk. Evaluate the platform's ability to apply enhanced behavioral monitoring to high-risk entity categories.
Behavioral analytics generates risk scores, not discrete alerts. Evaluate the alert prioritization model — whether risk scores accurately surface the most dangerous behaviors vs. generating noise from unusual-but-benign activity.
Behavioral analytics that incorporates identity context — role, department, recent HR events, access certification status — provides significantly more accurate anomaly detection. Evaluate identity data integration depth.
Single-source behavioral analytics misses multi-step attacks that each appear normal in isolation. Evaluate cross-source correlation capability — detecting the sequence of anomalies that together constitute an attack.
"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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