Anomaly detection powered by AI identifies deviations from normal behavior across users, devices, networks, and applications — catching insider threats, compromised accounts, data exfiltration, and advanced persistent threats before traditional alerts would fire.
Most enterprise security tools catch what they're configured to look for. Anomaly detection catches what you weren't expecting — and it's often what you weren't expecting that becomes your most serious breach.
Every engagement follows a structured process — from discovery and vendor evaluation to pilot design and scale — adapted to the specific constraints and maturity of your organization.
We identify the specific anomaly detection use cases most relevant to your environment — insider threat, compromised credential, data exfiltration, cloud misconfiguration drift — and prioritize the telemetry sources and platforms that address them.
We evaluate User and Entity Behavior Analytics platforms — Splunk UBA, Microsoft Sentinel UEBA, Securonix, Exabeam, and others — against your data sources and analyst workflow.
Anomaly detection generates value only when baselines accurately reflect normal behavior and sensitivity is calibrated to reduce noise. We design the initial calibration process and ongoing tuning methodology.
Anomaly alerts require context-rich investigation workflows. We design the integration between anomaly detection, your SIEM, SOAR, and case management to make anomaly-driven investigations efficient.
These are the evaluation dimensions that consistently separate successful deployments from expensive pilots that never reach production scale.
Users, service accounts, endpoints, servers, cloud workloads, network devices — comprehensive entity coverage is essential for detecting lateral movement and multi-stage attacks that cross entity boundaries.
Simple statistical baselines generate excessive noise. Evaluate whether the platform uses peer group analysis, time-of-day modeling, and multi-dimensional behavioral profiles that reflect the complexity of real enterprise behavior.
How quickly does the platform establish reliable baselines for new users and entities? Extended cold start periods delay detection coverage for new hires, contractors, and cloud resources.
Individual anomalies are often noise. Evaluate how the platform combines multiple weak signals into cumulative risk scores that surface truly suspicious entity behavior.
Anomaly detection is most powerful when correlated with identity events — logins, privilege changes, role assignments. Evaluate depth of integration with Active Directory, Okta, Azure AD, and PAM systems.
Insider threats have unique behavioral signatures — access pattern changes, data staging, after-hours activity, policy violations. Evaluate specific insider threat detection capability beyond generic anomaly identification.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought."
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
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