Surface Hidden Threats Across Your Entire Environment
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.
What RLM Delivers on AI Anomaly Detection
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.
How We Approach AI Anomaly Detection
Every AI Anomaly Detection engagement starts with what you have today and ends with something running in production — with independent evaluation in between.
Anomaly Detection Use Case Scoping
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.
UEBA Platform Evaluation
We evaluate User and Entity Behavior Analytics platforms — Splunk UBA, Microsoft Sentinel UEBA, Securonix, Exabeam, and others — against your data sources and analyst workflow.
Baseline Calibration & Tuning Design
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.
Investigation Workflow Integration
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.
AI Anomaly Detection Selection Criteria
What follows is the AI Anomaly Detection evaluation checklist we actually use — the criteria that predict outcomes rather than demo well.
Entity Coverage
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.
Baseline Sophistication
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.
Time-to-Baseline & Cold Start
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.
Risk Scoring & Prioritization
Individual anomalies are often noise. Evaluate how the platform combines multiple weak signals into cumulative risk scores that surface truly suspicious entity behavior.
Integration with Identity Systems
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 Threat Detection Specifics
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.
"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."
Independent means we will tell you when the answer is to keep what you have.
Where This Matters Most
Sector-specific considerations we see repeatedly in ai and automation engagements.
A Sample of the AI & Automation Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Ready to Get AI Anomaly Detection Right?
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
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