AI-powered threat detection applies machine learning to security telemetry — identifying attack patterns, anomalous behaviors, and novel threats that rule-based detection systems miss, while correlating signals across data sources at a scale and speed that human analysts cannot match.
The volume and sophistication of modern threats have outpaced what rule-based detection can handle. AI threat detection is the path to maintaining detection quality as environments grow in complexity — but platform selection, model quality, and integration determine whether AI detection adds value or adds noise.
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 your current detection program — MITRE ATT&CK coverage, dwell time metrics, detection-to-alert latency, and the specific attack scenarios where rule-based detection consistently fails — identifying where AI detection would provide the most improvement.
We evaluate AI threat detection platforms — Darktrace, Vectra AI, Microsoft Sentinel ML, Exabeam, and XDR platforms with AI detection — against your telemetry sources, detection requirements, and the quality metrics that differentiate genuine AI capability from marketing.
AI detection requires environment-specific baseline establishment. We design the deployment approach — telemetry ingestion, baseline learning period, model configuration, and the validation methodology that confirms AI detection accuracy in your environment.
AI detection generates risk scores and behavioral insights that must integrate into analyst workflows. We design the SOC integration that presents AI detections with sufficient context for efficient analyst triage.
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.
AI detection marketing claims are difficult to validate without testing. Evaluate AI detection accuracy on your specific telemetry — request proof-of-concept engagements and measure detection rates and false positive rates in your environment before commitment.
Analysts who don't understand why an AI system flagged something won't trust it. Evaluate explainability quality — human-readable detection rationale that enables analysts to make confident triage decisions.
AI models require sufficient training data to generalize reliably. Evaluate minimum data volume and quality requirements for your environment — particularly for less-common cloud services or niche applications.
Sophisticated adversaries test their tools against AI detection systems. Evaluate whether the AI detection platform is tested against adversarial evasion techniques relevant to your threat model.
AI detection augments but doesn't replace rule-based detection. Evaluate the integration model — whether AI detections enrich existing SIEM alerts or operate as a parallel detection stream.
AI detection platforms that ingest all telemetry generate significant data processing costs. Evaluate pricing models carefully — per-event, per-user, and per-TB models can produce very different economics at your telemetry volumes.
"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.
Speak to a Security Advisor