Detect Threats at Machine Speed and Scale Using AI
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.
What RLM Delivers on AI Threat Detection
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.
How We Approach AI Threat Detection
A structured path through the AI Threat Detection decision — current-state discovery, shortlist and benchmark, commercial negotiation, then support until it is actually working.
Detection Gap Analysis
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.
AI Detection Platform Evaluation
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.
Model Deployment & Baseline Design
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.
SOC Integration & Analyst Workflow
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.
AI Threat Detection Evaluation Criteria
Before committing to any AI Threat Detection platform, these are the points worth forcing a straight answer on.
Detection Accuracy Validation
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.
Explainability for Analyst Trust
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.
Training Data Requirements
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.
Adversarial Evasion
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.
Integration with Existing Detection
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.
Cost at Scale
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."
Every engagement is measured against the baseline we establish at the start — not against a vendor’s projection.
Where This Matters Most
Sector-specific considerations we see repeatedly in security engagements.
A Sample of the Security 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 →
Thinking About AI Threat Detection?
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.
Talk to a Security Advisor