Adaptive analysis identifies unusual communication activity and supports policy-driven response with traceable decision context.
Every message and session is evaluated against multiple threat vectors before a policy decision is made.
Identifies automated traffic, credential stuffing, and scripted abuse patterns.
Analyzes message content, URLs, and sender patterns for phishing indicators.
Flags unusual volume spikes, destination patterns, and timing anomalies.
ML models learn normal behavior per account and flag deviations in real-time.
Detects sender name spoofing, brand impersonation, and social engineering attempts.
Identifies repeated or reused message payloads and suspicious retry patterns.
Multiple signals are weighted and combined into a real-time risk score per message.
Past delivery rates, complaint ratios, and carrier block history.
NLP-powered scan for spam keywords, phishing links, and suspicious patterns.
Device fingerprint, IP reputation, and session behavior anomalies.
Country-level fraud rates, DND registry status, and carrier block lists.
Sudden spikes, unusual hours, and deviation from established sending patterns.
Historical open rates, opt-out velocity, and complaint trends.
Based on the risk score, the system takes proportionate action in real-time.
Message routes normally through carrier networks.
Message flagged for monitoring. Sent with enhanced logging.
Rate-limited. Sender notified. Manual review recommended.
Message blocked. Account suspended pending investigation.
From message submission to policy action — fully automated, fully auditable.
Collect sender identity, device signals, content analysis, and destination risk data.
ML models evaluate all signals against learned patterns and configured thresholds.
Policy engine applies proportionate action and records the decision rationale.
Every risk score is explainable. Every action is auditable. Every decision retains its context for governance review.