Beyond Static Rules: AI Agent Surveillance
Move beyond rigid logic. Discover how AI Agents observe behavior, adapt to novel patterns, and escalate with rationale to reduce false positives and catch hidden risks.
Move beyond rigid logic. Discover how AI Agents observe behavior, adapt to novel patterns, and escalate with rationale to reduce false positives and catch hidden risks.
Static Rules Miss Novel Patterns
Traditional surveillance relies on pre-defined "if/then" logic. See how rigid structures fail against evolving threats.
Novel Threat
Toggle below to see the difference between static rules and adaptive AI.
How the Agent Works
The AI Agent doesn't just match rules; it understands context. Click the steps below to explore the workflow.
Holistic monitoring across channels.
Dynamic logic evolution.
As patterns evolve (e.g., new fraud techniques), the agent updates its detection logic automatically, without waiting for manual rule rewrites.
High-signal alerts with reasoning.
Instead of just flagging a transaction, it provides:
Select a use case below to see how the AI Agent analyzes real-time data streams to identify complex risks that rules might miss.
Monitoring transaction velocity for potential structuring patterns.
Context & Rationale Engine
Analyzing recurring transaction sequences below reporting thresholds.
Anomaly Detected: Multiple small transfers totaling $9,800 within 24 hours. Pattern matches evolved 'structuring' behavior not caught by single-transaction limits.
Higher Signal, Fewer False Positives.
AI Agents reduce noise by understanding context, ensuring investigators focus on genuine risks rather than false alarms.
Security doesn't degrade over time. As bad actors shift tactics, the Agent learns and adapts logic instantly.