Every digital ecosystem — whether it's a banking platform, healthcare portal, enterprise system, or e-commerce site — contains user behaviors that form patterns. Identifying when something “feels off” is key to preventing fraud, data breaches, identity theft, or insider threats.

However, traditional systems often miss subtle behavioral shifts or create too many false alerts.

The Challenge:

• Attacks are increasingly subtle and adaptive

• Traditional models rely on static rules or limited features

• High rate of false positives overwhelm analysts

• Hidden behavior correlations are often missed (e.g., low-frequency, multi-user collusion)

Quantum-Inspired Solution

Quantum-Inspired Algorithms like HessQ offer a more intelligent, adaptive way to detect anomalies — using optimization and high-dimensional pattern recognition — even when attacks are new or data is incomplete.

Behavior Mapping in High Dimensions

HessQ creates a quantum-inspired behavioral graph where normal patterns are identified, and deviations are flagged in real time

Uses QUBO models to determine which behaviors deviate meaningfully — improving both precision and recall

Instead of flooding your team with alerts, HessQ highlights the most probable threats with adaptive scoring

Optimized Anomaly Scoring via QUBO

Fewer False Positives, More True Threats

The Results:

• 60% reduction in false alarms

• +40% increase in real threat detection

• Detection time reduced from hours to minutes

• Subtle identity abuse, session hijacking, and AI-driven fraud better captured