The Bayence Platform

Detection that evolves
with the threat landscape

Bayence is an ensemble of models designed to understand what normal actually looks like, adapt as it shifts, and stop threats before they cause damage.

How Bayence Works

A fundamentally different approach to anomaly detection.

Adaptive Anomaly Detection

We unify deep reconstruction, generative, and sequence-aware models into a single adaptive system. Subtle structural shifts, evolving patterns, and precise, context-driven responses become actionable.

Multi-Model Ensemble

During inference, our product correlates results from all relevant models to provide accurate, actionable events. We apply a novel ensemble-based approach versus the standard "one-model-fits-all".

Minimal Setup

Deploy seamlessly in a variety of network environments with a simple, efficient setup process. No complicated rules tuning or lengthy configuration required.

Actionable Insights

Our automated scoring engine provides security teams with clear, contextual information to rapidly respond to threats. Know what matters and why.

Key Differentiators

What sets Bayence apart from legacy detection systems.

Hyperautomation

Why do it?

  • → Risk vectors are constantly changing
  • → Bayence unifies model ensemble outputs through one statistical foundation
  • → The system fine-tunes itself with expert precision
  • → Reactive security becomes continuous improvement

No One-Size-Fits-All

Customized for your environment

  • → Customers have different data, environments, and use cases
  • → Self-learning methods adapt to your specific network
  • → Self-adjusting systems stay ahead of risks
  • → No manual rule writing or threshold tuning

Focus on Meaningful Anomalies

Signal over noise

  • → >99% of traffic is uninteresting and harmless
  • → We automate awareness of what makes up the <1%
  • → Builds understanding that grows stronger over time
  • → Eliminates alert fatigue for security teams

Platform Architecture

A multi-layer system designed for real-time detection at scale.

1

Ingest

Data processing, scaling, and per-model splitting

2

Training

Model configuration, verification, and playbook testing

3

Inference

Real-time detection with threshold evaluation

4

Automation

Label fusion, LLM-powered decisions, and response

Use Cases

Real-world applications where Bayence excels.

Pulsed DDoS Detection

Detect advanced, low-and-slow DDoS attacks that evade traditional volumetric detection.

Carpet-Bomb Attack Detection

Identify distributed many-to-many attack patterns across your infrastructure.

Network Drift Detection

Automatically identify when network behavior changes in ways that indicate compromise.

Triage Reduction

Reduce the time and effort required to investigate security alerts by orders of magnitude.

See Bayence in Action

We're working with design partners to shape the platform. Get early access and help define the future of anomaly detection.

Request a Demo