Technology · 02

Artificial Intelligence Supported Threat Hunting Engine

Find the threat without waiting for the alarm: traditional SIEM/SOC setups rely on signature and rule-based alarms; Unknown or low-and-slow threats can slip through these rules. Our engine continuously learns behavioral anomalies in network and endpoint telemetry, prioritizing suspicious activity before a rule is triggered.

How Does It Work?

Behavioral Anomaly Detection

The model learns the environment's baseline of "normal" behavior (reference value) and scores and ranks actors, processes, or network flows that deviate from this baseline. [Model architecture, data sources and MITRE ATT&CK matching approach will be added]

integration[Supported SIEM/EDR/log sources will be added]

output[Prioritized alert format and integration details into SOC workflow will be added]

Unknown Threat Detection

It aims to capture previously unseen attack patterns that signature-based systems miss.

Continuous Learning Model

The model is updated as the environment behavior changes, the false positive rate decreases over time.

Reducing SOC Load

We preserve analyst time by providing prioritized, contextualized findings rather than raw alarm.

Control & Test Compatible

The engine's findings can be cross-verified with our penetration testing and BIGR/SGYM audit services.

The point that makes the difference: The engine is not used as a standalone product; We interpret and present with the same team as our BİGR/SGYM audit and penetration testing services — the finding is not disconnected from the compliance context. [Measurement/success metrics (detection time, false positive rate, etc.) will be added]

Our Other Technology · 01

Predictive Maintenance with Acoustic Fingerprinting

Review →

Scale Tech · Engineering Layer

Logging Analysis and Engineering of the SIEM Engine

Review →

Let's talk about how we can apply this technology in your organization

Let's clarify the pilot scope, integration duration and expected impact together.