Core Model Architecture

Specialized neural networks and ensemble systems trained on decades of cyber threat data.

Sentinel-7
v4.2.1

Multi-modal threat classifier specializing in network traffic analysis and protocol anomaly detection.

ProductionNetwork SecurityReal-time
Accuracy99.84%
Latency0.8ms
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Nexus-9
v3.0.0

Behavioral analytics engine mapping user and entity behavior baselines to detect insider threats and compromised accounts.

ProductionUEBAIdentity
Accuracy98.92%
Latency1.2ms
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PhantomX
v2.5.0

Zero-day vulnerability predictor using code pattern analysis and exploit simulation to forecast attack vectors before deployment.

BetaAppSecPredictive
Accuracy96.75%
Latency4.5ms
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Guardian-5
v5.1.0

Automated response orchestrator that correlates alerts across models and executes containment playbooks with human-in-the-loop validation.

ProductionSOAROrchestration
Accuracy99.21%
Latency2.1ms
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Data-to-Response Pipeline

How telemetry transforms into actionable security intelligence.

01

Ingestion & Normalization

Raw logs, packets, and endpoint telemetry stream into our zero-ETL pipeline, normalized into a unified security schema.

02

Feature Engineering

Contextual enrichment, graph relationship mapping, and temporal feature extraction prepare data for model inference.

03

Ensemble Inference

Multiple models run in parallel. Sentinel-7 flags anomalies, Nexus-9 validates against behavioral baselines, and PhantomX checks for novel exploit patterns.

04

Decision & Orchestration

Guardian-5 aggregates model confidence scores, correlates threats, and triggers automated response workflows or alerts the SOC.

Performance Benchmarks

Independently verified metrics across enterprise workloads.

Model Accuracy (F1) False Positive Rate Inference Latency Throughput Status
Sentinel-70.99840.02%0.8ms120k events/secStable
Nexus-90.98920.05%1.2ms95k events/secStable
PhantomX0.96750.18%4.5ms42k events/secBeta
Guardian-50.99210.01%2.1ms88k events/secStable
CipherNet-20.99450.03%1.5ms110k events/secStable

Primary Use Cases

Where our AI models deliver the highest security ROI.

🌐

Network Traffic Analysis

Real-time inspection of east-west and north-south traffic to detect lateral movement, C2 beacons, and data exfiltration attempts.

👤

Identity & Access Risk

Continuous authentication validation, anomaly detection in privilege escalation, and compromised credential identification.

☁️

Cloud Workload Protection

Container scanning, IAM policy analysis, and runtime protection for Kubernetes, AWS, Azure, and GCP environments.

📧

Email & Social Engineering

Deep inspection of attachments, URLs, and sender reputation to neutralize BEC, phishing, and malware delivery campaigns.

Technical FAQ

Common questions about our AI model infrastructure and deployment.

Are models trained on our proprietary data? +
Yes. CyberVault supports fully isolated tenant training environments. Your data never leaves your VPC, and model weights are encrypted at rest and in transit. We provide federated learning options for multi-cloud deployments.
How often are models retrained? +
Core models undergo continuous fine-tuning. Sentinel-7 and Guardian-5 receive incremental updates every 48 hours based on emerging threat intelligence. Full retraining cycles occur quarterly with validation by our ML security team.
Can we access models via API? +
Absolutely. Our REST and gRPC APIs allow real-time inference requests, custom rule injection, and webhook integration with existing SIEM/SOAR platforms. Enterprise customers get dedicated model endpoints with SLA guarantees.
What frameworks are used? +
Our stack is built on optimized PyTorch distributions, ONNX runtime for low-latency inference, and custom C++ kernels for cryptographic operations. We deploy across bare metal and GPU-accelerated cloud instances.

Ready to Deploy AI-Driven Defense?

Get architecture guidance, API keys, or a dedicated model benchmark for your environment.