🎪 Threat Engine

⚡ Core Capabilities

🧠

Behavioral AI Classification

Unsupervised learning models detect anomalous entity behavior across endpoints, network, and cloud workloads without relying on signatures.

🔍

Zero-Day Heuristics

Sandbox-less dynamic analysis identifies exploit chains, obfuscated payloads, and fileless attacks before they execute.

⚡

Real-Time Telemetry Ingestion

High-throughput stream processing handles 500k+ events/sec per node with automatic backpressure handling.

🤖

Automated Containment

Playbook-driven response isolates compromised hosts, revokes tokens, and blocks C2 channels within milliseconds of detection.

🔄 Processing Architecture

Data flows through a deterministic, observable pipeline designed for maximum throughput and zero data loss.

Ingestion

Syslog, Kafka, STIX/TAXII, Agent Streams

Normalization

CEF/ECS Mapping, Deduplication

AI Analysis

Model Inference, Correlation Engine

Response

Orchestration, Ticketing, Isolation

📊 Technical Specifications

Ingestion Rate	Up to 1.2M events/sec (clustered) Horizontal Scale
Detection Latency	< 8ms p99 (inference + response)
False Positive Rate	< 0.02% (industry benchmark validated)
Supported Protocols	HTTP/2, gRPC, TCP/UDP, TLS 1.3, MQTT, CoAP
Deployment Models	SaaS, Air-Gapped, Hybrid, Edge-Only
Compliance Frameworks	NIST 800-53, ISO 27001, PCI-DSS, HIPAA, FedRAMP
Storage Retention	Hot: 30 days \| Warm: 1 year \| Cold: 7 years (configurable)

🔌 API & Integrations

The 🎪 Threat Engine exposes a fully documented REST & GraphQL API, along with native webhooks and SDKs for Python, Go, and TypeScript. Integrate with SIEMs, SOAR platforms, or custom orchestration layers.

/v3/threats/query GET
/v3/telemetry/ingest POST
/v3/playbooks/{id}/trigger POST
/v3/entities/{id}/contain PUT
/v3/models/evaluate POST

                    Python SDK Example
                    
                
import cybervault.threat_engine as ce

# Initialize client with API key
client = ce.Client(api_key="cv_live_xxxxx")

# Stream real-time detections
def on_alert(event):
    if event.severity >= "HIGH":
        client.auto_contain(event.entity_id)
        print(f"Blocked: {event.indicator}")

client.stream("threats", callback=on_alert)
# Output: Blocked: 192.168.4.12:4443

❓ Technical FAQ

We use metadata-only analysis for TLS 1.2/1.3 traffic, including JA3/JA3S fingerprinting, SNI parsing, timing analysis, and certificate chain validation. Full decryption requires client-side certificate injection or TLS termination proxies.

Yes. The Threat Engine ships as a fully containerized bundle (Docker/Kubernetes) with offline model weights. Updates are delivered via signed air-gap packages that can be verified and installed without external connectivity.

Lightweight edge agents consume ~150MB RAM and 0.5 CPU core at idle. Full inference nodes require 4 vCPUs and 8GB RAM. We recommend GPU acceleration (NVIDIA T4 or better) for clusters processing >500k EPS.

The engine includes an active feedback loop. Analysts can label alerts in the console, which triggers online fine-tuning. Custom suppression rules, allowlists, and confidence thresholds can be tuned per environment.

Ready to deploy the 🎪 Threat Engine?

Spin up a sandbox environment, access full API credentials, and run a live threat simulation in under 5 minutes.