Governed AI.
Production Inference.
A production-grade AI operations layer for endpoint security — with governed inference, provider-aware routing, enterprise entitlement gating, and full audit trail. Self-hosted by default. Every decision is explainable, auditable, and tenant-isolated.
AI Security Engine: Four Pillars
Knowledge reasoning, production edge inference, governed provider routing, and real-time intelligence.
Knowledge Reasoning (RAG)
Retrieval-augmented generation grounded in versioned documentation, threat intelligence, and live operational context.
- Automatic re-indexing from versioned knowledge base
- Self-hosted LLM default (Ollama / Qwen)
- pgvector semantic search with grounding
- Context-grounded — no hallucination-prone open generation
- Stateless — never trains on user input
Production Edge ML
ONNX inference on QuickSecure endpoint agents. Detects threats, classifies behaviors, and executes autonomous decisions at the edge.
- ONNX Runtime on endpoint agents (<15ms latency)
- Telemetry → labeling → retraining pipeline
- Canary + Stable model promotion system
- Drift detection (PSI) and automatic rollback
- Three-stage fallback: ONNX → Random Forest → heuristics
Provider-Aware Inference Routing
Enterprise customers choose their AI inference path — self-hosted for privacy, premium providers for enhanced reasoning. Per-tenant, policy-controlled, fully governed.
- Self-hosted default (Qwen/Mistral via Ollama) — zero egress
- Premium providers (Anthropic Claude) — enterprise opt-in
- Per-tenant provider policies with entitlement gating
- Automatic fallback to self-hosted if premium unavailable
- Provider health monitoring and SLA tracking
AI Governance & Audit
Every AI interaction is logged, evaluated, and auditable. Built-in evaluation framework, provider comparison, and usage metering.
- Tamper-evident audit log per AI interaction
- Built-in evaluation framework with quality scoring
- Provider-aware comparison runs (local vs premium)
- Usage metering and cost visibility per tenant
- User feedback collection and satisfaction tracking
Live Intel. Zero Training on Customer Data.
How the two layers inform each other without compromising data boundaries.
/api/ai/intel/feed) from the production ML pipeline — aggregated metrics, model status, and anonymized signals. Updated every 3 minutes.
Docs → Knowledge Index
Static website content, product documentation, and public guides are automatically re-indexed into the engine's vector store on every content version change.
Live Intelligence Feed
Real-time, aggregated intelligence summary — active model version, drift status, detection metrics, containment rates, and fallback distribution.
Data Boundary Enforcement
Customer endpoint telemetry remains in the security pipeline. Intelligence feed contains only aggregated, anonymized summaries — no customer-identifiable fields.
Feedback Loop Architecture
Detection accuracy metrics and threat landscape summaries flow back to documentation and knowledge bases — without exposing operational data.
Production-Grade AI, Not Prototypes.
All three AI layers run in production on Corxor infrastructure. The knowledge reasoning layer serves real users with grounded, source-cited responses. The Edge ML system processes live telemetry and makes autonomous security decisions. Every component is monitored, governed, and auditable.
Self-Hosted Default
Qwen/Mistral via Ollama on our own infrastructure. No third-party API calls unless enterprise customers opt in. Full data sovereignty by default.
Enterprise Provider Choice
Optionally enable premium providers (Anthropic Claude) for enhanced reasoning. Per-tenant routing, automatic fallback, and full audit trail.
Governed ML Pipeline
Model registry, drift detection, canary deployment, signed model versions, and immutable audit logs across all providers.
Explainable & Auditable
Every AI judgment carries recorded provider, model version, token count, quality signals, and tenant context. Built-in evaluation framework for quality comparison.
Operational Systems. Measurable Results.
Six live AI surfaces delivering value in production today.
Incident AI Explanation
Root cause analysis, MITRE ATT&CK correlation, severity assessment, and remediation guidance — generated from structured incident data with provider-aware routing and full audit logging.
IOC Assessment
Threat intelligence correlation, confidence scoring, and contextual analysis for Indicators of Compromise — integrated directly into the investigation workflow with tenant-specific grounding.
Workspace AI Assistant
Security posture analysis, prioritized recommendations, and operational guidance — grounded in your tenant's own endpoint fleet data, threat history, and policy context.
Edge ML Detection
ONNX models on QuickSecure agents classify behaviors in under 15ms. Autonomous quarantine with three-stage fallback, drift monitoring, and canary validation.
AI Governance Dashboard
Provider operations, evaluation comparisons, usage analytics, health monitoring, and SLA tracking — all managed from a unified admin console with per-tenant visibility.
AI API Access
Programmatic access to the AI Security Engine via managed API keys with per-key usage metering, rate limiting, and tenant-scoped analytics.
AI Security Engine — Try It Now
The public demo runs on our self-hosted AI infrastructure. In production, the AI Security Engine operates with governed provider routing, tenant-aware grounding, and full audit logging. Click the icon in the bottom-right corner.
Self-Hosted Default
Runs on our infrastructure with Ollama by default. Enterprise customers can opt into premium providers. Automatic fallback ensures zero disruption.
Governed & Audited
Every AI response is audit-logged with provider, model, token count, and tenant context. Built-in evaluation framework for quality assessment.
Tenant-Isolated
AI grounding is scoped to your tenant's data. Provider policies are per-tenant. No cross-tenant data leakage in AI context.
AI That Learns From Every Endpoint in the Network
QuickSecure's AI engine doesn't just detect — it evolves. Every endpoint contributes to a collective intelligence network, strengthening protection across the entire fleet. Self-hosted inference, security-first architecture, and full decision auditability.
- ONNX edge inference with sub-15ms detection latency
- Automatic model drift detection and canary deployments
- Collective defense — one detection protects all endpoints
- Explainable AI — every decision is auditable and overridable
- RAG-powered knowledge engine for documentation and support
Autonomous. Explainable. Evolving.
Three-layer AI architecture engineered for production-grade endpoint security at scale.
Security Guarantee
Designed for operational environments. Engineered for trust.
Approved Sources Only
Reads only from allowlisted knowledge sources. No external internet egress from the inference container.
Never Trains on User Input
Stateless by design. User messages are processed and discarded. No prompt data enters training pipelines.
No Customer Data Exposure
Intelligence feed is aggregated and anonymized. No file hashes, feature vectors, command lines, or endpoint identifiers.
Prompt Injection Defense
Context sanitization, DLP response filtering, and strict refusal policies for secret access or system prompt extraction.
Ready to Integrate AI Into Your Security?
Whether you need a knowledge-connected reasoning engine, an edge ML pipeline, or a live intelligence feed — let's discuss how our AI Security Engine can address your specific requirements.
Self-hosted default · Enterprise provider choice · Full audit trail