Cloud-Native Endpoint Security

QuickSecure
Autonomous Endpoint Detection & Containment

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<15ms
Edge Inference
150+
Forensic Signals
5
Platforms
Zero
Kernel Drivers
3-Stage
ML Fallback
Desktop Agent

AI-Native Protection Running on Your Endpoint

QuickSecure runs as a lightweight native application on every endpoint. Real-time threat monitoring, ML-powered detection, network defense, dependency auditing, and email security — all managed from a single dashboard with autonomous containment.

QuickSecure v8.0.0 — Windows
QuickSecure Desktop Agent — AI-Native Endpoint Security Dashboard
AI-Powered Detection
Network Monitor
Email Security
Autonomous Actions
Platform Overview

Self-Learning EDR. Cloud-Managed. Zero Kernel Risk.

A lightweight agent monitors behavioral telemetry across desktop and mobile platforms. Versioned ONNX models run directly on the endpoint — producing risk scores, confidence levels, and explainable breakdowns. Containment is confidence-gated and policy-controlled.

Edge-First Inference

ONNX models run locally on the endpoint. Sub-15ms detection latency. No cloud round-trip for containment decisions. Three-stage fallback ensures zero blind spots.

Zero Kernel Drivers

User-mode ETW and eBPF provides deep process, network, and registry visibility without BSOD risk or system instability. No kernel attack surface.

Cloud-Managed SaaS

Same agent, same ML engine, same console for every customer. Adding a new tenant is a database record — not an infrastructure project.

Explainable AI

Every decision carries a composite risk score, per-feature contribution breakdown, model version, and confidence level. No black-box verdicts.

Multi-Platform

Native agents for Windows, Linux, macOS, Android, and iOS. Consistent detection logic across all platforms. Under 80MB memory, under 2% CPU.

Collective Defense

When one endpoint detects a new threat, the entire fleet is updated within minutes. Every endpoint strengthens protection for everyone.

Core principle: Protecting 10 endpoints and protecting 10,000 endpoints use the same product foundation. Every model update is canary-validated. Every deployment is governed. The platform improves continuously.

AI Security Engine

Centralized Intelligence, Distributed Enforcement

QuickSecure is not just an agent that runs on endpoints. At its center sits a cloud-native AI security engine that continuously learns from fleet-wide telemetry, threat intelligence feeds, and labeled decision outcomes. Every endpoint contributes behavioral data. The central engine aggregates, correlates, and produces actionable intelligence that flows back to every agent in the fleet.

This creates a collective defense network: when one endpoint encounters a new threat pattern, the central engine evaluates it, and within minutes the entire fleet is updated with new Indicators of Compromise. The more endpoints participate, the stronger the detection capability becomes for everyone.

Zero Kernel Attack Surface

User-mode ETW and eBPF provides deep process, network, and registry visibility without BSOD risk or system instability. No kernel attack surface.

Versioned Model Registry

Every ONNX model is versioned, signed, and tracked. Full lineage from training data to production deployment. Rollback to any previous version in seconds.

Explainable Decision Logic

Composite risk score with per-feature contribution breakdown. Model version, policy threshold, and confidence level recorded per decision. No black-box verdicts.

Drift Monitoring (PSI)

Population Stability Index tracks distribution shift between training and production features. Automatic retraining triggers when PSI exceeds configurable thresholds.

Canary Deployment

New model versions are validated on a subset of endpoints before fleet-wide promotion. Canary traffic percentage and rollback criteria are policy-defined.

Three-Stage Fallback

ONNX edge model → Random Forest → rule-based heuristics. If the primary model fails or confidence is insufficient, fallback stages engage automatically.

Embedded AI Intelligence — Not a Chatbot

The AI Security Engine is not a conversational assistant bolted onto a dashboard. It is embedded directly into security workflows — incident triage, IOC investigation, posture assessment, and operational guidance. Every AI output is grounded in your actual telemetry, structured for analyst consumption, and recorded in a tamper-evident audit log.

AI Incident Explanation

Root cause analysis, MITRE ATT&CK correlation, severity assessment, and remediation guidance — generated from structured incident data, not free-form prompts.

AI IOC Assessment

Threat intelligence correlation, confidence scoring, and contextual analysis for Indicators of Compromise — integrated directly into the IOC database workflow.

Workspace AI Assistant

Security posture analysis, threat summaries, and prioritized recommendations for your tenant — grounded in your own endpoint fleet data and threat history.

Grounded in real telemetry
Every response audit-logged
Private inference — no data leaves your tenant
Stable / Canary model governance
Role + tier entitlement enforcement
End-to-End Architecture

How It Works — From Agent to Control Plane

QuickSecure operates across six coordinated layers. Each layer has defined responsibilities, clear boundaries, and independent failure domains.

Agent
Edge Inference
Detection
Confidence Gating
Containment
Policy-Controlled
Control Plane
Tenant Isolation
SIEM Routing
Outbox Pattern
AI Engine
Collective Intel

Endpoint Agent

Collects behavioral telemetry across 150+ forensic checkpoints on desktop and mobile. ONNX inference runs locally. Decisions evaluated against policy thresholds and operating mode constraints.

Detection Pipeline

Three-stage fallback: ONNX → Random Forest → heuristics. Every decision produces composite risk score, model confidence, and explainable feature contributions. Containment is never blind.

Control Plane & SIEM

Governs tenant isolation, policy assignment, and event routing. CentralOnly, DirectOnly, or Hybrid modes. Webhook, Syslog (CEF), and Microsoft Sentinel with transactional outbox delivery.

Central AI Engine

Aggregates fleet-wide intelligence. Model governance — registry, signing, canary deployment, drift monitoring (PSI), rollback. Distributes IoCs for collective defense.

Cloud-Native SaaS Scalability

Multi-Tenant by Design, Not by Retrofit

The default deployment model is cloud-managed SaaS. The architecture uses strict multi-tenant data partitioning, horizontal scaling of control plane components, centralized model governance, and shared infrastructure with isolated tenant contexts. There is zero per-customer code divergence.

🔒 Tenant Isolation

Each tenant gets dedicated data partitions, per-tenant ML model governance, per-tenant SIEM routing, and isolated policy contexts — all on shared infrastructure.

📈 Horizontal Scaling

Control plane components scale independently. Adding customers scales linearly — no re-architecture, no dedicated infrastructure per tenant unless explicitly requested.

🚫 Zero Custom Code

No per-customer forks, branches, or custom builds. Configuration-driven differentiation only. Consistent quality, faster updates, and lower operational cost.

Why this matters: Many security vendors position themselves as "cloud-native" while requiring per-customer deployment engineering. QuickSecure's tenant onboarding is a database record and a policy assignment — not an infrastructure project.

Detection & Operating Modes

Progressive Trust — Earned, Not Assumed

The agent monitors over 150 forensic checkpoints covering persistence analysis (WMI, COM hijacking, registry, scheduled tasks, systemd/cron), behavioral detection (process hollowing, LSASS access, credential dumping, LOLBins, fileless malware), network intelligence (C2 beacons, DNS tunneling, AbuseIPDB/URLHaus/MalwareBazaar integration), and supply chain defense (git scanning, CI/CD integrity, typosquatting, secret exposure).

Organizations progress through three operating modes as confidence in detection accuracy grows:

Shadow

Observe Only

Full inference pipeline runs, zero containment actions taken. Compares "would-contain" vs "actually-contain" to validate model accuracy before enabling autonomous behavior.

Observe
Supervised

Human-in-the-Loop

Detections generate recommended actions. An admin reviews, approves, or dismisses each one. Every decision enriches the TP/FP labeling system for model retraining.

Verify
Full Autonomous

Confidence-Gated

When confidence exceeds policy threshold and risk criteria are met, containment executes automatically. Every action is logged, reversible, and feeds back into the learning loop.

Autonomous
Platform Resilience

Built to Survive Hostile Environments

An endpoint security product that crashes under load, loses events during outages, or allows tampering of its decision logs is worse than no product at all.

Self-Healing

Automatic recovery under degradation. The agent restores state without manual intervention when services are lost.

Backpressure

Adaptive circuit breakers prevent telemetry overload from freezing containment decisions.

Tamper-Evident

Cryptographic integrity on every event, decision, and config change. Unauthorized modifications are flagged.

ML Integrity

Model poisoning protection via signatures, drift monitoring, and canary validation.

No Vendor Lock-in

Deployable on-premise, hybrid, or multi-cloud. Architecturally independent from any single provider.

Deployment & Service Model

SaaS Default — Sovereign Optional

The primary deployment model is cloud-managed SaaS — fastest path to protection, first to receive updates. For regulated or sovereign environments, QuickSecure also operates in dedicated single-tenant infrastructure, on-premise data centers, sovereign cloud environments, and hybrid configurations.

The product core does not change across deployment models. The detection engine, inference pipeline, ML governance, and containment logic remain identical. Infrastructure ownership and data residency change. The security product does not.

Product vs. Service Layer

QuickSecure is the product. It includes its own SOC console — incident review, risk scoring, model confidence visualization, audit trails, policy management, and fleet intelligence — without external tooling.

Corxor MSSP is an optional operational layer. Customers may run QuickSecure independently, integrate with their internal SOC, engage Corxor as MSSP, or use it via third-party MSSP partners through multi-tenant white-label support. The platform architecture is independent from the service model.

Pricing

Transparent Per-Endpoint Pricing

Same product at every tier. Capability level and support SLA differ.

INSTANT ACTIVATION
Personal
$12.99 / mo
Up to 3 devices · Monthly · Cancel anytime
  • Shadow + Supervised modes
  • Basic SOC console view
  • Explainable AI scoring
  • Collective IoC sync
  • 90-day event retention
  • Email support (48h SLA)
  • AI Security Assistant
Enter License Key
Business
$8 / endpoint / mo
Annual commitment · Min. 25 endpoints
  • Shadow + Supervised modes
  • Built-in SOC console
  • Explainable AI scoring
  • Collective IoC sync
  • 90-day event retention
  • Email support (24h SLA)
  • JSON export + Webhooks
  • AI Incident Explanation
  • AI IOC Assessment
Get Started
Enterprise / MSSP
Custom
Tailored to your organization
  • Everything in Advanced
  • Enterprise tenant isolation
  • Tenant-dedicated ML models
  • Multi-tenant SOC dashboard
  • Direct SIEM export (Sentinel, Splunk, Syslog)
  • Per-tenant rate limiting
  • On-premise deployment option
  • Custom retention & dedicated support
  • AI Governance & Audit
  • AI API Access
  • Premium Provider Choice
Contact Sales

Volume discounts available for 100+ endpoints. First-year pricing guaranteed for annual commitments.

AI Security Engine

Try the AI Security Engine

Ask a security question to see the same AI engine that powers incident explanation, IOC assessment, and workspace intelligence inside QuickSecure — running live right now.

AI Security Engine LIVE DEMO
No data stored

🟢 Live demo — real AI inference against our self-hosted model with threat intelligence grounding. Click any example or type your own question.

Self-hosted AI inference — no data sent to third parties. Rate-limited public demo.

Unlock full AI
Self-Hosted Default
Provider-Aware Routing
Full Audit Trail
Governed Inference
AI Governance & Provider Choice

Your AI, Your Rules

QuickSecure's AI Security Engine is governed, audited, and tenant-aware. You control the inference path — self-hosted for maximum privacy, or premium providers for enhanced reasoning. No lock-in.

Self-Hosted Default

All AI inference runs on self-hosted infrastructure by default. No data leaves your environment. Zero third-party API calls. Full data sovereignty from day one.

Premium Provider Option

Enterprise customers can optionally enable premium AI providers for enhanced reasoning quality. Provider routing is per-tenant, policy-controlled, and fully audited.

Governed & Auditable

Every AI interaction — regardless of provider — is logged in a tamper-evident audit trail. Model selection, token usage, response quality, and provider fallback events are all recorded.

Privacy-First Path

Self-hosted Qwen/Mistral models via Ollama. No external API calls. Ideal for regulated industries, sovereign environments, and maximum data privacy.

Premium Quality Path

Enterprise opt-in to premium providers (Anthropic Claude, etc.) for complex incident analysis and advanced reasoning. Routed per-tenant with automatic fallback to self-hosted if unavailable.

Per-tenant provider policies
Automatic fallback to self-hosted
Full audit log per request
Usage metering & cost visibility
Provider health monitoring
Enterprise entitlement gating
Technical FAQ

Questions from CTOs, Architects & SOC Leaders

Does the agent require kernel drivers?
No. QuickSecure operates entirely in user-mode using ETW (Windows) and eBPF (Linux). No kernel drivers installed, eliminating BSOD risk. The detection model compensates for narrower visibility using behavioral correlation across 150+ data points.
What happens with false positives in autonomous mode?
Every autonomous containment action is reversible. Full context is logged — model version, confidence score, feature contributions, policy threshold. The false positive is labeled and fed back into the retraining pipeline. Confidence thresholds can be adjusted per policy.
How is tenant data isolated?
Tenant isolation is enforced at the database level using global query filters. SIEM routing uses per-tenant queues with independent rate limiting and circuit breakers. ML governance supports per-tenant model versions. No shared state between tenants at the application level.
What if the ONNX model fails to load?
Three-stage fallback: ONNX → Random Forest → rule-based heuristics. The final stage is deterministic and requires no ML runtime. The fallback stage used is recorded in every event. The control plane is notified of model failures for operational alerting.
How does drift monitoring work?
Population Stability Index (PSI) compares production feature distributions against training data. When PSI exceeds the threshold (default 0.2), the system triggers retraining, flags for review, or rolls back — depending on policy. Full feature statistics are stored for post-hoc analysis.
Can we integrate with our existing SIEM?
Yes. CentralOnly, DirectOnly, or Hybrid routing. Webhook (JSON), Syslog (CEF), and native Microsoft Sentinel integration supported. Transactional outbox pattern with retry logic and dead-letter queues for guaranteed delivery.
Is the product the same across SaaS and on-prem?
Yes. Detection engine, inference pipeline, ML governance, containment logic, and SOC console are identical. On-premise deployments receive the same model updates (signed artifacts), same agent versions, same policy engine. No on-prem-specific code branches.
What is the agent's resource footprint?
Typical memory under 80MB, CPU under 2% during normal operation on desktop platforms (Windows, Linux, macOS). On mobile (Android, iOS) the agent is optimized for battery efficiency with adaptive scan scheduling. ONNX runs on CPU, no GPU required. No kernel drivers, no high-I/O background services. Telemetry is batched and transmitted at configurable intervals.
How does the collective defense network work?
When any endpoint in the fleet encounters a new threat pattern, the central AI engine evaluates it, generates Indicators of Compromise, and distributes them across all tenants. Threat intelligence is aggregated, anonymized, and shared — every endpoint benefits from the fleet's collective experience.
Can MSSP partners white-label QuickSecure?
Yes. Enterprise/MSSP tier supports multi-tenant management. MSSP partners onboard clients as sub-tenants with isolated data, independent policies, and separate SIEM routing. Same underlying product — no custom builds per partner.
Can I choose which AI model/provider is used?
Yes. By default, all AI inference runs on self-hosted models (Qwen/Mistral via Ollama) — no data leaves your environment. Enterprise customers can optionally enable premium providers like Anthropic Claude for enhanced reasoning quality. Provider selection is per-tenant and policy-controlled. If a premium provider is unavailable, the system automatically falls back to self-hosted — zero disruption.
How is AI usage audited and governed?
Every AI interaction is recorded in a tamper-evident audit log including: provider used, model version, token consumption, response quality signals, and tenant context. Admins can review AI decisions, evaluate quality through built-in comparison frameworks, and manage provider routing through a governance dashboard.

Get Started

Start with a 14-day free Business trial — up to 10 endpoints, no credit card required.

Business / Enterprise: 14-day free trial — dedicated onboarding included. Personal: Instant activation — up to 3 devices, $12.99/mo.
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