Runtime over assumptions
Controls should operate where AI behavior actually happens, not only in design documents.
About QNE Labs
Helping organizations build AI they can trust.
QNE Labs is the innovation and technology brand of Quantum Neuro Edge LLC. We develop AI security, governance and intelligent automation technologies grounded in enterprise security practice.
Mission
Intelligence with Accountability.
AI is becoming an actor inside the enterprise — reading data, making recommendations and taking actions. Our mission is to make that intelligence accountable: observable, governed by policy and backed by evidence.
What we focus on
- Enterprise AI Security
- AI Runtime Protection
- AI Governance and Accountability
- Secure Agentic AI
- Intelligent Enterprise Automation
- Applied AI Research
- Secure Enterprise Integrations
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Founder
Eric Gant
Founder, Quantum Neuro Edge LLC
Eric Gant brings more than ten years of Microsoft data protection and enterprise security experience to QNE Labs. His work spans applied AI development, AI governance and security engineering — the disciplines that shape how QNE Labs approaches runtime security for AI systems.
He founded Quantum Neuro Edge LLC to help organizations build AI they can trust: systems whose behavior can be observed, governed and accounted for.
- 10+ years · Microsoft data protection & enterprise security
- Applied AI development
- AI governance & security engineering
Enterprise security background
Built on data protection practice.
Enterprise security teams already know how to classify data, govern access, monitor activity and keep evidence. QNE Labs applies that discipline to AI — treating models and agents as systems that must meet the same standard as any other part of the enterprise.
That background shapes our priorities: least privilege for agents, protection for sensitive data wherever it flows, explicit policy, and records that stand up to review.
Technical philosophy
How we build
Explicit policy
Security intent should be written as policy that can be evaluated consistently and explained.
Evidence by default
Every meaningful decision should leave a record that supports investigation and audit.
Validate before claiming
Capabilities are described by what has been demonstrated, and enforcement is relied on only where validated.
Responsible AI principles
Principles we hold ourselves to
- Human accountability: people remain responsible for consequential decisions, and our designs keep them in the loop.
- Privacy and data minimization: collect and expose only what a task requires.
- Transparency: be clear about what our technology does, what it does not do, and how mature it is.
- Security by design: treat AI systems as privileged actors from the first prototype.
- Honest claims: no unverified performance figures, certifications or deployment claims.
Ready to Build AI You Can Trust?
Tell us about your AI systems and the risks you’re working to control.