Solution area

Enterprise AI Security

Enterprises are moving AI from pilots into workflows that touch customers, data and systems of record. Security programs need the same visibility and control over AI behavior that they expect for any other production system.

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The challenge

What makes AI risk different here

Unseen runtime behavior

Traditional controls see network traffic and identities, not what an AI system was instructed to do or why it acted.

Agent permissions

Agents often inherit broad credentials, so a single manipulated instruction can reach far more than intended.

Evidence gaps

When something goes wrong, teams lack a reliable record of the inputs, decisions and actions involved.

Our approach

How QNE Labs approaches it

Assess AI systems, agents and data flows to locate runtime risk
Define security policies that reflect how AI is actually used
Design runtime inspection, decision recording and evidence capture
Validate controls before relying on them for enforcement

This is a solution area QNE Labs is designing for. It describes our focus and approach, not existing customer deployments.

Working Prototype — Validation in Progress

Kavrent / ARIA

AI Runtime Security & Governance

Designed to detect suspicious AI interactions, evaluate security policies, support enforcement and preserve auditable security evidence.

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Working Prototype — Integration Testing

Connector as a Service

Secure Enterprise AI Integrations

Governed connections between AI systems and the enterprise tools and data they rely on.