Solution area

Financial Services & Data Protection

Financial institutions operate under strict expectations for data protection, model risk and auditability. AI systems and agents need to meet the same standard of evidence as other controlled processes.

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The challenge

What makes AI risk different here

Sensitive financial data

Customer and transaction data must not leak through prompts, outputs or tool calls.

Model and agent risk

Automated actions require explicit limits, approvals and monitoring.

Examination readiness

Teams need to show how AI behavior was controlled, not just describe intended controls.

Our approach

How QNE Labs approaches it

Apply data-protection practice to AI inputs, outputs and integrations
Constrain agent actions with policy and approval points
Capture decision records suitable for internal audit and review

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.