Security and governance

Control for critical AI workflows.

DataNXT is built for finance teams that need governed access, clear data boundaries, traceable results, and reviewable workflow steps.

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Security approach

Designed for controlled finance workflows.

Data

Data boundary options

Support for controlled deployment models and data residency requirements, including environments where sensitive information stays inside customer-managed infrastructure.

Access

Access governance

Role-based access patterns for teams that need separation between users, workflows, projects, and source collections.

Evidence

Auditability

Workflow steps, source usage, user actions, and generated results are designed to be reviewable and attributable.

Governance model

Human review stays part of the control layer.

Review points
Set where analysts check assumptions, sources, and intermediate results.
Source-level view
Generated analysis stays linked to the materials it was produced from.
Information barriers
Designed for separating sensitive workflows, teams, and data areas.
Model governance
Support for controlled AI usage patterns and institution-specific model preferences.
Deployment and operations

Adaptable to institutional security requirements.

Further reading

The details.

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Security measures

Encryption, hosting location, network and access controls, and incident response in detail.

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Compliance roadmap

Which standards we are working towards (GDPR, SOC 2, ISO 27001, 27017, and 27018) and in what order.

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Privacy policy

What personal data this website processes, on what legal basis, and for how long.

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Discuss your security requirements.

Send us your requirements checklist. We will go through it point by point and tell you plainly what we meet today and what we do not.

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