Protected data
Classification, Presidio DLP and tokenization before any model call; local models for restricted data.
Solutions
Banking AI, under the control of risk and compliance
Financial institutions want assistants and agents that help advisers, analysts and compliance teams, without exposing customer data or losing control over decisions.
At a glance
- Protected data
- Model registry and evaluation
- Audit evidence
- Service continuity
Your challenges
What AI must respect in your industry
01 - Customer data and banking secrecy
Customer data must not leave the authorized perimeter, nor appear in a prompt sent to an external provider.
02 - Model risk
Every model in use must be inventoried, evaluated and monitored, with an identified owner.
03 - Operational resilience
Resilience requirements, such as those of the DORA regulation, also apply to AI services and their providers.
Our answer
What Navin brings
Model registry and evaluation
Model and provider registries, evaluation with Ragas and Promptfoo, red teaming with garak.Audit evidence
Hash-chained audit and WORM storage: every answer links back to its request, its data and its policy.Service continuity
Provider failover, high availability per service and disaster recovery with Velero.
Use cases
Concrete use cases
Examples of use cases your teams can build on the platform.
01 - Adviser assistant
Sourced answers on products, procedures and internal regulations.
02 - KYC document analysis
Extraction and checking of supporting documents, with human validation.
03 - Complaint handling
Qualification, routing and draft answers, under supervision.
04 - Support for IT teams
Help with code and operations, with secrets that never leave the vault.
Other industries
Let's talk about your use cases
An architect studies your constraints and the right topology with you.