Key facts
| Access control | Scoped API keys with rotation; enterprise SSO and RBAC options |
| Deployment | Cloud with region selection, VPC, on-prem or air-gapped |
| Data residency | Sovereign deployment options for in-country processing |
| Auditability | Request, model and response logging for audit and review |
| Data grounding | Embeddings and RAG are live for policy, product and procedure search |
| Structured output | JSON mode returns review and case records in fixed schemas |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Endpoint roadmap | Audio, images, moderation, batch and fine-tuning are coming soon |
TL;DR
- Classify customer, payment and risk data before wiring any AI workflow.
- Choose residency explicitly: region selection, VPC or air-gapped deployment.
- Separate duties so no single team owns both model config and approval.
- Keep evaluation evidence and change records for every production prompt and model.
- Log interactions so any customer-facing answer can be reconstructed.
How it works, step by step
- Map workflows by risk tier and the data each one touches: customer, payment, credit, internal.
- Select deployment per tier, keeping regulated data in-region private environments.
- Define key ownership and segregation of duties: builder, approver and operator are different roles.
- Approve a model allow-list with documented evaluations, and version prompts like code.
- Add review gates for customer communications and any credit, fraud or risk decision support.
- Log request IDs, model versions, sources and outputs under a defined retention schedule.
- Test incident response: model outage, wrong answer and suspected data exposure.
Try it yourself
Open the AI data residency checklist →
Data classification and residency
Banking data spans public marketing content, internal procedures, customer records and payment data. Each class needs its own processing rules. Payment data is best kept entirely out of model prompts; use tools that return a status or decision instead of raw fields. Customer data should stay in private or in-region deployments with a documented path from request to answer.
Residency is an architecture decision: region selection for cloud plans, and VPC, on-prem or air-gapped deployment where mandates require it. Document the choice per workflow, not per vendor.
Segregation of duties and key ownership
Model credentials should not be shared across teams. Issue keys per application and environment, store them in a secrets manager, and assign ownership to a named team. Separate the roles that build prompts, approve them and operate them, so a change cannot reach production without an independent check.
Enterprise SSO and RBAC options govern console access, keeping permissions aligned with your joiner-mover-leaver process.
Model risk governance and change control
Treat model and prompt changes like production releases. Keep an approved model list with evaluation evidence for each task, record the version used in logs, and re-run evaluations when models change. Where supervisors expect model risk documentation, the evidence set is the same one your engineers need for debugging: test sets, metrics, known limitations and rollback plans.
Monitoring, audit and incident response
Log request IDs, model and version, retrieved sources, output and the following human decision, retained under your policy and readable only by authorized staff. Monitor error, fallback and escalation rates as risk signals, and write the incident playbook before launch: who disables a workflow, what customers are told, and how manual processing resumes. See the procurement checklist for the evidence package.
Honest comparison
| Control area | Plugsky capability | Common gap | Owner |
|---|---|---|---|
| Residency | Region selection plus VPC, on-prem and air-gapped | Unverified processing location | Compliance |
| Identity | Scoped keys, rotation, enterprise SSO and RBAC options | Shared team credentials | Security engineering |
| Change control | Versioned models behind one compatible API | Silent model changes | Model risk committee |
| Audit trail | Request, source and model logging | Outputs not reconstructable | Internal audit |
| Data minimization | Retrieval and tools instead of raw data in prompts | Full records pasted into prompts | Data governance |
| Incident response | Fallback patterns and status visibility | No disable procedure | Operational resilience |
Frequently asked questions
Can customer data be processed by the model?
Only under a deployment and agreement that meets your obligations. Keep raw records behind permissioned tools, minimize fields in prompts, and use in-region or private deployment for regulated data.
How do we evidence model risk management?
Maintain an approved model list, evaluation results per task, versioned prompts, logged model versions and rollback plans. That set supports both internal audit and supervisory questions.
What about payment data?
Keep it out of prompts. Use tools that return only the status or outcome an answer requires, so card and account details never enter the model context.
How should keys be organized?
One key per application and environment, stored centrally, owned by a named team, rotated on schedule, and separated from approval responsibilities.
Is fine-tuning available for bank-specific tasks?
Fine-tuning is coming soon. Start with retrieval over approved policies and glossaries, which updates immediately when content changes and keeps citations intact.
Which endpoints are live today?
Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, assistants, responses and fine-tuning are coming soon.
Where do we start?
Pilot on internal policy documents with the free plan, validate accuracy and logging, then take one customer-facing workflow through the full approval process.