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 |
| Auditability | Request, model and response logging for case review |
| Data grounding | Embeddings and RAG are live for policies, KYC and product knowledge |
| Structured output | JSON mode returns case and decision records in validated schemas |
| Models | 30+ models behind one API, routed by task sensitivity |
| 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
- Define the payment boundary: money movement data never enters prompts.
- Scope keys per service and environment, and rotate on a schedule.
- Version prompts and keep evaluation evidence as part of normal releases.
- Use JSON mode so case and decision records fit existing schemas.
- Keep human review on anything that moves, blocks or refunds money.
How it works, step by step
- Classify data: public content, internal procedures, customer identity, transaction and payment data.
- Draw the payment boundary and design tools that return status rather than credentials.
- Choose deployment per data class, using in-region or private environments for regulated flows.
- Issue keys per service and environment, and connect enterprise SSO and RBAC for console access.
- Version prompts in your repository and attach evaluation results to each release.
- Route risk and fraud outputs through structured schemas and human review.
- Log request IDs, model versions, retrieved sources and decisions under your retention policy.
Try it yourself
Open the AI API key security checklist →
Data classes and the payment boundary
Fintech data spans marketing copy, internal runbooks, customer identity records and transaction data. Each class needs a rule. The most important is the payment boundary: card numbers, bank credentials and full transaction payloads never enter a prompt or a log. Expose tools that return the outcome an answer needs - settled, pending, declined - and keep the underlying data in systems that already meet your obligations.
For identity data, minimize fields and prefer references over records. Retrieve what is necessary, not everything available.
Keys, services and fast-moving teams
Small teams share context but should not share credentials. Issue a key per service and environment, store it in a secrets manager, and rotate on a schedule that survives staff turnover. Usage analytics per key gives you both attribution and an anomaly signal. Where the console is used, enterprise SSO and RBAC options keep permissions aligned with roles.
See API keys, RBAC and audit logs for the pattern.
Fraud and risk decision support
AI is strongest at assembling evidence: summarizing a case, retrieving relevant history, and drafting an explanation. It should not make the final block-or-approve call. Return case assessments in JSON mode so your risk engine validates fields and thresholds, and log both the recommendation and the human decision. That separation also makes adverse-action explanations easier to produce.
Change control without slowing delivery
Speed and evidence are compatible if prompts live in version control with evaluation results attached to each release. Maintain a model allow-list with test outcomes, record the model version in logs, and keep a rollback path. When a regulator, auditor or partner asks how a decision was produced, the release record and interaction log answer the question without a scramble.
Honest comparison
| Control area | Plugsky capability | Common gap | Owner |
|---|---|---|---|
| Payment boundary | Tools return status; prompts exclude credentials | Transaction payloads in prompts | Payments engineering |
| Key management | Per-service keys with rotation and analytics | One shared key across services | Security engineering |
| Residency | Region selection plus VPC, on-prem and air-gapped | Unknown processing location | Compliance |
| Decision records | JSON mode schemas plus human review | Free-text risk decisions | Risk operations |
| Change control | Versioned prompts and model allow-list | Prompt edits pushed ad hoc | Engineering |
| Audit trail | Request, source and decision logging | No reconstruction path | Internal audit |
Frequently asked questions
Can AI see transaction data?
Keep full transaction payloads and credentials out of prompts. Use tools that return only the status or category needed, and process regulated flows in an in-region or private deployment.
How do we keep shipping fast with these controls?
Put prompts and evaluation results in your normal release process. Model choice stays configuration, so shipping a feature does not require a new compliance review for every test.
Should AI decide fraud cases?
No. It assembles and summarizes evidence. A human analyst owns the block, approve or refund decision, and the recommendation plus decision are logged.
What is the right deployment for regulated data?
Region selection covers many requirements; VPC, on-prem and air-gapped cover stricter mandates. Document the data path per workflow.
Is fine-tuning available?
Fine-tuning is coming soon. Start with retrieval over current policies and product content, which updates immediately and keeps citations.
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 policies and public documentation with the free plan, then take one customer-facing workflow through the full evidence process before expanding.