AI controls an examiner can follow.
Banks, lenders, broker-dealers, and asset managers need more than a usage policy for AI. They need to show which models touched which data, who approved them, and how information barriers held. Data.co Control enforces those rules in your environment and records the evidence.
Three expectations that collide with hosted AI.
Financial regulators expect firms to protect customer information, maintain information barriers between business lines, and manage models as risk-bearing assets with inventories, validation, and ongoing monitoring. A general-purpose chatbot connected to shared drives meets none of these on its own.
Data.co Control treats each model endpoint as a registered asset with an owner and approval status, applies classification labels to customer and account data at ingest, and evaluates every request against barrier rules before retrieval. The resulting audit log is designed to support examination and internal audit requests without reconstructing events from scattered systems.
Where firms are putting AI to work.
Credit memo drafting
An analyst assistant drafts memos from financial statements and internal ratings for the borrowers in the analyst's portfolio only.
Policy and procedure Q&A
Front-line staff query compliance manuals and product terms, with answers cited to the controlling document version.
Complaint triage
An agent classifies incoming complaints and routes them, reading customer PII only for the case it is working.
Research barrier enforcement
Research and banking identities sit on opposite sides of a barrier. Retrieval across it is denied and logged.
KYC file review
Agents check onboarding files for missing documents and flag gaps for a human reviewer, without export rights.
Reconciliation support
Operations agents explain breaks across ledgers they are scoped to, with destructive actions requiring approval.
What examiners ask, and where the answer lives.
| Question | Where Data.co records it |
|---|---|
| Which models are in use, and who approved them? | Model catalog with owner, version, and approval status |
| What data can each model or agent reach? | Policy set in Git, with history and tests |
| Did any request cross an information barrier? | Audit log entries with decision and matching policy ID |
| Who approved a high-risk action? | Approval records with approver identity and timestamp |
| Did customer data leave the firm? | Model endpoint routing and egress decisions in the log |
Fits the environments financial firms run.
- Private cloud tenancy with no public model endpoints
- On-prem GPU servers for latency-sensitive or restricted workloads
- Separate deployments per legal entity or region
- Integration with existing identity providers and groups
- Audit export to your SIEM and records retention systems
- Change management through pull requests and CI
AI output can be a business record too.
Privacy rules for customer financial information, such as the GLBA safeguards requirements, are only part of the picture. Broker-dealers and advisers also have books-and-records and supervision obligations, and an AI-drafted client communication or research note may fall within them. Firms need to decide which AI interactions are records, where those records are retained, and how supervisors review them.
Data.co does not decide that for you, but it makes the decision enforceable. Policies can require a purpose and a client or account reference on certain requests, route drafts of client-facing text to a supervisory approver, and deny use of models that have not been approved for that purpose. The audit log is content-free by design, so firms that must retain the text itself keep it in their existing archive, linked to the audit record by request ID.
An information barrier in eight lines.
Identities and datasets carry a side attribute. The deny wins over any allow, regardless of who wrote the allow. This is an illustrative starting point for your compliance team to adapt.
id: pol_fin_wall_001
description: Private-side data is never retrieved for public-side identities
subject: { attributes: { side: public } }
resource: { classification: [MNPI] }
action: [read, search, export]
effect: deny
explain: "Information barrier: public-side identities cannot access private-side material."Illustrative example · final API surface documented in /docs
Questions buyers ask
Does Data.co replace our model risk management program?
No. It supplies inventory, approval, and usage records that a model risk program needs, and enforces which data an approved model may receive. Validation and governance decisions stay with your team.
How are information barriers enforced?
Users, agents, and datasets carry barrier tags. Policies deny retrieval and tool calls that would cross a barrier, and every attempt is logged with the policy that blocked it.
Can we use hosted frontier models for some tasks?
Policies can allow specific non-sensitive data classes to reach an approved external endpoint while keeping customer and account data on local models. That routing decision is made and logged per request.
Where are audit records stored?
In an append-only log inside your environment. You can export it to your SIEM or archive under your own retention rules.
Does the audit log capture the prompts and responses?
No. It records who asked, what data and model were involved, the decision, and the policy IDs, but not content. Firms that must retain AI communications keep the text in their existing archive and link it by request ID.
How do we roll this out across business lines?
Start inside one business line with a low-risk workflow such as procedures Q&A, then add barrier rules before connecting a second line. Barriers are easier to prove when they exist before the data does.
See it against your own use case.
A 30-minute technical walkthrough, policy simulation on your scenarios, and a deployment recommendation for on-prem, private cloud, or hybrid.