AI Governance in the Back Office of Banks and Broker-Dealers
Financial firms are adopting AI fastest inside compliance, risk, finance, and HR, where the records and controls are already regulated. This is how to put that internal AI use under governance before an examiner asks for the evidence.
The exposure sits in the back office
A compliance analyst feeds a year of supervisory review logs into a large language model and asks for a summary. Down the hall an accountant is drafting the quarter's control narrative the same way, and over in HR a performance file goes into a public chatbot to come back as a cleaner write-up. None of this reaches a customer, so it rarely draws a second look from anyone inside the firm. But compliance, risk, internal audit, finance, legal, and HR are the functions examiners go through line by line.
Each of those moments touches a record that an examiner from the SEC, FINRA, the OCC, or the Federal Reserve can demand. Putting a model in the loop does not relieve the firm of responsibility for the work product it helped create.
A model-drafted SAR narrative falls under 17a-4
Broker-dealers live under SEC Rule 17a-4, which requires covered records to be preserved on non-rewriteable, non-erasable, time-stamped storage and electronic communications to be supervised. A model that drafts a SAR narrative, a suitability memo, or a KYC summary is generating exactly that kind of record. When the prompt, the output, and the approval are scattered across a dozen chat sessions and personal accounts, the firm cannot produce the time-ordered file the rule expects.
When the model output becomes the record but the prompt that produced it was never captured, the examiner has no audit trail to inspect, and writes up the gap.
If the output drives a decision, it is a model
The Federal Reserve's SR 11-7 guidance on model risk management predates generative AI, and it still applies cleanly. SR 11-7 expects that anything driving a decision be soundly developed, then validated by someone other than the developer, with ongoing monitoring under a named owner. A general-purpose model that an analyst leans on to triage alerts or score a counterparty is doing model work, whether or not anyone filed it as a model. Once the output shapes outcomes, the firm owes validation, monitoring, and an accountable owner for it.
Internal controls raise a parallel set of obligations. Under Sarbanes-Oxley Section 404, management must document and have audited the internal control over financial reporting. Once AI touches the close, the reconciliations, or the control evidence itself, the firm has to show who reviewed that output and on what basis. An auditor who finds SOX control evidence generated by a model, with no sign of a human checking it, treats that control as untested.
Some internal uses are high-risk by law
Scope matters, because internal AI use does not all carry the same regulatory load. The EU AI Act, Annex III classifies AI used to evaluate creditworthiness and AI used in hiring and worker management as high-risk. For a global bank, that puts the credit risk team and HR under obligations that lighter-touch functions never trigger. A governance program has to find where the law draws that line and apply tighter controls where it matters most.
How LogicNerve keeps the evidence intact
We built LogicNerve around a specific failure mode. Someone in compliance or finance does something reasonable with a model, and weeks later no record of the prompt, the data it touched, or the sign-off exists anywhere the firm controls. A policy that only works when employees remember it under deadline pressure is the kind of gap SR 11-7 and Rule 17a-4 turn into findings. With LogicNerve, requests route to an approved, department-owned agent, and sensitive data is caught at the boundary before it leaves in a copy-paste. Every action stays policy-controlled, and the prompt, the output, the data touched, and the approval are captured together as a record the supervisory and audit teams can pull on demand.
That captured record is what lets an examiner sign off on an AI program instead of writing it up. Get in touch to see how LogicNerve can help your organization adopt AI responsibly.
Sources
SEC Rule 17a-4: Records to be preserved by brokers and dealers (eCFR)
Federal Reserve SR 11-7: Guidance on Model Risk Management
SEC: Management Report on Internal Control Over Financial Reporting (Sarbanes-Oxley Section 404)
EU AI Act, Annex III: High-Risk AI Systems (creditworthiness, employment)