FINRA compliance for generative AI data controls demands precision on day one. The rules are clear: protect customer data, maintain audit trails, and prove your controls work under stress. AI models cannot be an excuse for data leakage, recordkeeping failure, or opaque decision-making.
Generative AI adds new risk vectors. Model training can expose sensitive information if data pipelines are not ring-fenced. Output generation can create false records without proper logging. Engineers must enforce strict segregation of regulated and non-regulated datasets, apply deterministic retention policies, and implement automated archiving for every AI-assisted interaction.
Traditional FINRA compliance frameworks still apply, but they must be extended. Every AI output that informs a client communication or trade decision needs traceable provenance. Access controls must cover model prompts, parameters, and inference results just as much as raw databases. Encryption at rest and in transit is non-negotiable. Monitor every access event, whether from a human user or a machine process.