The debate about whether AI belongs in financial services is over: a 2026 Cambridge University study found 81% are adopting it at some level. Now, AI is embedded in research workflows, client communications, trade analysis, and daily decision-making in the sector.
What remains unsettled, and increasingly urgent, however, is whether firms can govern it responsibly. Many financial organizations may default to acceptable use policies and employee training as a way to manage AI governance, but that only tells employees what they should do.
But as Eric Wiggins, Product Marketing Director at Smarsh, explains:
"Policy alone does not create evidence."
When an AI interaction influences a business decision or shapes a client communication, it is a business record, and both the SEC and FINRA demand documentation regardless of the technology involved. For firms still relying on attestations and access logs, that gap is where the exposure begins, and the consequences are becoming evident.
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Where the Gaps Start to Cost You
The moment an AI interaction becomes a business record is not always obvious, and that ambiguity is itself a risk. Under SEC and FINRA guidance, the threshold is crossed when an interaction relates to regulated activity, like researching a trade, drafting investment commentary, analyzing a portfolio, or influencing a recommendation. Critically, the record is not limited to what the end product was.
Prompts matter. So do the files shared, the responses generated, the edits made, and the metadata that explains how a decision was constructed. What appears as a final email correspondence to a client is the visible end of a much longer interaction chain that spans multiple AI conversations and even platforms, and regulators may want to see the whole chain.
Without capturing this chain of interactions, three consequences can follow. The first is defensibility: the inability to explain how AI influenced a decision or shaped a communication. The second is supervisory exposure. "If the firm does not capture AI interactions in a complete, retrievable format, supervisors may not be able to identify risky usage or assess whether outputs were appropriate," says Wiggins. That matters not just for individual incidents, but for demonstrating that an AI governance programme is functioning in practice, not just on paper.
The third is operational lag. Without this chain, investigations take longer, risk teams cannot detect patterns, and legal teams are left reconstructing events from partial logs and disconnected systems, each with different export capabilities, retention policies, and access controls. That is the reality for firms without a capture foundation, and it is precisely the gap that demands a fundamentally different approach.
One Layer Across Every Tool, Every Interaction
A unified capture layer is that different approach, and for Smarsh, capture is where it starts. With their compliance solution, every AI interaction an employee has, every prompt entered, every response generated, and every file shared is recorded, at source. Nothing is reconstructed, nothing inferred; the record exists because it was captured at the moment it was created.
