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.
This means when a financial analyst uses AI to research a trade, then AI to subsequently draft a client communication, the full interaction, the prompts, the response, the files involved, are captured automatically, without any action required from the employee.
That capture extends across every platform a firm uses. Via direct API integrations with Microsoft, OpenAI, and Anthropic, a user who starts their process with ChatGPT Enterprise or Claude, and finishes it with Copilot, can have that whole multi-platform interaction fed into the same layer.
From that layer, the captured data routes wherever the firm needs it. For organisations that want full ownership, it feeds directly into their own data lakes or third-party systems. Smarsh does not lock firms into a proprietary archive. The data is theirs.
For firms that choose to bring it into the Smarsh compliance platform, something more powerful becomes possible. Those AI interactions are threaded alongside every other communication record held there, voice, email, mobile, collaboration, linked by user ID, into a single, continuous record. “You can actually follow the conversation in its entirety,” says Wiggins.
“You can see how it evolved, where decisions were made, and what influenced them.”
Enriched with metadata at the point of capture, content, context, timestamps, file types, user activity, that record becomes both rich and defensible. It is not a reconstruction. It is the truth of what happened.
The Firms That Build the Foundation Now Will Lead
For organisations still treating AI governance as a policy exercise, the direction of travel is clear. The SEC has a history of reviewing companies’ AI use, and enforcement actions for misrepresenting AI capabilities are already on record. Firms best positioned to navigate that landscape will not be the ones with the most detailed acceptable use policy, they will be the ones with the most complete captured record.
In practice, that means AI interactions captured, enriched with metadata, classified by risk, and available for supervision workflows. Compliance teams can demonstrate exactly how AI was used, what information was involved, and how it was supervised.
The firms investing in that foundation now are not simply managing today’s regulatory risk. They are positioning themselves to govern AI with confidence as the landscape tightens and AI use deepens, because without capture, governance is intention. With it, it becomes evidence.
Discover how Smarsh gives compliance teams complete visibility into AI usage, transforming AI interactions into searchable, supervised, and regulator-ready business records.
