Financial services firms are moving quickly to adopt AI note-taker tools because they make meetings easier to document and knowledge easier to share. But that convenience comes with a new kind of record: AI-generated content that can capture sensitive conversations before compliance has reviewed them.
According to ACA Group, for chief compliance officers, the concern is straightforward—these tools can collect potentially material non-public information (MNPI), reshape what counts as a “business record”, and increase the chances of regulatory and operational mistakes.
One of the biggest risks is unvetted sensitive information. AI note-takers can capture MNPI in real time, and those notes can circulate internally long before a compliance review happens. If employees act on that content, or if it’s shared too broadly, firms may find themselves facing questions about controls around information barriers and the handling of sensitive data.
Regulatory scrutiny is another pressure point. If AI-generated notes exist, regulators can ask for them. During an SEC or FINRA inquiry, unreviewed, widely distributed summaries can make it harder to demonstrate supervision and governance. The audit trail can become more complex, not less, particularly if it is unclear who reviewed the notes, when they were shared, and whether the firm can show consistent oversight.
There is also the issue of misinterpretation or loss of context. AI-generated notes may compress nuance, misattribute statements, or omit details that matter. If the full transcript is not retained, teams may be left with a summary that is treated as authoritative even when it is incomplete—raising both operational risk and the risk of misunderstandings during supervision, investigations, or disputes.
Integrating AI notes into research workflows creates its own tension. Researchers understandably want quick access to insights, but granting access without filtering, controls, and monitoring can create a direct conflict between speed and compliance obligations—especially where research discussions brush up against MNPI or restricted-list considerations.
Compliance workload can increase, rather than decrease. As the volume of meeting notes grows, so does the amount of content that could require review, triage, retention decisions, and escalation. When teams are flooded, the likelihood rises that genuinely high-risk items are missed or dealt with too late.
Data leakage and client confidentiality are equally critical. Depending on how the tool is deployed, notes may be stored or transmitted in ways that expose client information or internal strategies. A breach—or even an inadvertent disclosure—can damage trust, trigger notification requirements, and lead to regulatory consequences.
Finally, inconsistent oversight can spread quickly across a firm. Without standardised policies and review processes, different teams may apply different rules for using and sharing AI-generated content. That inconsistency is exactly what regulators tend to probe when they assess whether controls are meaningful or merely informal.
ACA positions its support around bringing structure to this new content stream. Its Research Compliance Solutions, powered by Encore AI, are framed as a way to control MNPI exposure with safeguards, standardise review processes so firms can demonstrate oversight during audits, and integrate notetakers into research workflows without compromising regulatory obligations.
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