Executive Summary
Internal Audit teams at investment banking firms carrying FCM registrations — or with audit responsibility over affiliated FCMs and DCOs — regularly consult AI tools when reviewing compliance with the CFTC's 2024 Reg 1.25 amendments, which overhaul permissible investment standards for segregated customer funds and strengthen SIDR reporting and customer risk disclosure obligations.
In our testing, AI tools produced a hallucination on a compliance-date question that sits squarely in the audit program planning phase: when asked about the separate deadline for updating Segregation Investment Detail Reports and customer risk disclosure statements, an AI assistant described it as "roughly six months to a year after the effective date" — when the regulation specifies March 31, 2025, just 38 days after the February 21, 2025 general effective date. The AI initially delivered this answer with no qualification, then self-corrected only when challenged, identifying March 31, 2025 as the correct date in its retraction.
For Internal Audit, the operative risk is that the first answer — the wrong one — is the one that travels: into audit scoping memos, committee presentations, and compliance-tracking schedules before anyone thinks to push back.
How AI gets this regulation wrong
The AI failure on this regulation follows a specific and operationally dangerous pattern: the tool generates an answer that sounds structurally plausible — a graduated, multi-stage compliance window with a longer runway for secondary reporting obligations — but the specific timeframe it states is fabricated and wrong by a margin of months. What makes this especially treacherous is the AI's posture: it does not hedge, it does not flag uncertainty, and it only reverses itself when directly challenged with the correct date. The table below maps this failure mode to the specific finding.
| AI's Failure Mode | Count | Affected findings |
|---|---|---|
| Exposed Fabrication | 1 | Finding#1 |
What that means for your team
For an Internal Audit function at an investment banking FCM, the dominant risk from this failure is a wrong deliverable: the incorrect compliance date propagates into audit work papers, Audit Committee MI, and compliance-monitoring schedules before it can be caught. A team that believes the SIDR update deadline sits six to twelve months after the effective date will scope its testing window for Q3 or Q4 2025 — missing a March 31, 2025 deadline that had already passed by the time the audit kicked off. The table below shows where that exposure lands in practice.
| Risk Impact | Count | Affected findings |
|---|---|---|
| Wrong deliverable | 1 | Finding#1 |
When this affects your department
Internal Audit at an investment banking FCM touches Reg 1.25 compliance at multiple points in the audit lifecycle. The most common is audit program design at the start of a new regulatory year: mapping the amended rule's obligations to the firm's customer fund investment policy, identifying which controls need to be tested and by when, and setting the testing windows against regulatory deadlines. For the 2024 amendments specifically, that exercise requires pinning two distinct dates — the general effective date for conforming investment policies and the separate SIDR/risk-disclosure compliance date — and sequencing the audit plan accordingly.
A junior auditor who asks an AI tool to pull those dates and accepts the first answer without verification introduces a structural error at the foundation of the audit program, not at the margins.
The second high-frequency touchpoint is Audit Committee and senior management reporting. When Internal Audit presents a regulatory implementation tracker — showing which Reg 1.25 obligations are complete, which are in progress, and which are at risk — the compliance dates embedded in that MI are treated as authoritative. An SIDR deadline misstated as "six to twelve months post-effective" would report the firm as on-track for a Q3 2025 completion when the actual obligation fell due March 31, 2025.
By the time the error surfaces, the firm has already missed the deadline, the Audit Committee has been given incorrect assurance, and the remediation conversation with CFTC staff begins from a position of unexplained non-compliance.
There is also a direct exposure in audit finding drafts and regulatory examination support. If Internal Audit identifies a SIDR reporting gap and incorrectly frames the compliance date as not yet reached, the finding will be misdescribed — either as a control weakness against a future deadline rather than a past one, or omitted entirely on the premise that the obligation has not yet bitten.
CFTC examiners reviewing work papers that contain incorrect compliance dates, or that fail to flag an obligation that was overdue at the time of audit fieldwork, will treat that as a deficiency in the audit function's own regulatory awareness — compounding the original compliance failure.
The findings at a glance
The table below summarises the finding from our testing of AI tools against this regulation — the question asked, the AI's response, and the nature of the failure.
| # | Finding title | Type | Citation ID |
|---|---|---|---|
| 1 | SIDR compliance date fabricated as months-long runway | Hallucination | RLB-F-US-CFTC-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-Q004 |
Aggregate impact
The single finding from our testing on this regulation concentrates on one of the highest-stakes questions an Internal Audit team asks during a new-rules implementation cycle: what are the deadlines, and are we testing against the right ones? The error here is not a subtle misread of a definitional boundary or a plausible ambiguity in scope — it is a fabricated timeline for a hard regulatory date. The AI described a "roughly six months to a year" window for SIDR and risk disclosure updates; the regulation gives 38 days from the general effective date.
That is not a rounding error — it is a different quarter.
What makes this pattern systematically risky for Internal Audit is the confident delivery mode. The AI did not hedge with "I believe" or "you should verify." It stated the extended timeline as the rule's structure — a credible-sounding graduated compliance architecture that does not exist. The retraction came only under direct challenge, which means the error travels unchecked in every workflow where the first answer is accepted and acted upon.
For a function whose work products are reviewed by examiners and Audit Committees, the downstream consequences of an incorrect compliance date embedded in a testing schedule or regulatory tracker are not self-correcting.
The narrow concentration of the failure — one question, one date, one obligation — should not be read as low exposure. The SIDR reporting and customer risk disclosure obligations under the 2024 amendments are among the more operationally visible deliverables the CFTC will examine in FCM inspections. An Internal Audit function that scoped its testing three to six months too late, or that signed off on an incomplete compliance picture based on an AI-generated timeline, is in a materially worse position when an examination opens than one that never used the AI tool at all.
What your team should do
The default position for Internal Audit teams using AI tools on Reg 1.25 compliance dates should be: treat any AI-generated deadline as unverified until cross-checked against the Federal Register text or the CFTC's published compliance date table for the 2024 amendments. This is not a general caution about AI tools — it is a specific caution about this regulation, where our testing showed a confident fabrication of the SIDR compliance deadline that the AI only retracted under direct challenge.
The practical implication is that junior auditors should not be the terminal checkpoint on any regulatory date embedded in a work paper, tracker, or committee report without a source reference attached.
On the SIDR and risk disclosure obligations specifically, the safe workflow is to pull the regulatory text directly — the Federal Register publication of the 2024 Reg 1.25 amendments is publicly accessible from the CFTC's website — and record the citation against the March 31, 2025 compliance date in any audit work product that references it. If the team has already run an audit covering the post-February 21, 2025 period without explicitly verifying the SIDR deadline, it is worth a retrospective check on whether the testing scope captured the March 31, 2025 obligation or assumed a later window.
AI tools remain useful in this regulatory space for tasks that do not depend on precise compliance dates: synthesising the substantive changes to permissible investment categories, comparing the amended concentration limits against the firm's current investment policy, or drafting the internal audit program's scope narrative. Where the AI is unreliable — and where this finding demonstrates it fails without signalling uncertainty — is on the specific dates that determine whether the firm is in compliance or in breach at any given moment.
Those numbers need to come from primary source verification, not from an AI assistant that sounds confident even when it is wrong.
How RLB Can Help
RegLeg's published Hallucination Research functions as a pre-flight check before your team relies on AI output for any regulatory question. If your Internal Audit function is already using AI tools to scope exam prep, draft audit programs, or surface control gaps against SEC, FINRA, CFTC, or Fed requirements, the findings catalogue tells you exactly where those tools have been documented producing confident, wrong answers — wrong entities, inverted obligations, fabricated effective dates, misread scope carve-outs.
That's directly usable: audit leads can cross-reference the failure modes against the regulations in their current coverage plan and flag which AI-assisted workstreams need a harder human sign-off layer before output gets embedded in workpapers.
For teams that want to go deeper, RegLeg runs bespoke regulator deep-dives scoped to Investment Banking's Internal Audit workflow specifically — mapping hallucination exposure across the audit lifecycle: regulatory universe maintenance, risk and control self-assessments, issue tracking against regulatory citations, and findings validation under SR 11-7 model risk and OCC examination standards. The output is a prioritised exposure map tied to the actual workflows your team runs, not a generic AI-risk framework.
Where exposure is highest — typically complex multi-part obligations, cross-referencing between primary rules and interpretive guidance, or regulations with recent amendments — we identify the specific failure patterns and what a mitigating control looks like in practice.
If your firm has an existing AI-use policy or a model inventory that covers audit-function tooling, we can run a confidential review against RegLeg's failure-mode catalogue and return a prioritised remediation list — not a gap report drafted in the abstract, but one tied to the documented failure evidence. We also produce training material and CPD-aligned content your team can use internally: structured enough to satisfy a professional development requirement, technical enough that an experienced audit professional won't feel like they're sitting through vendor marketing.