Executive Summary
Public auditors engaged with FCMs and DCOs need precise command of Regulation 1.25's 2024 amendments — the specific concentration ceilings, the tiered thresholds tied to fund and manager asset sizes, and the sequenced compliance deadlines that govern how quickly those firms must conform. Across two questions that sit squarely in public audit work on customer-funds segregation, AI assistants produced confident, incorrect answers that were retracted only under direct challenge.
The failures were not edge-case ambiguities: both concerned hard numerical rules — a 50% ceiling for large government money market funds and a March 31, 2025 deadline for SIDR and risk-disclosure updates — that AI assistants either omitted entirely or fabricated as something materially different. For a public auditor whose sign-off implicitly certifies that an FCM's segregated-funds investment policy conforms to current CFTC rules, either error creates a document trail that is wrong from the start.
How AI gets this regulation wrong
The dominant pattern across this regulation is confident fabrication: AI assistants presented invented rules as settled law, then walked them back when pressed — but only when pressed. Both failure instances involve AI tools asserting uniform, simplified thresholds in place of the actual tiered structure, or fabricating a compliance deadline by interpolating vaguely from the general effective date rather than citing the specific date the rule text states.
| AI's Failure Mode | Count | Affected findings |
|---|---|---|
| Exposed Fabrication | 2 | Finding#1 · Finding#2 |
What that means for your practice
Both failures map directly to wrong-deliverable risk: the practitioner produces a memo, opinion, or sign-off built on parameters that do not exist in the regulation as written. For a public auditor on a Reg 1.25 engagement, the exposure is not theoretical — a concentration-limit table or a compliance-calendar entry sourced from AI output rather than the final rule text will be incorrect in ways that the client may only discover during an examination or a deficiency letter.
| Risk Impact | Count | Affected findings |
|---|---|---|
| Wrong deliverable | 2 | Finding#1 · Finding#2 |
When this affects Public Auditors
Public auditors reach for AI tools most naturally when they are scoping or preparing for a Reg 1.25 engagement — building the testing matrix that maps each investment category to its applicable ceiling, or confirming the exact compliance calendar before scheduling fieldwork. On a regulation with tiered thresholds (where the permissible exposure to a government money market fund depends on the fund's asset size and the management company's AUM), those look-up moments are precisely where the AI failure lands.
An auditor who accepts a flat "10% per fund, uniformly" answer builds a testing program that never checks whether the FCM holds a position in a qualifying large-fund that should instead be measured against the 50% ceiling — and the whole engagement is scoped wrong.
Timing errors create a separate category of exposure. Auditors advising FCMs on compliance-program updates need to distinguish between the general effective date of the rule and the separately enumerated deadline for SIDR and customer risk-disclosure updates. Confusing a "six months to a year after effective date" estimate — which is what AI assistants produced — with the actual March 31, 2025 hard deadline means the client gets an implementation timeline that is wrong by a factor of roughly five to ten on the shorter end.
If the auditor incorporates that timeline into an advice memo or a management letter, the client may miss the statutory deadline and face examination findings for a violation that auditor guidance arguably helped cause.
The compounding risk is that both failure types — wrong concentration parameters and wrong deadline — could appear in the same engagement document without any internal consistency check catching the error. A public auditor working from AI-assisted drafts who does not independently verify each numerical element against the final rule text has no backstop. The AI's self-retraction behavior (it admitted the fabrication only when directly challenged with the correct text) means that passive reliance produces the wrong answer; only adversarial verification produces the right one.
The findings at a glance
The table below summarises the two findings documented for this regulation, covering the question area, the nature of the AI failure, and the resulting risk category for public auditors.
Aggregate impact
Both findings cluster on the same structural characteristic of the 2024 amendments: the regulation's detail is in the specificity of its numbers, and AI assistants systematically smoothed that specificity away. The tiered concentration framework — where the 50% ceiling applies only when both the fund meets the ≥$1B asset threshold and its management company clears the ≥$25B AUM bar — is exactly the kind of multi-condition rule that AI tools flatten into a single uniform figure. The result is not a minor mis-statement; it is the elimination of the tiered structure itself, replaced by a simpler regime the AI invented.
The compliance-deadline finding follows the same pattern from the opposite direction: rather than omitting a threshold, the AI interpolated a plausible-sounding estimate ("six months to a year") from the general effective date, apparently because the exact 38-day gap between the general effective date and the SIDR deadline is counter-intuitive. A deadline that is closer than one might expect from a typical staggered-compliance structure is harder for a language model to reconstruct from general knowledge, and the AI defaulted to a more conventional-sounding range instead of the actual date.
For public auditors, the systemic implication is that AI tools are most unreliable on this regulation at precisely the points where its text diverges from intuitive regulatory templates — tiered thresholds with specific asset-size cutoffs, and a short-fuse secondary compliance date. Those are the points a practitioner would most benefit from AI assistance on, since they are the details most likely to be forgotten or conflated across engagements. The failures documented here suggest AI assistants cannot be trusted to supply those specifics reliably; they must be verified independently against the rule text each time.
What your team should do
The default position for any Reg 1.25 engagement is that the numerical elements — concentration ceilings, asset-size thresholds, and compliance dates — must be verified directly against the final rule text and the CFTC's official regulatory releases. AI assistants are demonstrably capable of producing plausible-sounding but fabricated numbers on this regulation, and the fabrications do not announce themselves; they read as confidently stated facts. Treat any AI-generated concentration limit or compliance deadline as a draft placeholder, not a verifiable citation, until a team member has confirmed the figure against the source document.
For the investment-policy testing component specifically, build the audit checklist from the final rule's concentration table rather than from any summary — AI-generated or otherwise. The tiered threshold structure (the 50% ceiling that applies only above both the fund-size and management-company-size cutoffs, layered alongside the issuer-based caps) needs to be explicit in the testing matrix. If a junior prepares that matrix with AI assistance, a senior reviewer's first check should be whether the ≥$1B / ≥$25B two-condition trigger is represented; its absence is the specific failure pattern documented here.
For compliance-calendar work, the SIDR and risk-disclosure update deadline (March 31, 2025) is short enough relative to the general effective date that it warrants a dedicated verification step, separate from confirming the effective date itself. The AI failure on this point was not a near-miss — it produced an estimate an order of magnitude longer than the actual interval.
AI tools are appropriate for orientation and initial scoping on this regulation — identifying which sections to read, what categories of investment are covered, how the amendment fits into the prior Reg 1.25 history — but the numerical content that drives actual audit work must come from the rule text directly.
How RLB Can Help
RegLeg's published hallucination research functions as a pre-flight check for any engagement where your team is leaning on AI tools to interpret federal or state regulatory requirements. Before you rely on AI-generated output to scope an audit, frame a control opinion, or brief an audit committee on a regulatory exposure, the research surfaces the specific failure modes — misquoted thresholds, inverted obligations, fabricated agency guidance — that AI assistants produce on the exact regulations you are likely working with. It is free, regulation-specific, and indexed by jurisdiction, so your staff can pull the relevant findings before a workpaper hits review.
For firms running parallel audit engagements across the same regulatory portfolio — Single Audit Act compliance, FDIC Rules of Practice, GASB interpretations, OMB Uniform Guidance — RLB offers bespoke deep-dives scoped to the specific regulations your teams are touching. That means a structured failure-mode analysis against your actual engagement universe, not a generic survey, delivered in a format your QC leadership can use to set reviewer expectations and document the firm's AI-reliance rationale. Where multiple engagements share the same regulatory substrate, the economics of a joint engagement frequently make sense.
On the training and policy side, RLB can develop CPD-aligned content that maps AI failure patterns to the specific professional-judgment moments public auditors encounter — distinguishing criteria-setting from evidence evaluation, handling AI output in a GAGAS-compliant documentation chain, or flagging where an AI assistant's confident but wrong regulatory characterization could migrate into a finding narrative. We can also conduct a confidential review of your firm's existing AI-use policy against RegLeg's failure-mode catalogue, identifying where current guardrails address the observed failure patterns and where gaps remain — without publication and without attribution.