When AI Gets Your Regulated Firm Wrong: A Defence Playbook

This is a methodology piece rather than a case study. It sets out how we would approach the problem for a regulated firm, so you can judge the process on its merits rather than on results we would have to ask you to take on trust.

The problem itself is straightforward to state. An AI assistant tells a prospect something about your firm that is wrong — the wrong regulator, a licence category you do not hold, a fee structure you do not charge, a service outside your permissions. The prospect acts on it. You never find out.

In an unregulated sector that is embarrassing. In a regulated one it is potentially a compliance matter, because a statement about your permitted activities circulating in public — even one you did not make — is a problem your regulator may take an interest in.

**Why regulated firms are disproportionately exposed**

Three reasons compound.

Regulated firms publish conservatively. Compliance review tends toward removing specifics, and the resulting website says less about permissions, activities and jurisdictions than the public register does. A model with little authoritative material to work from fills gaps from adjacent sources — competitors, directories, generic explainers about the sector.

Second, the sector's vocabulary is precise and the distinctions are invisible to a general-purpose model. The difference between advising on investments and arranging deals in investments is enormous to a regulator and almost undetectable to a language model summarising a category.

Third, corporate structures are complex. A group with a DIFC entity, an ADGM entity and an offshore vehicle presents several similar names to something trying to identify one company, and conflation is the natural failure mode.

**Step one: find out, systematically**

You cannot fix what you have not observed. Build a standing prompt set covering the claims that would matter if they were wrong — regulator, licence category, permitted activities, jurisdictions, fee structure, named individuals and their credentials.

Run it across the major assistants, in every language your clients use, and record answers verbatim. Repeat monthly. In regulated sectors we would run it more often than that, because the exposure window is what you are trying to shorten.

**Step two: triage by consequence, not by annoyance**

Not every inaccuracy deserves the same response. Rank by what a prospect acting on it would actually do.

Highest priority: anything touching regulatory status, permissions or licensing. These carry consequences beyond a lost deal.

Next: fee and pricing claims, which drive real decisions and are the most commonly wrong.

Then: service scope — being credited with capabilities you do not have is its own risk, particularly if a client engages you expecting them.

Lowest: tone, emphasis, dated descriptions. Irritating, rarely consequential.

**Step three: fix at source, not on your own site alone**

Publishing the correct fact prominently on your own site is necessary and rarely sufficient. The work is tracing where the wrong claim actually lives.

Usual suspects: directory entries almost nobody has updated in years, old press coverage, aggregator profiles built from stale scrapes, a partner's website describing you incorrectly, and your own historic pages that were never removed.

Correct at source, then reinforce with unambiguous structured data on your side stating the same fact. Prose can be misread. Markup asserting a licence identifier cannot.

**Step four: make the truth easier to find than the error**

Models prefer corroborated claims. If your regulatory position is stated only on your own site, it is one source. If it is stated identically on your site, in your structured data, on the regulator's public register and in your professional profiles, it is four sources agreeing.

This is why entity consistency matters more in regulated sectors than anywhere else. It is not tidiness. It is the mechanism by which the correct version outweighs the incorrect one.

**Step five: re-test and record**

Corrections propagate unevenly. Retrieval-based answers can update within days of a source changing. Answers rooted in training data may persist far longer.

Keep the record: what was wrong, when it was found, what was changed, when it cleared. In a regulated firm that log is worth having independently of the marketing benefit, because it evidences that you identified an issue and acted on it.

**What cannot be promised**

There is no mechanism today by which an ordinary business compels an AI provider to correct a statement. Some platforms offer feedback routes; none offer anything resembling a dependable correction process at commercial speed.

So this is containment and influence rather than control. Anyone describing it as control does not understand it, or is hoping you do not.

**Two things that do not work**

Publishing a rebuttal page tends to backfire — it creates a page firmly associating your firm with the wrong claim, which is the opposite of what you want retrieved.

Waiting also does not work. Unlike a bad review, which ages out, an incorrect fact is repeated identically to everyone who asks, indefinitely, until the underlying sources change.

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