When a prospective client wants to learn about your firm, they do not always go to your website first.
They open ChatGPT. They run a search through Perplexity. They ask Google’s AI Overview what your firm does, who you serve, what your fees look like, and whether you are a fiduciary.
Those systems answer.
Whether the answer is accurate is a separate question. And for registered investment advisors, broker-dealers, law firms, and other regulated businesses, the accuracy of that answer is no longer just a marketing concern.
How AI Systems Fill Information Gaps
AI systems that answer questions about specific businesses do not retrieve information directly from those businesses. They infer.
They draw on training data, indexed content, third-party directories, and whatever structured signals your website and associated properties provide. When those signals are clear and complete, the output tends to be accurate. When they are ambiguous, incomplete, or absent, the system fills the gap.
Inference is not precision. It is approximation.
For a consumer brand, an approximation may be harmless. A slightly wrong product description costs nothing. But for a regulated firm, the details that AI systems are most likely to approximate are precisely the ones that carry compliance weight.
What services does the firm offer? Is it a fiduciary? What are the fee structures? Is the advisor registered? What strategies are used? Who is the firm affiliated with?
These are not casual details. They are material disclosures.
The Regulatory Dimension
The SEC and FINRA have both identified AI-related misrepresentation as an active risk area for registered advisory firms.
FINRA’s 2026 Annual Regulatory Oversight Report states directly that “misrepresentation or incorrect interpretation of rules, regulations or policies or inaccurate client or market data can impact decision making.” The concern is not hypothetical. It reflects what regulators are already seeing as AI systems become the first point of contact for clients researching financial services.
The SEC has gone further on enforcement. Within a single year, the SEC commenced four enforcement actions against registered firms for misrepresentation of AI’s purported capabilities and usage in their advisory services. Those cases targeted firms that overstated their own AI use. But the enforcement logic applies in both directions: accuracy in how a firm is represented to clients is a regulatory obligation, regardless of the source.
The Advisers Act requires that registered investment advisors not engage in fraudulent, deceptive, or manipulative conduct. It requires accurate disclosure of material information. It does not include an exception for misrepresentations that originated with a third-party AI system.
A firm cannot comply with disclosure requirements it does not know are being violated.
Who Bears the Risk
Courts are beginning to address the question of liability when AI systems produce false information about businesses or individuals.
In 2024, a Canadian tribunal held Air Canada directly liable for misinformation provided by the company’s own customer service chatbot (Moffatt v. Air Canada, 2024 BCCRT 149). The court rejected the argument that the chatbot was a separate entity and that the company bore no responsibility for its outputs. The principle established is simple: if an AI system is making representations on your behalf, or in your name, you are accountable for those representations.
The cases against AI providers themselves have largely failed. Courts have shielded companies like OpenAI behind the “no reasonable reader would treat this as fact” defense, noting that disclaimers about AI accuracy are prominently communicated. That defense protects the AI provider. It does not protect the business being misrepresented.
The result is a liability structure where AI providers face limited exposure, and the regulated firms whose names and services are being described face unquantified risk without meaningful recourse.
The Infrastructure Connection
For regulated businesses, the risk is not distributed evenly.
AI systems build their understanding of a firm from available signals. A firm with a well-structured website, accurate entity declarations, complete structured data, and consistent information across directories gives those systems something reliable to work from. The output is more likely to be accurate.
A firm with a thin or ambiguous digital presence provides almost nothing. The system infers from whatever it can find. It may find outdated information, third-party descriptions, regulatory filings that predate a service expansion, or simply nothing that clearly answers the question.
The firms with the weakest digital infrastructure are the ones most likely to be misrepresented.
This is not a speculative risk. In our March 2026 national audit of over 13,000 RIA firms we found that 50% had no DMARC record and only 10% enforced a reject policy. These are firms whose infrastructure is not configured to defend against impersonation at the email layer. The same firms are unlikely to have structured, machine-readable digital presences that give AI systems accurate information to work from.
The infrastructure gaps compound.
What This Requires
Knowing what AI systems are saying about your firm is not optional for regulated businesses. It is due diligence.
That means testing the major platforms. It means auditing what ChatGPT, Perplexity, Google AI Overviews, and similar systems return when asked about your firm’s services, credentials, and affiliations. It means identifying discrepancies between what those systems say and what your compliance documents require them to say.
It also means building the infrastructure that reduces inference. Structured data. Explicit entity declarations. Clear, consistent information expressed in machine-readable form across every property associated with your firm.
AI systems will continue to describe your business to prospective clients. The question is whether what they say is accurate, and whether your firm has done what it reasonably could to ensure that it is.
Related Research
For context on infrastructure gaps across the advisory industry, see our March 2026 audit: RIA Email Security Audit: National Infrastructure Findings.
For data on how the Great Wealth Transfer is reshaping advisor discovery, see: $124 Trillion Is Moving. Most Financial Advisors Are Invisible to the People Who Will Inherit It.
Hyrizen works with businesses and regulated organizations on AI visibility, data architecture, and machine-readable infrastructure. If you want to know how AI systems currently describe your firm, request an audit.