When AI Disclosure Arrives Before Evidence
Why the SEC Investor Advisory Committee Has Created an Evidentiary Gap
AIVO Journal — Governance Commentary
On December 4, 2025, the SEC Investor Advisory Committee approved a recommendation urging the Commission to consider a disclosure framework addressing the impact of artificial intelligence on issuer operations. The recommendation is non-binding, but its significance lies elsewhere. It positions AI not as a technical curiosity or ethical abstraction, but as a disclosure-relevant governance topic for public companies.
What the recommendation does not do is specify how issuers can substantiate the disclosures it anticipates. That omission matters. Disclosure expectations are forming faster than the governance infrastructure needed to support them, creating an evidentiary gap that will surface only when disclosures are later scrutinized.
AI becomes a disclosure surface
The Committee’s recommendation centers on three proposals:
- Issuers should adopt a definition of artificial intelligence.
- Issuers should disclose board oversight mechanisms, if any, related to AI deployment.
- If material, issuers should report separately on AI’s effects on internal operations and on consumer-facing matters.
Individually, these proposals appear incremental. Collectively, they shift AI from an internal productivity tool to an external disclosure surface. Once AI is framed in this way, its effects on how products, services, and risks are understood by consumers and investors become relevant to securities disclosure.
This framing does not assert that AI outputs are issuer speech by default. It does, however, acknowledge that AI-mediated representations can materially affect consumer and investor understanding. Where an issuer concludes that such effects are material, the question becomes how that conclusion is supported.
Disclosure without evidence is unstable
Narrative disclosure works when the underlying facts are stable, observable, and internally controlled. Public-facing AI systems challenge all three conditions.
Large language models produce probabilistic outputs that vary across time, prompts, and model versions. Issuers typically do not operate these systems, yet they increasingly influence how third parties understand issuer products, services, and risks. The Committee implicitly recognizes this tension by emphasizing oversight and materiality, while avoiding prescriptive requirements for monitoring or evidentiary logging.
The result is a familiar regulatory pattern. Principles are articulated before operational mechanisms are specified. Once disclosure language exists, it may later be scrutinized for reasonableness and consistency, particularly following an incident tied to AI-mediated representations. At that point, the absence of contemporaneous evidence becomes a governance problem.
Oversight must be observable to be meaningful
The recommendation’s instruction to “disclose board oversight mechanisms, if any” is carefully phrased. It signals that the presence or absence of oversight is itself disclosure-relevant.
Boards are not expected to manage AI systems. They are expected to demonstrate that material risks are visible, escalated, and reviewed. In the context of AI-mediated representations, visibility is the limiting factor. Policies and charters describe intent, not observation. Without evidence of what claims were present at a given time, oversight remains declarative rather than inspectable.
Once oversight is disclosed, questions naturally follow. What information did the board receive. How frequently. Based on what evidence. These questions do not arise because AI is novel, but because disclosure transforms governance assertions into factual claims.
Materiality cannot be assessed in the abstract
The Committee conditions reporting obligations on materiality. That qualifier is central, but often misunderstood.
Materiality is not a property of AI as a technology. It is a judgment about the likelihood and magnitude of impact arising from specific representations in specific contexts. Many issuers will reasonably conclude that internal AI deployment does not rise to disclosure significance. The risk emerges where AI-mediated claims materially influence consumer decisions, product understanding, or risk perception.
In those cases, materiality determinations rely on visibility into what claims are actually being made. Without such visibility, issuers are left to infer impact indirectly, increasing the risk that judgments appear inconsistent or unsupported when revisited later.
Mapping the evidentiary gap
The Committee’s three recommendations implicitly create three evidentiary questions:
- Definition of AI requires scope boundaries and inventories.
- Oversight disclosure requires demonstrable processes and review artifacts.
- Reporting on material effects requires support for materiality judgments and consistency over time.
The recommendation addresses none of these evidentiary requirements directly. It leaves issuers to bridge the gap themselves.
Claim-level evidence as a governance primitive
This is where Reasoning Claim Tokens, or RCTs, enter the discussion.
RCTs are a minimal evidenti construct designed to capture discrete, time-indexed claims expressed by AI systems at the point of interaction. They do not explain model internals, assess correctness, or attempt to control outputs. Each token records what was said, about what, and when, together with contextual metadata.
For governance purposes, RCTs matter because they transform AI behavior into inspectable evidence. They allow issuers to demonstrate what claims were observable at a given time, rather than reconstructing narratives after the fact.
To be defensible, RCT implementation requires clear guardrails. These include defined sampling frames, frozen prompts, logged model versions, time stamps, claim taxonomies established in advance, and retention policies that prevent selective reconstruction. Without such controls, claim-level evidence risks being dismissed as anecdotal.
When properly implemented, RCTs do not replace judgment. They support it.
Oversight, supported rather than asserted
RCTs do not constitute oversight on their own. Oversight still requires interpretation and decision-making. What RCTs provide is a factual substrate. They make AI-mediated representations observable and reviewable by disclosure committees, risk functions, and boards without requiring technical intervention in the models themselves.
In this sense, RCTs align with how other governance evidence is used. They are records, not conclusions.
Why this will surface through scrutiny, not rule text
It is unlikely that near-term SEC guidance will mandate claim-level evidence mechanisms. Historically, the Commission articulates disclosure principles first, then tests their application through comment letters, examinations, and enforcement actions.
In those settings, the decisive question is rarely intent. It is whether the issuer can demonstrate what it knew, when it knew it, and how that knowledge informed its disclosures. AI disclosure is likely to follow a similar path. Once issuers acknowledge AI impact on consumer-facing matters, the absence of contemporaneous records becomes difficult to explain.
Conclusion
The Investor Advisory Committee’s recommendation marks the beginning of AI’s treatment as a disclosure-relevant governance topic. It does not resolve how issuers should safely support the disclosures it anticipates.
That unresolved space is where governance failures tend to emerge. Claim-level evidence does not expand disclosure obligations. It reduces the risk associated with meeting them. Issuers that recognize this early will approach AI disclosure as an evidentiary discipline rather than a wording exercise. Those that do not will encounter the gap later, under less forgiving conditions.
A question worth asking now
If an AI-related incident were scrutinized six or twelve months from now, what contemporaneous records would exist to support the issuer’s disclosure judgments made today?
The answer to that question may determine whether AI disclosure is viewed as a matter of judgment or hindsight.
Editor's Note
Recent CIO coverage has highlighted the breadth and ambiguity of the SEC Investor Advisory Committee’s AI disclosure recommendations, including concerns about definitional sprawl and investor usefulness. That ambiguity is precisely why the evidentiary question matters: where issuers exercise judgment under existing materiality standards, the ability to explain and support those judgments without hindsight depends on what was observed and recorded at the time.
