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Editorial Board

From Observed Distortion to a Missing Governance Layer

From Observed Distortion to a Missing Governance Layer

Recent anonymised evidence has documented a phenomenon that many organisations have suspected but few have been able to demonstrate: external AI systems are now generating decision-relevant representations of brands that materially influence choice, without those brands having visibility into what was presented, when, or under what conditions. These observations

When Brands Lose at the AI Decision Stage

When Brands Lose at the AI Decision Stage

Anonymised Case Study Sector Consumer skincare, anti-ageing Trust-sensitive, health-adjacent category Executive summary This case study examines how widely deployed AI assistants evaluate and resolve consumer purchase decisions in a trust-sensitive category. It documents a consistent pattern in which a well-known mass-market brand is excluded,

From External AI Representations to a New Governance Gap

From External AI Representations to a New Governance Gap

Context This article builds on a previously published evidentiary record documenting observed behaviour in external AI systems under decision-adjacent conditions. Where that record was intentionally descriptive and non-interpretive, the present article addresses the governance implications of those observations and the procedural requirements that follow. A new class of

The Evidence: A Record of Observed Behaviour in External AI Systems

The Evidence: A Record of Observed Behaviour in External AI Systems

Why this record exists External AI systems now generate decision-relevant descriptions of enterprises on a continuous basis. These descriptions influence purchasing decisions, risk assessments, regulatory understanding, and reputational trust, often before stakeholders engage with any owned or official enterprise channels. Despite this influence, such representations are typically ephemeral, non-

When External Parties Ask About AI Influence

When External Parties Ask About AI Influence

The question does not come from inside the organization. It arrives from outside. The email is from external counsel preparing for a deposition. “Can you show us what external AI-generated information was relied upon at the time?” The phrasing is calm. The tone is procedural. The request is legitimate.

GEO Optimization and Evidentiary Contamination

GEO Optimization and Evidentiary Contamination

A Governance Risk for Regulated Finance AIVO Journal Executive summary Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) platforms are often described as “SEO for AI.” That framing is incomplete in regulated contexts. GEO tools do not inject prompts or control inference. They systematically reshape the external content corpus

Reasoning Without Records

Reasoning Without Records

Why AI-Mediated Decisions Require a Ledger AIVO Standard Date: January 29, 2026 Abstract Most approaches to AI governance focus on outputs: accuracy, bias, explainability, and auditability. These approaches implicitly assume that the informational basis of decisions can be reconstructed after the fact. This paper examines a different limitation. Once