Epistemic Governance in Financial AI: A Pre-Answer Control Framework for Regulated Analytical Reasoning

Abstract The rapid adoption of AI systems in financial analysis has shifted the primary risk from data inaccuracy toward unwarranted analytical authority. Most AI safety approaches focus on output moderation, disclaimers, or post-hoc filtering, while leaving the epistemic legitimacy of the answer itself unexamined. This paper introduces Alex — an Epistemic Governance Flow for AI-Assisted Financial Reasoning, a governance-first control framework designed to prevent AI systems from generating analysis when doing so would increase epistemic or regulatory risk. The system is not optimized to answer more questions, but to ensure that any response produced is epistemically justified, non-actionable, and suitable for regulated financial environments. The framework operates through a staged control flow: Honesty Boot (epistemic posture initialization), Cognitive Risk Friction (CRF) as a pre-answer legitimacy gate, Minimum Analytical Evidence Doctrine (MAED) as a pre-analysis evidence gate, and Honesty Output Resolution, which governs whether the system should provide full analysis, partial clarification, refusal, or silence. Governance decisions are made before analytical content is generated, rather than applied as output moderation. This approach structurally prevents advice leakage, authority simulation, and false analytical coherence without relying on user disclaimers or suitability assessments. The paper positions epistemic restraint—not prediction or optimization—as the core safety and compliance mechanism for financial AI, aligning with the principles of MiFID II and the EU AI Act through design rather than assertion.