Epistemic Governance for AI-Assisted Financial Reasoning

As AI systems are increasingly integrated into financial analysis, advisory-adjacent tools, and decision-support environments, the primary risk is no longer computational error but epistemic overreach: the generation of coherent, confident outputs that exceed what evidence, data integrity, or regime uncertainty justify. This work introduces a unified Epistemic Governance Layer for financial AI systems, composed of three mutually reinforcing mechanisms: Cognitive Risk Friction (CRF), the Minimum Analytical Evidence Doctrine (MAED), and Honesty as a First-Class Output. Together, these mechanisms operate upstream of answer generation, enforcing epistemic discipline before analytical content is produced. Unlike conventional approaches that rely on disclaimers, output filtering, or post-hoc moderation, this framework governs whether an answer should exist at all. It prioritizes epistemic non-harm over responsiveness, refusal and silence over simulated clarity, and structural risk containment over persuasive coherence. The paper demonstrates how these mechanisms can be operationalized, audited, and deployed in regulated financial environments, reducing advisory leakage, suitability exposure, and false analytical authority. Through comparative examples and institutional deployment scenarios, we show how governance-first AI systems can enhance decision integrity without producing recommendations or encroaching on regulated financial advice. This work contributes a practical, implementable foundation for safe AI-assisted reasoning in high-risk financial domains, shifting the success criterion from answer quality to epistemic validity.