How General Counsel Can Operationalise AIVO Inside Legal Workflows

As AI-generated summaries, explanations, and rationales increasingly influence governed decisions, legal risk shifts from model performance alone to evidentiary sufficiency. When reliance becomes relevant in supervision, audit, investigation, or litigation, organisations may be unable to reconstruct what decision-makers actually saw, when they saw it, and under what constraints. This creates a distinct exposure in non-deterministic systems where later re-execution cannot reliably reproduce prior outputs.

This publication defines an evidence-first operational approach for General Counsel and legal risk leaders. It distinguishes evidentiary preservation (authenticity, provenance, temporal integrity, chain of custody) from methodological reliability (validation, bias, explainability), and argues that these questions are separable in practice and in legal scrutiny. The paper introduces a restrained artifact model for preserving AI-mediated outputs at the moment of reliance, including policies designed to prevent post-incident contamination and narrative drift. It also provides a “prompt pack” intended for reference-only use in internal assistants via Model Context Protocol (MCP) style interfaces, explicitly prohibiting paraphrase or synthesis of evidence artifacts.

This work does not claim to validate underlying AI systems or guarantee admissibility. Its contribution is a practical framework for capturing evidence-bearing AI outputs so that reliability questions can later be examined on the merits rather than litigated through speculation.

Affiliation Statement

AIVO Standard is an independent research and governance initiative. It has no affiliation with any agency, framework, or commercial methodology operating under the name AIVO or otherwise.