Establishing the Data Integrity and Verification Methodology for AI Visibility (www.aivojournal.org)

AIVO Standard Institute today published DIVM v1.0.0, a governance-grade Data Integrity & Verification Methodology designed to make AI visibility measurements reproducible, auditable and legally defensible across LLM ecosystems (e.g., ChatGPT, Gemini, Claude). DIVM codifies a three-phase verification flow—Data Capture → Replay Verification → Audit Certification—backed by full metadata logging, a replay-harness specification, and SDK/API schemas for third‑party auditors and dashboard vendors. It sets explicit statistical reproducibility thresholds (CI ≤ 0.05, CV ≤ 0.10, ICC ≥ 0.80) and a ±5% reproducibility tolerance, and aligns its evidence architecture with anticipated 2026 regulatory requirements (EU AI Act, ISO/IEC 42001, SOX-aligned assurance). For practitioners and regulators this turns previously ad hoc visibility sampling into verifiable evidence: auditors get repeatability criteria, enterprises get protection against “invisible” revenue erosion or misreported exposure, and regulators gain an auditable foundation for AI assurance. Technically, DIVM’s combination of strict metrics, metadata replay, and open verification schemas enables independent reproduction of visibility claims and supports integration via APIs and SDKs. The Institute is urging adoption and contribution via its open-source GitHub—positioning DIVM as a GAAP-like discipline for AI visibility that aims to standardize trust, compliance, and measurement across AI deployments.