AI Narrative Safety Commission (AINSC)
Independent standards body defining governance-aligned requirements for AI narrative integrity, drift control, and institutional assurance.
AINSC publishes and maintains AINSC-26: AI Narrative Integrity and Safety Standard, a voluntary framework that specifies minimum expectations for how large language models represent institutions across AI ecosystems.
AINSC is a non-commercial standards body. It does not provide remediation services, operational support, or vendor endorsements.
Executive Summary
The AI Narrative Safety Commission (AINSC) defines independent, governance-aligned requirements for institutional narrative accuracy within AI-mediated information environments.
AINSC-26 provides a structured framework for identifying, measuring, and managing narrative drift across LLM systems. Institutions adopt the standard to ensure that AI-generated representations remain materially accurate, current, and consistent with verified information.
AINSC maintains independence, neutrality, and alignment with established governance and risk-management practices. AINSC-26 is a voluntary, informational standard that supports internal governance and third-party assessment.
Mission Statement
Our mission is to protect institutional narrative integrity across AI ecosystems through standards-setting, assessment, verification, and certification.
The ANSC Provides Requirements for
Narrative Accuracy Assurance
Drift Detection and Scoring
Incident Readiness
Governance-Oriented Monitoring
Cross-Model Convergence Review
AINSC-26 is designed to sit beside governance frameworks such as the NIST AI RMF and AI model governance standards, with a specific focus on narrative representation.
THE PROBLEM
Narrative Drift as Institutional Exposure
Large language models increasingly shape institutional understanding through summaries, bios, crisis snapshots, and autogenerated narratives. Narrative drift can result in:
Outdated or inaccurate leadership or organizational details
Skewed coverage of past controversies
Omission of clarifying or exonerating context
Invented claims, associations, or timelines
Resurfacing of resolved or irrelevant incidents
Partial misinformation following model updates
Unmitigated drift compounds as it propagates across AI systems. AINSC-26 provides a structured governance approach for identifying, evaluating, and correcting institutional narrative risk.
GOVERNANCE STRUCTURE
AINSC maintains a multi-layer governance model to ensure independence, accountability, and alignment with global standards practice.
Standards Working Groups
Subject-matter experts in AI governance, risk, and communications draft and maintain technical requirements, definitions, and taxonomies for AINSC-26.
Independent Advisory Panel
Cross-sector reviewers provide oversight on independence, transparency, and institutional alignment during revisions and interpretive guidance.
Public Draft Review Cycle
Revisions to AINSC-26 follow a public comment process for institutions and relevant stakeholders. Change logs are published for transparency.
Annual Revision Process
AINSC reviews AINSC-26 annually or as needed to reflect developments in AI system behavior, governance practices, and institutional needs.