AI Governance Assessment: Free 28-Question Template (2026)

An AI governance assessment is a structured internal review that identifies gaps in how an organization inventories, secures, tests, documents, and monitors the AI systems it builds, purchases, or uses, particularly as shadow AI expands through business-led development tools. The proposed 28-question framework scores seven domains—inventory and discovery, risk classification, data and privacy, model testing, access controls, documentation, and monitoring and incident response—on a 0-to-3 evidence-based scale and maps them to NIST AI RMF and ISO/IEC 42001. Organizations are advised to define scope and ownership, collect artifacts before scoring, turn low-scoring areas into assigned remediation work, and repeat the assessment quarterly or before major launches. Total scores classify programs as exposed, partial, managed, or evidenced, with inventory and access controls prioritized because other safeguards depend on knowing which systems exist and who can use them. The text distinguishes assessments from maturity models and audits, highlights regulatory and data-security pressures, and presents Superblocks as a platform that can automate governance evidence for AI-built internal applications through centralized inventories, SSO and role-based access, audit logs, security scanning, and deployment options.

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