Most AI governance assessments are expensive, subjective, and slow — a week of consultant interviews produces a report that's already stale. This scanner shows a different approach: point it at any public repository and receive a scored compliance analysis in under 30 seconds, derived directly from source code. No generated answers. No manual review. Just a reproducible, auditable baseline that's the same every time you run it. This scanner is the code-analysis module inside the full v3 governance dashboard, which adds a Framework Explorer, Policy-as-Code engine, and a DevSecOps / PM-Scrum / Software Factory runbook library on top of the same scan engine.
Organizations building AI products are increasingly expected to demonstrate compliance with NIST AI RMF, ISO 42001, the EU AI Act, and other frameworks before going to production or facing audit. The problem is that compliance assessment has traditionally required hiring consultants or security firms to conduct manual code reviews — a process that costs weeks, depends on reviewer expertise, and produces a point-in-time artifact that doesn't stay current as the codebase evolves.
Coverage was built by translating canonical framework requirements into testable code patterns. Each framework maps to its standard pillar structure — so a NIST result can be taken directly into an AI RMF assessment, and an EU AI Act result maps directly to Article obligations. Coverage spans the major AI and data governance standards plus enterprise-specific domains.