AI Governance Methodology for Evidence Mapping
Move78 turns AI governance frameworks into editable implementation evidence: inventories, control matrices, policy files, vendor diligence records, risk registers, FRIA starter materials, agentic AI control boundaries, and board reporting packs.
Methodology position
Move78 does not sell legal conclusions. It sells a practical artifact system for teams that need to classify, map, document, assign, review, and retain AI governance evidence.
How framework requirements become evidence artifacts
The methodology converts regulatory and framework language into fields, decisions, owners, and evidence objects. The output is a working file structure, not a theoretical slide deck.
| Method step | What happens | Output |
|---|---|---|
| Source reading | Identify relevant framework requirements, principles, risk categories, and evidence expectations. | Framework source register and review note |
| Control normalization | Translate overlapping governance requirements into common control themes. | Unified controls matrix |
| Evidence design | Convert controls into operational artifacts that owners can maintain. | Inventory, risk register, evidence tracker, policy files |
| Buyer adaptation | Leave fields editable so each organization can tailor ownership, scope, maturity, and review cadence. | Client-specific implementation file |
| Version discipline | Maintain dates, review notes, and change summaries for governance hygiene. | Changelog and last-reviewed fields |
Evidence objects used across Move78 ACT
AI system register
Records AI systems, owners, vendors, use cases, data sensitivity, deployment status, and evidence gaps.
Unified control matrix
Maps governance expectations into normalized controls that can be owned, tested, reviewed, and evidenced.
AI risk register
Links risks to owners, mitigations, evidence, status, and review cadence.
AI vendor diligence
Structures procurement and security questions before approving AI systems or AI-enabled suppliers.
MCP and agent control records
Captures tool permissions, identity scope, escalation rules, human override, and incident/shutdown triggers.
Board reporting pack
Converts technical and compliance evidence into decision-ready executive reporting.
Claim control rules
- No claim of guaranteed compliance, safe harbor, certification, or regulator acceptance.
- No unsupported claim of being the best, only, most complete, or market-leading solution.
- No legal conclusion about whether a buyer is a provider, deployer, developer, or covered entity.
- No claim that a template alone satisfies ISO/IEC 42001, EU AI Act, Colorado AI Act, NIST AI RMF, or security requirements.
- All buyer-specific legal, tax, privacy, cross-border, and regulatory positions should be reviewed by qualified professionals.
Frequently Asked Questions (FAQs)
Does the Move78 methodology guarantee compliance?
No. The Move78 methodology organizes governance evidence and implementation artifacts. It does not provide legal advice, certification, conformity assessment, regulatory approval, audit assurance, or safe harbor. The methodology is useful when a team needs a defensible structure for inventory, risk, controls, ownership, and evidence.
What source materials does the methodology use?
The methodology uses public regulatory and framework source materials, including Regulation (EU) 2024/1689, ISO/IEC 42001, NIST AI RMF, NIST AI 600-1, OWASP Agentic AI guidance, and Colorado AI Act materials. Source interpretation should be reviewed by qualified professionals where legal, tax, audit, or regulatory reliance is required.
How are frameworks mapped into artifacts?
Frameworks are mapped into evidence objects that teams can actually maintain: inventory fields, risk records, owner assignments, policy clauses, control matrices, vendor questions, evidence trackers, incident records, and board reporting items. The goal is practical traceability, not decorative framework coverage.
How should buyers use the methodology?
Buyers should use the methodology to adapt the files to their organization, assign internal owners, document decisions, retain evidence, and maintain version history. The methodology works best when it is reviewed against real AI systems, real vendors, real data flows, and the organization’s actual risk appetite.
Why does Move78 use an evidence-first approach?
Move78 uses an evidence-first approach because governance without records is not operational control. Policies matter, but buyers, auditors, boards, and customers usually ask for evidence: what exists, who owns it, what risk was accepted, what control operates, and where the review history is kept.
Source and review note
This page is based on Move78 product scope and public framework references. It is not legal advice and does not certify compliance.
| Reference | Source |
|---|---|
| EU AI Act | Regulation (EU) 2024/1689 on EUR-Lex |
| ISO/IEC 42001 | ISO/IEC 42001:2023 official ISO page |
| NIST AI RMF | NIST AI Risk Management Framework |
| NIST AI 600-1 | NIST Generative AI Profile |
| OWASP Agentic AI | OWASP Top 10 for Agentic Applications |
| Colorado AI Act | Colorado SB24-205 and Colorado AG rulemaking page |
Published: 2026-04-28. Last updated: 2026-04-28. Last reviewed against official source pages: 2026-04-28.