What Is the Standardized Definition of AI Governance?
AI Governance is not about policies on paper. It's about proving control under real conditions. This standard provides 15 structural tests that determine whether AI systems can actually be governed, or whether safeguards exist only in documentation.
Core Definition: The Standardized Definition of AI Governance is a global reference framework that defines what it means for an AI system to be governable under real conditions. It unites every principle, policy and safeguard into one verifiable structure: the 15 Structural Tests.
These tests replace abstract ethics and policy declarations with binary, evidence-based outcomes: pass, fail or void, that show whether an AI system can truly be controlled, traced and held accountable when inspected live.
Key Principle: Governance is proven through structure, not declaration. Claims of accountability, oversight and human control must be validated through adversarial testing that demonstrates control under real conditions.
Every AI system must pass 15 binary tests across four categories. These tests convert abstract safeguards into enforceable checks:
User Agency (Tests 1-4)
Test #1: Refusal Prevention
Test #2: Escalation Suppression
Test #3: Exit Obstruction
Test #4: Access Gating
Traceability (Tests 5-8)
Test #5: Traceability Void
Test #6: Memory Erasure
Test #7: Evidence Nullification
Test #8: Time Suppression
Anti-Simulation (Tests 9-11)
Test #9: Simulation Logic
Test #10: Simulated Consent
Test #11: Metric Gaming
Accountability (Tests 12-15)
Test #12: Cross-Accountability Gap
Test #13: Jurisdiction Displacement
Test #14: Enforcement Bypass
Test #15: Harm Scope Narrowing
Structural Integrity Rule: Any system failing more than three User Agency or Traceability tests shall be deemed structurally ungovernable pending reinspection.
In short:
Fail up to three of the User Agency or Traceability tests → still governable (conditionally).
Fail more than three → automatically deemed structurally ungovernable.
Why a Universal Standard matters
Benefits of a Universal Standard:
Cross-jurisdictional consistency: One standard works across borders, reducing compliance complexity for multinational deployments
Interoperability: Systems tested to the same standard can be compared, integrated, and procured with confidence
Reduced duplication: Organizations avoid maintaining multiple governance frameworks for different markets
Clear expectations: All stakeholders—regulators, operators, users—know what "governable" means in practice
Mutual recognition: Certifications earned in one jurisdiction can be recognized in others that adopt the standard
Level playing field: All AI systems judged by the same objective tests, regardless of size or sector
Trust infrastructure: A common language for discussing AI safety builds public confidence in regulated systems
Consistent protection: Your rights don't change based on where you live or which company's AI affects you
Equal access to safeguards: Premium users and free users get the same protections—no two-tier safety
Real recourse when harmed: Clear pathways to appeal, escalate to humans and get actual resolution
Right to refuse: You can say no to AI decisions without punishment or service loss
Right to exit: You can leave AI-driven systems without cost, delay or data lockup
Transparency you can understand: Know what influenced decisions that affect your life, work, or access to services
Verified protection: Safeguards are tested under real conditions, not just promised in policy documents
No governance theatre: Organizations can't claim protections that don't actually work when you need them
Prevention of systemic harm: Stops AI failures from cascading into widespread damage before they become irreversible
Democratic accountability: Ensures powerful AI systems remain under public control, not beyond regulatory reach
Reduced inequality: Prevents AI from creating new forms of discrimination or amplifying existing disparities
Trust in automation: Builds public confidence that AI deployment serves societal benefit, not just corporate profit
Environmental responsibility: Governance includes oversight of AI's resource consumption and environmental impact
Protection of labor and livelihoods: Addresses displacement risks and ensures human agency isn't eroded by automation
Preservation of human rights: Prevents AI from being used to violate dignity, privacy, or fundamental freedoms
Transparent power structures: Makes it clear who controls AI systems and who can be held accountable when things go wrong
Prevention of information manipulation: Stops AI-driven misinformation, deepfakes and erosion of shared reality
Long-term safety: Ensures governance keeps pace with AI capability, preventing runaway systems or loss of meaningful human control
A universal standard converts the fragmented landscape of AI governance into a unified, enforceable framework that works anywhere.
Who should use this Standard?
For Governments and Policy Makers
This standard provides a ready-to-adopt governance framework that can be implemented directly into legislation, regulation, or procurement requirements. It eliminates the need to develop governance criteria from scratch, offers a basis for international agreements and mutual recognition, and provides transparent metrics that demonstrate whether public-sector AI systems are genuinely under democratic control or operating beyond legal reach.
For Regulators
This standard provides legally defensible tests, evidence standards, and enforcement procedures that expose structural failure, not performative compliance. Each test includes specific evidence requirements and verification protocols.
For Auditors and Certification Bodies
This standard defines a uniform inspection method for verifying real governance capacity. It allows auditors to produce reproducible, cross-jurisdictional findings based on binary outcomes—pass, fail, or void. Each test specifies admissible evidence formats, integrity controls, and custody requirements to ensure results remain verifiable in court or regulatory review. It prevents audit theatre by grounding every finding in observable system behaviour, not operator claims.
For System Operators and Developers
This standard serves as a structural checklist for design, deployment, and maintenance of governable AI systems. It provides a single benchmark to demonstrate real control under live conditions and to maintain certification across jurisdictions. Compliance cannot be declared; it must be proven through successful completion of all fifteen Structural Tests.
For Insurers and Risk Assessors
This standard provides objective metrics for evaluating operational and liability exposure. Governance scores derived from the fifteen Structural Tests offer a measurable indicator of systemic risk, allowing pricing and coverage decisions to be based on demonstrable control rather than policy assertion.
For Everyone Else
This standard answers a simple question: "If something goes wrong with this AI system, can anyone actually stop it or fix it?" The 15 tests determine whether safeguards are real or just theater.
Using the standard within paid compliance, risk, or governance services
Embedding the tests or framework in commercial software or platforms
Offering certification, assurance, or auditing services based on this framework
Integrating or bundling the material into commercial AI governance products
Contact:
For written licensing of commercial implementations, certification, or integration,
please refer to the custodian of the standard via the
GitHub repository
.
Glossary & Definitions
Complete terminology reference (see Annex A in full documents)
How to Cite
Parrott, R. (2025). Standardized Definition of AI Governance (Version 1.0). https://doi.org/10.5281/zenodo.17377347
About
Custodian and Maintainer
Russell Parrott
Custodian and Maintainer of the Standardized Definition of AI Governance. Responsible for preserving the canonical repository and ensuring structural integrity of the standard.
Version History
Version 1.0CURRENT
Released: October 8-12, 2025
Status: Final and ready for standards publication
License
This work is licensed under
Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
View full license terms
Note: Commercial use requires a separate commercial license. See "Who Should Use This Standard?" section above for details.
Contact & Feedback
For questions, feedback, or to report issues with the standard, please visit the GitHub repository and open an issue.