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One Governance Program.
Every Framework Covered.

AI regulations are multiplying. Trustible maps your governance program to 10+ frameworks simultaneously — so your teams document once and stay current as requirements evolve.
Frameworks supported
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More AI use cases approved
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Reduction in AI governance cycle times
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Audit-ready AI use cases
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Understanding AI Frameworks

What Are AI Governance Frameworks?

AI governance frameworks are the regulations, standards, and guidelines that define how organizations should develop, deploy, and oversee AI systems responsibly. They come in two main forms.

Regulations are legally binding. The EU AI Act and Colorado SB 21-169 carry enforcement penalties and mandatory timelines. You don’t choose whether to comply — you choose how to prove it.

Standards and voluntary frameworks like NIST AI RMF and ISO 42001 are adopted by choice, but increasingly expected. Enterprise customers, regulators, and investors treat them as evidence that your AI governance program is real, not just documented.

Regulations

Binding legal requirements with enforcement penalties. Compliance is mandatory for organizations in scope.
EU AI Act, Colorado SB 21-169, NAIC Model Bulletin

Standards

Certifiable management system standards from international bodies. Increasingly required by customers and regulators.
ISO/IEC 42001

Voluntary Frameworks

Guidance from government agencies and coalitions. Referenced in procurement and enterprise risk programs.
NIST AI RMF, Singapore AI Framework, CHAI

Supported Frameworks

Browse All Frameworks

Each framework page includes requirements detail, capability mapping, and a step-by-step implementation guide.
Regulation
European Union

EU AI Act

Binding AI law. Risk-based obligations with penalties up to €35M or 7% of global turnover.
International Standard
Global

ISO/IEC 42001

Binding AI law. Risk-based obligations with penalties up to €35M or 7% of global turnover.
Voluntary Framework
United States

NIST AI RMF

Most widely referenced US AI governance framework — GOVERN, MAP, MEASURE, MANAGE.
State Law
Colorado, USA

Colorado SB 21-169

Insurance AI fairness testing, board accountability, and annual compliance certification.
State Law
Colorado, USA

Colorado AI Act (SB 24-205)

Consumer protection requirements for high-risk AI across multiple sectors.
Voluntary Framework
Singapore

Singapore AI Framework

IMDA and PDPC framework covering accountability, human-centricity, transparency, and fairness.
Government Standard
Australia

Australia AI Standard

DTA technical standard for Commonwealth agencies — risk assessment, procurement, human oversight.
Industry Framework
US Insurance

Insurance AI (NAIC+NYDFS)

Consolidated US insurance framework: written programs, bias testing, and vendor accountability.
Industry Framework
Healthcare

Healthcare AI (CHAI)

Coalition for Health AI guidelines for responsible AI in clinical and operational settings.
Government Framework
United States

GAO AI Framework

US Government Accountability Office framework for AI accountability and governance best practices.
Regulation
South Korea

South Korea AI Basic Act

Korea's foundational AI legislation establishing governance structures for AI development.

Trustible Methodology

How We Map Frameworks

The Problem We're Solving

Most AI regulations share significant structural overlap — but organizations treat each one as a separate compliance track. Separate owners, separate documentation, separate audit trails for what is fundamentally the same governance activity.

Consider documenting human oversight mechanisms for an AI system. The EU AI Act requires it under Articles 14 and 22. NIST AI RMF references it across MAP-3.5, MEASURE-3.2, and MAP-2.2. ISO 42001 addresses it in Annex B sections B.3, B.4, and B.9. Without normalization, that’s three separate tasks. With Trustible Controls, it’s one.

How We Build Framework Mappings

Trustible’s AI policy and regulatory experts read every framework in full — identifying every obligation, clause, and requirement. Requirements are normalized into Controls mapped to every article and clause they satisfy across all supported frameworks. Satisfy a control once, and your compliance posture updates across every applicable framework simultaneously.
1

Read the Framework

Every regulation and standard read in full by AI policy and legal experts.
2

Define Controls

Requirements normalized into structured Controls with guidance, questions, and evidence requirements.
3

Map Across Frameworks

Each Control mapped to every article and clause it satisfies – across all supported frameworks.
4

Scope to Use Cases

Designations and framework assignments auto-determine which controls apply to each AI system.
5

Satisfy Through Work

Controls satisfied through policies, workflows, documentation fields, or uploaded evidence.
6

Stay Current

As frameworks evolve, Trustible updates mappings. Your compliance work carries forward.

Not All Framework Are the Same

Trustible’s AI policy and regulatory experts read every framework in full — identifying every obligation, clause, and requirement. Requirements are normalized into Controls mapped to every article and clause they satisfy across all supported frameworks. Satisfy a control once, and your compliance posture updates across every applicable framework simultaneously.

ATTRIBUTE

NIST AI RMF

ISO 42001

EU AI Act

Type

Voluntary Framework

International Standard

Enforceable Regulation

Requires an Audit?

Requires Org Policy?

Model Eval Guidance?

Recommends Controls?

Requires Risk Assessment?

Requires Model Transparency?

Requires Impact Assessment?

Requires Incident Reporting?

Trustible Controls

The Controls Architecture

Controls are the operational core of Trustible’s compliance architecture. Each Control is a normative statement about a specific action, documentation requirement, or process — mapped to every framework article it satisfies. Controls are organized hierarchically: parent controls describe a broad governance area; sub-controls break it into specific, assessable requirements.

Each Trustible Control includes

Control Statement

A clear, concise description of what the control requires

Guidance

Detailed implementation guidance explaining what good looks like

Guiding Questions

Specific questions used to assess whether the control is satisfied

Framework Mappings

The specific articles and clauses this control addresses across frameworks

Suggested Evidence

What documentation or artifacts demonstrate the control is in place

Control Hierarchy Example

POL-AIP-1 (Parent)

The organization has an established AI policy covering key roles, responsibilities, and policies related to AI development and internal use of AI tools.

POL-AIP-1-1 (Sub-control):

The organization’s AI policies clearly define relevant roles and responsibilities for building and governing AI systems.

Control Types

Address what your AI governance policies must cover. Satisfied by linking policies to a control and verifying — through AI-assisted analysis — that the policy adequately addresses the control’s guiding questions.
Represent specific processes, assessments, or recurring governance activities — things your team runs on a cadence or in response to specific events like a new AI system intake or a material change.
Ensure critical information is properly recorded — at the use case level (human oversight mechanisms, deployment context) and the model level (model cards, dataset documentation, version history).
Define specific tests, benchmarks, or assessments that should be performed on AI models — performance, robustness, bias, fairness. Particularly relevant for GPAI and sector-specific regulations.
Cover disclosure requirements to users, affected individuals, regulators, and the public — AI labeling, incident notifications, and explanation rights for AI-assisted decisions.
A dedicated set of controls for obligations unique to the EU AI Act: CE marking, Annex IV technical documentation formats, GPAI transparency requirements, and post-market monitoring.

Scoping

Designations: The Right Controls for Each Use Case

Not every control applies to every AI system. Designations are attributes assigned to a use case that reflect its regulatory classification. When you assign frameworks and designations, Trustible automatically determines which controls apply — so your teams see only what’s relevant.
High Risk
Surfaces the full set of EU AI Act documentation, technical, and oversight controls for systems classified as high-risk under Annex III.
Provider
Your organization placed this AI system on the market or put it into service. Provider obligations are more extensive than deployer obligations.
Deployer
You're using an AI system built by another organization. A different, generally narrower, set of controls applies — though third-party accountability still does.
GPAI
The system uses or is a general-purpose AI model. GPAI-specific controls apply under the EU AI Act from August 2025.

Trustible Maps Your Governance Program to Every Framework at Once.

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