title: “Getting Started” description: “Start using and understanding the AIGO Framework.”
Getting Started
AIGO provides a structured approach to AI governance, risk management, controls, assurance, implementation, and continual improvement. The repository contains both the conceptual AIGO Framework and an expanding machine-readable governance layer.1. Understand the framework
Start with the Framework Charter to understand the purpose, scope, objectives, and structure of AIGO. Then review the Framework Principles to understand the concepts that guide the framework. Next explore: * governance domains * governance roles * AI governance lifecycle * AI risk management * governance controls * maturity * AI system profiles These materials establish the conceptual foundation of AIGO.2. Understand implementation
After the framework foundation, review the implementation material. Recommended areas include: * implementation guide * governance implementation * AI system registration * classification * risk assessment * control assessment * approval * change management * incident management * monitoring * assurance * risk acceptance * retirement * continuous improvement The repository also provides procedures, templates, and examples for these activities.3. Understand the machine-readable layer
AIGO includes machine-readable schemas representing governed objects and records. These include structures for areas such as: * AI systems * risks * controls * assessments * approvals * monitoring * incidents * changes * assurance * evidence * management review * improvement * retirement * governance The schemas provide structured representations that can support governance systems and traceability.4. Understand governance relationships
AIGO should be understood as a connected governance model rather than a collection of independent documents. A representative governance chain is: AI system → classification → risk → control → evidence → assessment → approval → monitoring → assurance → improvement Different organizations may implement the sequence differently, and not every activity applies identically to every AI system. The relationship provides a conceptual model for maintaining traceability throughout the lifecycle.5. Understand machine-readable evaluation
The repository now includes a machine-readable evaluation layer in addition to the original framework and schema materials. The current repository contains validated components for: * rule definitions * rule semantic validation * evaluation semantics * evaluation fixtures * evaluation validation * orchestration * orchestration validation This allows defined governance conditions to be represented and evaluated more systematically. The evaluation layer should be understood as a governance-support capability. It does not automatically establish that an organization is legally compliant, certified, accredited, or regulatorily conformant.6. Understand validation
The repository contains validation tooling covering multiple layers of repository quality. Current validation includes areas such as: * repository references * traceability * control coverage * evidence coverage * framework consistency * document integrity * repository health * rule validation * evaluation validation * orchestration validation A successful validation run provides evidence about the integrity of the validated repository content within the relevant scope. It does not establish legal compliance, certification, accreditation, or regulatory conformity.7. Explore mappings
AIGO provides mappings to: * EU AI Act * ISO/IEC 42001 * NIST AI RMF * cross-framework relationships Mappings are interpretive and implementation-oriented. They should always be checked against the current authoritative external source before being used for legal, regulatory, contractual, or certification decisions.8. Explore examples and templates
Use the templates and examples to understand how AIGO concepts can be translated into operational governance records. Examples are illustrative and should be adapted to the organization’s: * governance model * risk profile * AI systems * applicable requirements * policies * procedures * evidence practices9. Repository structure
The major repository areas include: *framework/ — canonical AIGO Framework material
* guidance/ — implementation guidance, procedures, templates, and examples
* schemas/ — machine-readable governance schemas
* rules/ — machine-readable governance rule and evaluation specifications
* mappings/ — external framework and regulatory mappings
* validation/ — repository and machine-readable validation
* engine/ — evaluation-engine-related implementation material
* tools/ — governance validation and supporting tools