> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aigoframework.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Controls

***

title: "Governance Controls"
description: "The AIGO Framework governance control model and its connection to risk, evidence, assessment, evaluation, and assurance."
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# Governance Controls

AIGO controls provide structured governance mechanisms for managing AI-related risks and translating governance expectations into operational practices.

Controls connect governance principles and requirements to accountable activities, implementation evidence, assessment, evaluation, assurance, and continual improvement.

## Purpose

Controls help organizations establish repeatable mechanisms for addressing AI governance expectations.

A control may define or support:

\* a governance objective
\* an expected activity
\* an accountable owner
\* applicability conditions
\* implementation expectations
\* evidence requirements
\* assessment criteria
\* monitoring activities
\* assurance activities
\* improvement actions

## Control lifecycle

AIGO controls should be considered across the AI governance lifecycle, including:

\* identification and design
\* applicability determination
\* implementation
\* operation
\* assessment
\* approval
\* monitoring
\* assurance
\* improvement
\* retirement

## Control relationships

Controls are connected to the wider governance model.

A typical traceability chain is:

**governance expectation → risk → control → evidence → assessment → assurance**

The machine-readable evaluation architecture adds the ability to represent and validate defined governance rules and evaluate applicable conditions:

**requirement / condition → rule → evaluation → result**

This creates a foundation for more systematic governance evaluation while retaining the need for organizational judgment and contextual interpretation.

## Control applicability

Not every control necessarily applies in the same way to every AI system.

Applicability can depend on factors such as:

\* AI system characteristics
\* classification
\* risk
\* lifecycle stage
\* intended use
\* deployment context
\* organizational policy
\* applicable external requirements

Applicability decisions should be documented and traceable.

## Control evidence

Controls should be supported by evidence appropriate to their implementation and operating context.

Evidence can be connected to:

\* the control
\* the AI system
\* relevant risks
\* assessments
\* approvals
\* monitoring
\* assurance activities

This supports both operational governance and later review.

## Control assessment

Control assessment determines whether controls have been implemented and whether they are operating as intended.

Assessment findings can result in:

\* remediation
\* additional evidence collection
\* changes to control implementation
\* additional monitoring
\* assurance activity
\* governance decisions
\* continual-improvement actions

## Validation

The AIGO repository includes validation capabilities that inspect control-related structures, references, coverage, and associated machine-readable governance content.

Current repository validation includes dedicated control coverage validation as part of the broader repository-quality validation process.

Validation demonstrates the integrity of the repository content within the validation scope. It does not establish legal compliance, certification, accreditation, or regulatory conformity.

## Source

The canonical control framework is maintained in:

`framework/07-controls/AIGO-AI-Governance-Controls-v0.1.md`

The machine-readable definition is maintained in:

`schemas/03-control/03-AIGO-Control-Schema-v0.1.json`

The control assessment procedure is maintained in:

`guidance/02-procedures/05-AIGO-AI-Control-Assessment-Procedure-v0.1.md`