> ## 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.

# Risk Management

> AI risk management within the AIGO Framework.

# Risk Management

Risk management is a core component of the AIGO Framework and provides a structured approach for identifying, assessing, treating, monitoring, and reviewing AI-related risks.

## Purpose

AIGO risk management helps organizations understand and manage risks associated with AI systems throughout their lifecycle.

Risk management should support governance decisions rather than operate as an isolated compliance activity.

## Risk lifecycle

AIGO connects risk management with lifecycle activities including:

* risk identification
* risk analysis and assessment
* risk treatment
* control selection
* acceptance and approval
* monitoring
* review
* change management
* retirement

## Relationship to controls

Risk assessment provides an important basis for determining which governance controls are needed.

The relationship can be understood as:

**risk → treatment → controls → evidence → assurance**

Controls should therefore be considered in relation to the risks they are intended to address.

## Ongoing review

AI risks can change as systems, use cases, data, operating environments, and organizational conditions change.

Risk management should therefore be revisited when material changes occur and as monitoring or assurance activities identify new information.

## Machine-readable support

AIGO provides a machine-readable risk schema to support structured risk records and integration with governance workflows.

The canonical schema is:

`schemas/02-risk/02-AIGO-Risk-Schema-v0.1.json`

## Source

The canonical risk-management document is maintained in:

`framework/06-risk/AIGO-AI-Risk-Management-v0.1.md`

The GitHub repository is the authoritative source for the complete v0.1 risk-management model.