> For the complete documentation index, see [llms.txt](https://documentations.seclea.com/nist-ai-risk-management-framework/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentations.seclea.com/nist-ai-risk-management-framework/map-1-ai-application-context/map-1.5-organisations-risk-tolerance.md).

# MAP 1.5 - Organisations Risk Tolerance

NIST AI RMF (in the playbook companion) states:

> ### MAP 1.5 
>
> Organizational risk tolerances are determined. 
>
> <details>
>
> <summary><strong>About</strong></summary>
>
> \
> Risk tolerance reflects the level and type of risk the organization will accept while conducting its mission and carrying out its strategy.
>
> Deployment should not be pre-determined. Rather, it should result from a clearly defined process based on organizational risk tolerances.
>
> Go/no-go decisions should be incorporated throughout the AI system’s lifecycle. For systems deemed “higher risk,” such decisions should include approval from relevant technical or risk-focused executives.
>
> Go/no-go decisions related to AI system risks should take stakeholder feedback into account, but remain independent from stakeholders’ vested financial or reputational interests
>
> </details>
>
> <details>
>
> <summary><strong>Actions</strong></summary>
>
> * Establish risk tolerance levels for AI systems and allocate the appropriate oversight resources to each level.
> * Identify maximum allowable risk thresholds above which the system will not be deployed, or will need to be prematurely decommissioned, within the contextual or application setting.
> * Attempts to use a system for “off-label” purposes should be approached with caution, especially in settings that organizations have deemed as high-risk. Document decisions, risk-related trade-offs, and system limitations.
>
> </details>
>
> <details>
>
> <summary><strong>Transparency and Documentation</strong></summary>
>
> \
> **Organizations can document the following:**
>
> * What justifications, if any, has the entity provided for the assumptions, boundaries, and limitations of the AI system?
> * How has the entity identified and mitigated potential impacts of bias in the data, including inequitable or discriminatory outcomes?
> * To what extent are the established procedures effective in mitigating bias, inequity, and other concerns resulting from the system?
>
> </details>