# RAIBR03-BP01 Identify the likelihood of each potential harm
Establish a risk rating methodology that considers the likelihood of the event occurring. The risk likelihood indicates the probability of a harmful event occurring when the system is deployed for the use case.
**Level of risk exposed if this best practice is not established:** High
## Implementation considerations
1. Create a standardized likelihood scale with clear definitions. For example, establish ranges from *almost certain* (95%\+ probability) to *highly unlikely* (less than 5% probability). Include specific frequency ranges for each category to maintain consistent evaluation.
1. Document likelihood assessments with supporting evidence. For example, consider a content moderation system where historical data shows harmful content detection failures occur in 15% of edge cases, placing this risk in the *unlikely* category. Include rationale for each assessment.
## Resources
**Related documents:**
+ [Learn how to assess the risk of AI systems](https://aws.amazon.com/blogs/machine-learning/learn-how-to-assess-risk-of-ai-systems/)
+ [NIST Risk Management Framework](https://csrc.nist.gov/projects/risk-management/about-rmf)
+ [Responsible AI in the generative era](https://www.amazon.science/blog/responsible-ai-in-the-generative-era)
+ [ISO/IEC 42001:2023](https://www.iso.org/standard/42001) A.5.2 AI system impact assessment process
## See also
* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for AWS Well-Architected. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query wellarchitected` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).