AI risk management examines how a system may fail and what those failures mean for its users and the business. The assessment should reflect the workflow, access permissions and consequences of an incorrect output.
Identify failure modes such as unsupported answers, inappropriate data disclosure, biased decisions or unauthorized actions. Evaluate their likelihood and impact in the actual use case. Distinguish model limitations from failures caused by poor data, integration or unclear operating procedures.
Choose controls that address the identified risks, including evaluation, permission limits, output validation and meaningful human review. Test whether the controls work with challenging inputs. NIST's AI RMF offers a voluntary reference for structuring this work; it is not an automatic certification.
Assign owners and thresholds for intervention after launch. Preserve incident evidence and review changes to data, prompts or models. A risk register is useful only when its findings influence deployment and operating decisions.
NIST voluntary AI Risk Management Framework
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
Related Articles
Reading adjacent decision areas keeps the topic from becoming an isolated note.