AI risk management
Assess what could go wrong in an AI workflow, who is responsible for each risk, and how controls will be checked.
View readiness assessmentsReview the specific workflow
Review the information the system receives, the outputs people rely on, and the actions its tools can take. Consider incorrect results, inappropriate data disclosure, manipulated inputs, and actions outside the intended scope.
Connect risks to controls
For each identified risk, document the responsible owner, the control, how it will be tested, and the evidence a reviewer needs. Record unresolved issues and the decisions that require approval before a pilot proceeds.
Our work on governance, agent security, and data access addresses these requirements.
Review results as the system changes
Use pilot findings, exceptions, and changes in data or tools to revisit the assessment. Use audit records to examine what happened and whether the intended controls applied.