AI assuranceAI Audit Finding Follow-Up
AI audit finding follow-up verifies that corrective actions address the root cause, operate as intended, and reduce the stated risk before a finding is closed.
AI assuranceAI Audit Scope and Criteria
AI audit scope defines the systems, life-cycle stages, data, decisions, vendors, locations, and stakeholders included in an engagement, while audit criteria define the requirements used to judge them.
Responsible AIAI Bias and Fairness Assessment
AI bias and fairness assessment evaluates how data, labels, objectives, design choices, and deployment conditions can create uneven errors or harms across groups.
AI change assuranceAI Change Evidence Package
An AI change evidence package assembles the artifacts needed to understand, approve, reproduce, and later audit a model, data, prompt, configuration, or integration change.
AI assuranceAI Control Design Effectiveness
AI control design effectiveness asks whether a control, as documented and configured, is capable of preventing, detecting, or correcting the risk it was selected to address.
AI controlsAI Control Monitoring
AI control monitoring observes whether preventive, detective, and corrective controls remain active, correctly configured, sufficiently covered, and effective as systems change.