An enterprise has deployed multiple AI systems and is building an AI governance framework aligned with Microsoft's Responsible AI principles. The security team needs to define the process for reporting AI safety incidents—cases where an AI system produces harmful or unexpected outputs that affect users. Which element of the governance framework should specify the incident classification, escalation path, and post-incident review process for AI safety incidents?

A dedicated AI Incident Response Plan that supplements the existing incident response plan with AI-specific incident categories, severity classifications, escalation to the AI governance committee, and mandatory post-incident Responsible AI review is correct because AI systems introduce unique incident types that standard IT security incident response plans are not designed to address—such as safety failures (a model producing harmful content), fairness incidents (biased outputs affecting a group), model failures (degraded accuracy in production), and security breaches (model extraction or data poisoning). Defining AI-specific incident categories, severity levels, escalation paths that include the AI governance committee and Responsible AI team, and mandatory post-incident review processes ensures that AI incidents are handled with appropriate expertise and governance. The organization's existing IT security incident response plan (unchanged) is wrong because it lacks the AI-specific incident categories, severity criteria, and Responsible AI review components needed; supplementing it with AI-specific content is the correct approach. The model deployment approval checklist is wrong because it is a pre-deployment governance artifact, not an incident response procedure. Azure Monitor alert rules and notification action groups are wrong because they are technical monitoring tools that detect incidents; they are not the governance process or framework for classifying and responding to them.