What it means
AI risk treatment tracking follows mitigation, transfer, avoidance, acceptance, or pursuit decisions from approval through implementation and validation.
Why it matters
AI risks often span model, data, application, vendor, and human processes, so fragmented action lists can leave critical dependencies unfinished.
Practical focus
- Define the system boundary, accountable owner, approved purpose, material risks, and decision authority for AI Risk Treatment Tracking
- Record the selected treatment, accountable owner, milestones, dependencies, resources, target residual risk, validation method, due date, and escalation path.
- Collect and protect this evidence: treatment approvals, action evidence, status history, dependency changes, validation results, residual-risk decisions, and overdue escalations.
- Exercise this failure scenario as a planned test: a model control is marked complete while the required logging, contract change, and analyst training remain unfinished.
Common mistakes
- Using the label AI Risk Treatment Tracking without defining the exact systems, data, people, decisions, and time period in scope
- Treating a policy statement or tool demonstration as proof that AI Risk Treatment Tracking operates consistently in production
- Failing to retain treatment approvals, action evidence, status history, dependency changes, validation results, residual-risk decisions, and overdue escalations in a form that lets an independent reviewer reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when a model control is marked complete while the required logging, contract change, and analyst training remain unfinished.
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: