Key Takeaway
A dedicated AI risk register surfaces risks that generic enterprise risk registers miss -- training data bias, model drift, prompt injection, and vendor model deprecation. This template provides pre-populated risk categories with AI-calibrated scoring rubrics, mitigation tracking workflows, and integration points with your existing enterprise risk management process.
Why Generic Risk Registers Fail for AI
Enterprise risk registers are designed around categories like financial risk, operational risk, compliance risk, and reputational risk. AI risks cut across all of these categories in ways that generic registers do not accommodate. Model drift is an operational risk with compliance implications. Training data bias is a technical risk with legal, reputational, and financial consequences. Prompt injection is a security risk that can manifest as a data privacy violation. When AI risks are forced into generic categories, they get split across multiple register entries, lose their AI-specific context, and become difficult to assess because the scoring rubric was not calibrated for probabilistic system failures.
Risk Scoring Rubric
The scoring rubric uses a five-point scale for both likelihood and impact, with descriptors calibrated to AI system behavior rather than generic business risk. The likelihood scale accounts for the probabilistic nature of AI failures (model degradation is not a question of if but when), and the impact scale considers both immediate effects and downstream consequences.
| Score | Likelihood | Impact |
|---|---|---|
| 1 - Rare | Has not occurred in similar systems; requires highly unusual conditions; probability < 5% annually | Negligible impact; auto-recoverable; no user-visible effect |
| 2 - Unlikely | Has occurred in similar systems but is infrequent; probability 5-15% annually | Minor degradation; affects small user segment; no regulatory implication |
| 3 - Possible | Expected to occur at some point; probability 15-40% annually; common in the industry | Moderate impact; noticeable quality degradation; may trigger internal investigation |
| 4 - Likely | Expected to occur within the next year; probability 40-70% annually; multiple industry precedents | Significant impact; affects substantial user base; potential regulatory inquiry; remediation cost material |
| 5 - Almost Certain | Expected to occur within months; probability > 70% annually; inherent to the system design | Severe impact; broad user harm; regulatory enforcement action; material financial or reputational damage |
Pre-Populated Risk Categories
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