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Model Risk Management: Quantifying and Controlling LLM Risk
A practical framework for identifying, scoring, and mitigating risks in LLM-powered products.
Why LLM Risk Is Different
LLMs can generate plausible but incorrect outputs, creating hidden risk.
Traditional software risk models do not capture probabilistic failure modes.
Risk Taxonomy
Classify risks into safety, compliance, privacy, and business impact.
Clear taxonomy improves prioritization and accountability.
Scoring and Thresholds
Assign risk scores to workflows based on impact and likelihood.
Define thresholds that trigger additional review or guardrails.
Mitigation Playbooks
Create standard mitigations: human review, retrieval grounding, or stricter policies.
Reuse playbooks to reduce response time when new risks appear.
Continuous Risk Monitoring
Risk profiles drift as models and data change.
Monitor incidents and near-misses to update risk models.
Governance and Ownership
Assign clear owners for each risk category and mitigation plan.
Establish a risk review board with cross-functional representation.
Define escalation paths for high-severity incidents.
Use regular audits to validate that mitigations are implemented.
Integrate risk checks into product launch reviews.
Track risk acceptance decisions to keep accountability visible.
Maintain a registry of known model limitations for stakeholders.
Align risk reporting with compliance and legal requirements.
Risk Metrics and Reporting
Create dashboards that track incidents, severity, and remediation time.
Set thresholds that automatically trigger mitigation workflows.
Measure user impact when risks materialize to guide prioritization.
Include risk metrics in quarterly business reviews.
Use leading indicators like policy violations to predict future incidents.
Report false positives and false negatives to tune guardrails.
Track model changes that affect risk exposure.
Keep a risk backlog so unresolved items are visible.
Segment risk metrics by product line to pinpoint hotspots.
Include near-miss reporting so early warning signals are captured.
Review remediation SLAs to ensure critical issues are resolved fast.
Archive risk reports so trends can be analyzed over time.
Run tabletop exercises to validate risk response readiness.
Share risk summaries with leadership to keep alignment strong.
FAQ: Model Risk
Do I need a formal risk program? Yes, for any production AI system.
What is the fastest win? Identify the top 3 high-impact workflows and add safeguards.
Who owns risk? Product, engineering, and compliance should share ownership.
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