AI Policy Drift Early Warning Calculator
Quantify how current violation density, severity, and drift telemetry compare with historical guardrails so governance teams can escalate before risk brews into an incident backlog.
Risk analytics aid. Pair the output with qualitative policy reviews and human adjudication before rolling back models or shipping hotfixes.
Examples
- 42 violations across 125,000 monitored prompts, baseline 0.018%, severity 1.3, drift 1.15 ⇒ Observed rate 0.03%, index 2.79 (critical risk).
- 18 violations across 90,000 interactions, baseline 0.02%, optional fields blank ⇒ Observed rate 0.02%, index 1.00 (stable range).
FAQ
How long should the monitored window be?
Most teams use a rolling 7- or 14-day window to catch drift early while still capturing enough volume for a stable rate. Align the window with your incident review cadence.
What feeds the embedding drift factor?
Pull divergence metrics from your vector store or embedding service—cosine distance between recent and baseline embeddings, for example. Normalise the metric so 1.0 reflects no drift and values above 1.0 amplify the index.
Can I change the alert thresholds?
Yes. Adjust the index interpretation downstream in your governance dashboard if your risk tolerance differs. The calculator outputs the raw index so you can map it to custom runbooks.
Additional Information
- Result unit: dimensionless index where 1.0 equals historical risk.
- Severity and drift multipliers default to 1.0 when omitted so the index reflects pure rate-of-change.
- Status thresholds flag caution above 1.2 and critical risk above 1.5 to mirror many AI governance playbooks.