This is a ready-to-use monitoring review record. Replace every <<FILL: ...>> placeholder with your own specifics, set your document numbers and dates, and route it through your normal document control. A worked filled specimen follows the blank record. This form is the periodic operational record; the procedure that governs it is your model monitoring SOP.
Monitoring is what converts “it worked when we released it” into “it is working now.” An AI model can leave its validated state with no code change at all, because the data it sees has moved. This record captures each monitoring review, the signals against their release baselines, any trigger breach, and the response, so the ongoing validated state is evidenced rather than assumed. Monitoring without a recorded response is decoration.
Document control header
| Field | Entry |
|---|---|
| Document title | Production AI Model Monitoring and Drift Review Record |
| Document number | <<FILL: DOC-ID, e.g. QP-AI-020-F03>> |
| Version | <<FILL: version>> |
| Model / system | <<FILL: MODEL NAME + version, e.g. deviation-triage v2.1>> |
| Review period | <<FILL: from>> to <<FILL: to>> |
| Reviewer | <<FILL: name / role>> |
1. Purpose
To record the periodic monitoring review of <<FILL: MODEL NAME>>, comparing the live monitoring signals to their release baselines and thresholds, documenting any breach and the response, and confirming whether the model remains in its validated state.
2. When to complete
- On the defined cadence (for example monthly for automated signals, quarterly for the labeled-sample performance check), and
- Immediately whenever any automated trigger fires between scheduled reviews.
3. Field definitions
| Field | Format | Required | Who | When |
|---|---|---|---|---|
| Model and version | text | Yes | Reviewer | Each review |
| Signal | text | Yes | Reviewer | Each review |
| Release baseline | value | Yes | Reviewer | Each review |
| Alert threshold | value | Yes | Reviewer | Each review |
| Observed this period | value | Yes | Reviewer | Each review |
| Breach (Y/N) | Y/N | Yes | Reviewer | Each review |
| Response taken | text | Yes if breach | Reviewer | Each review |
| Disposition | enum | Yes | Reviewer + QA | Each review |
4. The monitoring signals (blank)
| Signal | Release baseline | Alert threshold | Observed this period | Breach (Y/N) | Note |
|---|---|---|---|---|---|
| Input-distribution drift vs training profile | <<FILL: baseline distance>> | <<FILL: limit>> | <<FILL>> | <<FILL>> | <<FILL>> |
| Reviewer override rate (rolling) | <<FILL: e.g. 8%>> | <<FILL: e.g. 15%>> | <<FILL>> | <<FILL>> | <<FILL>> |
| Output / class mix | <<FILL: baseline mix>> | <<FILL: shift limit>> | <<FILL>> | <<FILL>> | <<FILL>> |
| Confidence distribution | <<FILL: baseline>> | <<FILL: shift limit>> | <<FILL>> | <<FILL>> | <<FILL>> |
| Operational health (latency, error rate) | <<FILL>> | <<FILL>> | <<FILL>> | <<FILL>> | <<FILL>> |
| Labeled-sample performance (cadence) | <<FILL: spec, e.g. recall >= 0.90>> | <<FILL: below spec>> | <<FILL>> | <<FILL>> | <<FILL>> |
5. Trigger response (complete if any breach)
- Trigger that fired:
<<FILL>> - Labeled sample pulled and performance recomputed:
<<FILL: result vs spec>> - Action taken:
<<FILL: keep serving / pause automated decisions and route to fallback / investigate>> - Investigation or deviation reference:
<<FILL: number or N/A>> - If paused, resumption condition:
<<FILL: fix, revalidate through the gated pipeline, confirm on locked test set, QA release>>
6. Disposition
- Model remains in its validated state; no action beyond routine.
- Drift observed within spec; monitoring tightened; note the cause.
- Performance below spec; automated decisions paused, work routed to fallback, investigation opened.
- Reviewer:
<<FILL: name, signature, date>> - QA review:
<<FILL: name, signature, date>>
7. Acceptance criteria
- Every defined signal is recorded against its baseline and threshold for the period.
- Every breach has a recorded response, not just an observation.
- Any performance drop below spec results in a pause to the defined fallback and an investigation.
- The record is complete, signed, and QA-reviewed, and retained per the retention schedule for not less than
<<FILL: retention period>>.
References
21 CFR Part 11 and EU GMP Annex 11 for the monitoring evidence as a controlled GxP record. ICH Q9(R1) for the risk basis of the thresholds and cadence. The model validation report and release baseline that define the specification these signals are compared against.
Confirm the current version of each reference before issue.
Revision history
| Version | Date | Author | Summary of change |
|---|---|---|---|
<<FILL: 1.0>> | <<FILL: date>> | <<FILL: author>> | Initial issue. |
Approvals
| Role | Name | Signature | Date |
|---|---|---|---|
| Author | <<FILL>> | ||
| Approver (QA) | <<FILL>> |
Filled specimen
The following shows a completed monthly review for an example deviation-triage model, so you can see the level of detail expected. The values are illustrative; replace them with your own.
Model: deviation-triage v2.1. Review period: 01 June 2026 to 30 June 2026. Reviewer: A. Haddad.
| Signal | Release baseline | Alert threshold | Observed this period | Breach (Y/N) | Note |
|---|---|---|---|---|---|
| Input-distribution drift | 0.04 distance | 0.15 | 0.06 | N | Slight rise, within limit |
| Reviewer override rate (30-day) | 8% | 15% | 17% | Y | Rose over the month |
| Output / class mix (critical tier share) | 12% | +/- 5 pts | 13% | N | Stable |
| Confidence distribution | baseline | shift limit | stable | N | No shift |
| Operational health | <200 ms, <1% error | as left | 140 ms, 0.3% | N | Healthy |
| Labeled-sample performance | recall >= 0.90 | below 0.90 | 0.86 (triggered) | Y | Recomputed on the override breach |
Trigger response: the override-rate breach triggered an immediate labeled-sample review, which recomputed recall at 0.86, below the 0.90 spec. Automated tiering was paused and all new deviations were routed to full human triage. Investigation DEV-AI-021 found a new deviation category (a recently added product line) underrepresented in training. The model is being retrained through the validated pipeline on data including the new category; it will resume only after it meets 0.90 recall on the locked test set and QA releases it.
Disposition: performance below spec; paused to fallback; investigation open. Reviewer A. Haddad, signed 02 July 2026. QA R. Gomez, signed 02 July 2026.
In this example the cheap automated signal (override rate) was the leading indicator: reviewers were correcting the model more often before the formal performance metric would have been recomputed on its quarterly cadence. That is exactly why the override rate is monitored continuously, and why a breach pulls the labeled-sample check forward rather than waiting.
Common inspection findings this record prevents
- No evidence the model was monitored after release, so the validated state is an assertion from go-live day.
- A monitoring alert fired but no response was recorded, so the alert was decoration.
- Performance dropped below spec and the model kept making automated decisions with no pause and no investigation.
- Thresholds were invented after something looked wrong rather than set in advance against the release baseline.
How to adapt this record
- Set your model, thresholds, and cadence from your validation report and risk assessment, not generic defaults.
- Add or remove signals to match what your monitoring stack actually produces.
- Point the investigation reference to your real deviation procedure.
- For a generative or LLM system, add an evaluation-harness score row and a hallucination or unsupported-claim rate row.
- File each completed record with the system’s periodic review evidence.