An AI incident review should establish whether monitoring data was retained and accessible and whether monitoring covered the platform, infrastructure, and workload layers. The central lesson is to preserve usable evidence for timely detection, response, and the post-incident analysis that follows.
How to check the evidence
The cited reliability guidance supports three practical checks:
| Check | Review question |
|---|---|
| Retention and access | Was the relevant monitoring data retained and accessible for timely detection, response, and post-incident analysis? |
| Layer coverage | Did monitoring provide visibility across the platform, infrastructure, and workload layers? |
| Operational use | How did the available data support detection and response, and what could it establish during the later review? |
A clear review record should identify which layers were monitored, whether the relevant data remained available, and how that evidence informed the response and subsequent analysis. It should also state any unresolved evidence gaps rather than treating coverage across the three layers as proof that the incident was fully addressed.
What teams must still confirm
The operational points cited here do not specify retention periods, access roles, alert thresholds, escalation procedures, notification duties, or reporting requirements. Teams must separately confirm any contract, internal-policy, or legal obligations that apply to their systems and incidents.
The review must also establish whether the actual monitoring and retained data covered the systems and layers involved in the specific incident. Visibility across all three layers does not, by itself, demonstrate that detection or response was timely.
The reusable lesson is straightforward: retain relevant monitoring data, keep it accessible, and examine coverage across platform, infrastructure, and workload layers. This provides an evidence base for incident response and later analysis without guaranteeing a particular outcome.