At a glance
A recommendation to keep access can save a reviewer time, but only if the reviewer understands what it represents. Similarity to colleagues, previous decisions and activity signals are useful context. None independently proves that access is still justified.
- Understand the inputs and their limitations.
- Challenge recommendations for sensitive or exceptional access.
- Record why the final decision is appropriate.
Begin with the business decision
A fictional finance owner at Mariner Distribution is reviewing payment-release access. One employee changed teams recently. A recommendation may reflect the wider peer group, but the owner knows that the person’s current task no longer includes releasing payments.
The relevant question is whether this person still needs this permission in the current scope. A review should not become a contest between the owner and a score. Give the owner enough context to explain the final decision.
Ask what information supports the suggestion
Check whether the recommendation uses current assignments, reliable activity data and the relevant access population. If the source data predates a reorganisation, the suggestion may describe an earlier situation. If activity is unavailable, avoid interpreting that absence as proof that access is unused.
| Signal | Question before relying on it |
|---|---|
| Peer access | Are the peers doing the same work, or sharing old exceptions? |
| Past approval | Has the job or permission changed since that decision? |
| Recent use | Was the activity authorised and relevant to the current role? |
| No recorded use | Is monitoring complete, and is the task seasonal? |
Where the interface provides an explanation, read it for important decisions. Where it does not, keep the decision grounded in the business facts that can be checked.
Keep human review meaningful
IGAX’s website describes recommendations and risk-based prioritisation for access certification. Establish which cases require explicit owner review, how disagreements are recorded and what happens when there is not enough information. Do this before introducing broad automated approval rules.
Use a pilot with deliberately difficult examples: a transferred employee, a rare but legitimate specialist permission and a commonly held obsolete permission. Review whether the recommendation directs attention usefully and whether reviewers can correct it.
If generated explanations are part of the configured experience, verify material claims rather than trusting fluent wording. NIST’s generative-AI guidance identifies confabulation and human reliance as relevant concerns.
NIST AI 600-1 provides guidance on generative-AI risks. It is relevant to generated explanations; it should not be treated as a description of the specific model used by IGAX. NIST AI 600-1: Generative Artificial Intelligence Profile.
Measure decision quality as well as speed
Count substantive corrections, unresolved cases and verified execution of removal decisions alongside reviewer completion. A very high acceptance rate may reflect good recommendations, but it may also reflect reviewers accepting suggestions without enough examination.
Discuss disagreements as opportunities to improve the data, role model or review instructions. Do not treat a reviewer’s override as an error merely because it differs from the suggestion.
References
Primary guidance used for the specific points cited above. The examples, templates and recommended working practices are AIdentX editorial guidance.
Sources reviewed September 2026.
Published by AIdentX Editorial. Illustrative scenarios are fictional and do not represent customer results.