Topic starter
25/06/2026 3:03 pm
TL;DR: Machine learning is being used to reduce reviewer fatigue in access certifications by flagging unusual entitlements and grouping low-risk items, according to SecurEnds. Access review quality now depends on whether reviewers can still prioritise exceptions and make defensible decisions at scale.
Editorial analysis by NHI Mgmt Group, based on content published by SecurEnds: “AI in User Access Reviews: Can Machine Learning Reduce Reviewer Fatigue”.
Key questions
Q: Why does reviewer fatigue create governance risk in access certification?
A: Reviewer fatigue matters because exhausted reviewers are more likely to approve broadly, miss stale access, and overlook privileges that no longer match the user’s role.
Q: How do organisations know whether access reviews are working?
A: Access reviews are working when they lead to timely removals, reduced exception volume, and role definitions that stop accumulating unused rights.
Q: What breaks when access certification is handled with ad hoc manual reviews?
A: Ad hoc manual reviews usually break at scale.
Practitioner guidance
- Define high-risk review thresholds Separate entitlements that deserve human scrutiny from routine items that can be grouped or deprioritised.
- Require explanation for every recommendation Make the AI rationale visible to reviewers and auditors so each flag, grouping, and suggested approval can be traced back to a clear access signal.
- Measure review quality, not just completion Track how often reviewers approve everything, how many exceptions are challenged, and how much stale or excessive access is uncovered after certification.
Bottom line: AI-assisted access review is aimed at reducing reviewer fatigue, but the real governance problem is whether certification remains meaningful under load.
Glossary terms: Reviewer Fatigue, Access Certification, Risk-Based Review, Explainable Recommendation
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This topic was modified 18 hours ago by NHI Mgmt Group
25/06/2026 4:20 pm
Manual certification has reached a scale limit: access review processes were designed for human review of manageable entitlement sets, not for repeated approval cycles where managers must decide quickly with incomplete context. When the queue grows, the control degrades into compliance theatre rather than governance. The implication is that access certification quality must be treated as a capacity problem, not only a process problem.
A few things that frame the scale:
- Over 70% of organisations lack automated access risk analysis, user access reviews and provisioning and deprovisioning, according to Pathlock's 2025 Digital Transformation and Access Risk Report.
A question worth separating out:
Q: Should AI replace human reviewers in access certification?
A: No. AI should support reviewers by sorting noise, surfacing anomalies, and recommending where to look first, but final certification still needs human accountability. Access review is a governance decision, not a pure classification problem. The best model is machine assistance with named human ownership of the outcome.
👉 Read our full editorial: AI access reviews need exception handling, not blanket certification
This post was modified 18 hours ago by NHI Mgmt Group