Topic starter
16/01/2026 4:39 pm
TL;DR: Access approvals in large organizations often drift into an “approve all” habit because reviewers lack context and time, but AI agents can evaluate peer behavior, usage patterns, and risk signals to make decisions that are consistent, explainable, and auditable, according to Fabrix Security. That shifts access governance from fatigue-driven approval to evidence-based least privilege.
Editorial analysis by NHI Mgmt Group, based on content published by Fabrix Security: “Ending “Approve All”: AI Agents Reduce Human Bias”.
Key questions
Q: How should security teams reduce approve-all bias in access reviews?
A: Security teams should make review decisions evidence-led instead of purely judgment-led.
Q: Why do access reviews often fail in large organisations?
A: They fail when managers are asked to review too many entitlements without enough context to make informed decisions.
Q: What are the signs that access review decisions are becoming unreliable?
A: Warning signs include near-universal approvals, frequent reviewer overrides without clear justification, low use of contextual evidence, and entitlement decisions that vary widely between teams.
Practitioner guidance
- Rebuild review criteria around evidence Use peer usage, entitlement history, and role context to decide whether a requested access level is still justified.
- Require explainable recommendations Make every AI-assisted access decision return a rationale, risk score, and logged justification so auditors and policy owners can challenge the outcome.
- Keep humans on exception handling Use AI agents to triage routine requests, but reserve final authority for policy owners on high-risk entitlements, unusual context, and business exceptions.
Bottom line: Access review fatigue can turn least-privilege controls into approve-all routines that do little to stop privilege drift.
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06/10/2026 9:12 pm
Access-review fatigue is now a governance control failure, not a process inconvenience. When reviewers approve quickly to keep work moving, recertification stops functioning as a meaningful check on privilege. The result is privilege drift, inconsistent decision quality, and weak audit evidence. The practitioner takeaway is that review design must be judged by decision quality, not by completion rates.
A few things that frame the scale:
- Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously.
A question worth separating out:
Q: How should IAM teams balance AI-assisted approvals with accountability?
A: IAM teams should use AI agents as a first-pass decision aid, not as an accountability substitute. Policy owners still need final authority over exceptions, high-risk entitlements, and unclear cases. Every recommendation should carry an explanation and audit trail so the decision can be reviewed, challenged, and improved over time.
👉 Read our full editorial: AI agents can replace approve-all access reviews with evidence