Access Reviews Are Broken: Why Rubber Stamping Happens (and What Agentic UARs Change)

By Justin Woody, Co-Founder

Overview#

Access reviews are designed to enforce least privilege principles, yet at enterprise scale they frequently devolve into rubber stamping-where reviewers approve nearly all requests. This occurs not due to negligence, but because the process is structurally optimized for speed over quality decisions.

Root Causes of Rubber Stamping#

The problem stems from three interconnected factors:

• Excessive entitlements per reviewer create overwhelming volume
• Insufficient context regarding each entitlement’s purpose and necessity
• Perceived high costs to access removal-workflow disruption and escalations

Why Traditional Policy Fails#

Organizations frequently respond by intensifying policy demands: additional reminders, stricter deadlines, elevated approvals. While these measures improve completion metrics, they do not enhance decision quality. Without substantive evidence and intelligent prioritization, expediting workflows simply processes approvals faster without improving actual access governance.

Defining Agentic UARs#

“Agentic User Access Reviews” represent workflows where AI assembles evidence, generates recommendations (approve/deny/modify/time-bound), and may initiate bounded follow-up actions-all within defined policy and permission limits. Critically, this differs from autonomous revocation systems. The objective is achieving higher-quality decisions at scale while maintaining human accountability.

Four Signal Upgrades That Reduce Rubber Stamping#

1) Evidence Completeness#

Reviewers require comprehensive data: identity context, entitlement specifications, grant history, and temporal information. A reviewer can’t make a defensible call if the record is incomplete.

2) Usage and Resource Context Enrichment#

Access decisions benefit from visibility into last-access timestamps, privilege levels, resource sensitivity classifications, and peer comparisons. These details transform decisions from guesswork into evidence-based assessments.

3) Risk-Tier Prioritization#

Rather than reviewing everything equally, intelligent systems concentrate human attention on high-risk access while de-emphasizing policy-aligned, low-risk entitlements. This approach directs expertise where it matters most.

4) Traceable Rationale#

Every recommendation should be auditable, demonstrating which evidence and policies drove the decision. The approach emphasizes repeatable, reviewable decision records enabling future verification.

Executive Guardrails: Trust Model Precedes Automation#

Effective implementation requires controlled autonomy-improved decision inputs with bounded actions where policy permits, preserving human ownership of significant decisions.

Key guardrails include:

Permission boundaries: Systems should recommend without autonomous execution capability for sensitive changes.

Human accountability: Clear escalation paths and explicit separation between recommendation and approval.

Separation of duties: No single actor-human or system-should unilaterally modify high-risk access.

Auditability by design: Decision rationale must remain reproducible and evidence traceable.

Evaluation Framework: Five Critical Questions#

1. What data sources provide recommendations, and what is the data provenance?
2. What policy constraints are technically enforced versus merely configured?
3. How are audit outputs structured for third-party review and compliance?
4. Does the solution integrate cleanly with existing IAM/IGA infrastructure?
5. What rollback and exception-handling mechanisms exist for managing failures?

Conclusion#

Access review failures stem not from leadership indifference but from structural misalignment. Modern approaches should prioritize higher-quality decisions at scale through evidence enrichment and prioritization, operating within explicit permission boundaries while maintaining human oversight and accountability.