Manual license reviews matter when applications have weak or inconsistent API coverage, or when usage data is incomplete. In those cases, teams need a fallback process to map users to licenses, identify unused or underused entitlements, and recover spend. This is especially useful when governance depends on accurate visibility before renewal or cleanup decisions.
Why This Matters for Security Teams
Manual license reviews become important when SaaS governance depends on evidence that the platform cannot reliably provide. In practice, that usually means missing API coverage, inconsistent telemetry, stale role mappings, or fragmented tenant data. Without a fallback review process, teams can miss unused entitlements, keep paying for dormant access, or approve renewals based on assumptions instead of usage. That creates both cost leakage and governance risk, especially when access reviews are tied to compliance or procurement decisions.
This is not a niche administrative task. The same visibility gaps that complicate renewals can also hide risky access paths, which is why NIST Cybersecurity Framework 2.0 treats asset, identity, and continuous monitoring activities as part of core governance, not optional cleanup. NHIMG research shows the scale of the problem: in The State of Non-Human Identity Security, only 1.5 out of 10 organisations were highly confident in securing NHIs, and 85% lacked full visibility into third-party vendors connected via OAuth apps. The same visibility discipline applies to SaaS license oversight, even when the target is a human user rather than an NHI. In practice, many security teams discover license sprawl only after renewal overspend or access disputes have already been approved.
How It Works in Practice
A manual review should be treated as a controlled exception process, not as the primary system of record. The goal is to reconstruct who is using what, validate that against procurement or identity data, and then decide whether the license is justified. That usually involves exports from the SaaS admin console, identity provider, billing system, and any available activity logs. Where APIs are incomplete, the review becomes a reconciliation exercise across imperfect sources.
Practitioners typically use a simple workflow:
- Map active users to assigned license tiers and confirm whether each entitlement is still needed.
- Check recent activity, last login, and feature usage to distinguish active, dormant, and misassigned accounts.
- Compare assigned licenses against business role, department, or cost center to surface mismatches.
- Flag orphaned accounts, duplicate entitlements, and accounts provisioned outside standard workflows.
- Escalate exceptions that cannot be resolved through data alone, especially where access is tied to regulated workflows.
This approach aligns with Top 10 NHI Issues because the underlying governance pattern is the same: when system telemetry is incomplete, human review becomes the control that catches what automation misses. It also fits the direction of the NIST Cybersecurity Framework 2.0, which emphasises governance, monitoring, and ongoing risk treatment rather than one-time attestation. Manual review works best when paired with a fixed cadence before renewal, a documented exception threshold, and a clear owner who can approve removals or reassignments. These controls tend to break down in federated SaaS estates with multiple admins, overlapping tenants, and poor activity logging because no single source can reliably prove entitlement use.
Common Variations and Edge Cases
Tighter manual review often increases operational overhead, requiring organisations to balance control quality against the time and coordination needed to complete the review. That tradeoff becomes especially visible in large SaaS portfolios, where some apps provide rich usage reporting while others expose little more than seat counts. Current guidance suggests using manual reviews selectively where the data gap is material, rather than forcing every application into the same process.
Edge cases usually appear in three places. First, shared accounts and service accounts may distort user counts, so the review must separate human seats from operational access. Second, contractors and temporary staff can create legitimate churn that looks like waste unless the business context is included. Third, some platforms report activity but not feature-level consumption, which means a license may be technically assigned yet functionally underused. In those cases, the question is not only whether the user logged in, but whether the entitlement matches the actual business need.
For audit-ready cleanup, teams should preserve evidence of the manual decision path and tie it back to policy. The Ultimate Guide to NHIs — Regulatory and Audit Perspectives is useful here because it reflects the same principle: when automation cannot prove the state of an identity or entitlement, documented review becomes the defensible control. Manual license review is most effective when it is time-bound, exception-driven, and paired with a plan to improve telemetry so that future cycles rely less on spreadsheets and more on trustworthy system data.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM | Manual reviews support risk-based governance when SaaS telemetry is incomplete. |
| OWASP Non-Human Identity Top 10 | NHI-03 | License reviews often reveal stale or unmanaged access and entitlements. |
| CSA MAESTRO | IAM-02 | Governance needs continuous entitlement validation across cloud services. |
| NIST AI RMF | GOVERN | Manual review is a governance fallback when automation cannot assure entitlement accuracy. |
Document ownership, review criteria, and exception handling for every manual license assessment.
