They should combine identity relationships with data classification so reviews focus on the access paths that matter most. The practical goal is to identify direct and inherited routes into sensitive datasets, then remove or narrow those paths before they become audit findings or incident conditions.
Why This Matters for Security Teams
Identity graphs are useful because they expose indirect pathways that traditional entitlement reviews miss: group nesting, delegated admin, service account chaining, and inherited access into sensitive data stores. The risk is not just who can reach a dataset directly, but who can reach it through one or more intermediary identities. That is why access review quality must be judged against data exposure, not just role counts. Current guidance from the OWASP Non-Human Identity Top 10 and NHIMG research such as Ultimate Guide to NHIs points to the same operational gap: many organisations still do not have enough visibility to understand where sensitive paths actually exist.
That matters because indirect access often survives longer than direct access. A seemingly harmless role change, application permission, or inherited group membership can keep a high-value dataset reachable even after the original business need has passed. In practice, many security teams encounter data exposure only after an audit, incident, or privilege review uncovers the path, rather than through intentional design.
How It Works in Practice
The practical approach is to combine graph-based identity analysis with data classification, then prioritise the paths that terminate in sensitive systems. Start by mapping direct entitlements, then expand to inherited relationships such as nested groups, federation trust, workload-to-workload delegation, and service account impersonation. Once those paths are visible, rank them by data criticality, not by how many identities are involved.
A useful operating model is to review three layers together:
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Identity layer: users, service accounts, API keys, and privileged roles that can transitively reach the target.
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Relationship layer: group nesting, app-to-app trust, token exchange, and delegated administration paths.
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Data layer: classification labels, sensitivity tags, and ownership for the datasets being reached.
That is where 52 NHI Breaches Analysis is especially relevant: the recurring pattern is not isolated over-permissioning, but accumulated access paths that were never re-evaluated against the data they could reach. Pairing this with the Guide to the Secret Sprawl Challenge helps teams see that credential sprawl and graph sprawl reinforce each other.
Remediation usually means removing inherited access, splitting broad roles into narrower ones, converting standing access into just-in-time approval, and forcing higher-friction controls on the most sensitive path edges. For workload and automation-heavy environments, the current direction of travel is toward tighter non-human identity controls, better secret handling, and explicit approval for graph-discovered routes that land in regulated data. These controls tend to break down in hybrid environments with legacy directory sync and shadow service accounts because the graph is incomplete and the true access path cannot be proven end to end.
Common Variations and Edge Cases
Tighter graph-based access control often increases review overhead, requiring organisations to balance reduced exposure against the cost of maintaining accurate identity and data metadata. That tradeoff is real, especially when business teams rely on shared accounts, inherited application roles, or fast-moving CI/CD pipelines.
Best practice is evolving in a few areas. For example, there is no universal standard for how much indirect access should trigger automatic remediation versus manual review. Some teams use a strict threshold for highly sensitive datasets, while others accept indirect access if it is time-bound, fully logged, and tied to a named owner. The right choice depends on classification, regulatory scope, and the maturity of the identity governance program.
For implementation detail, the Anthropic report on AI-orchestrated cyber espionage is a reminder that adversaries now exploit chained access and automation quickly once they find a weak path. That is why indirect access reviews should also consider machine identities, not just employees. A mature program treats every sensitive path as a living relationship, then continuously validates whether the business need still exists and whether the access route is still justified.
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 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 |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Indirect access often comes from overprivileged NHIs and stale relationship paths. |
| NIST CSF 2.0 | PR.AC-4 | Access reviews must account for indirect paths, not only direct user entitlements. |
| NIST AI RMF | AI RMF supports governance of graph analytics used to make access decisions. |
Inventory NHI entitlements, then remove inherited routes to sensitive data that lack explicit business need.
Related resources from NHI Mgmt Group
- How should IAM teams govern conversational access review tools for identity data?
- How should security teams handle sensitive data when identity access and data discovery are disconnected?
- How should security teams reduce stale identity data in access reviews?
- How should healthcare teams reduce plaintext exposure of sensitive data?
