How should security teams inventory identities across cloud, SaaS, and AI systems?

They should use one inventory model that includes human users, NHIs, and AI-linked identities, then map each identity to an owner, purpose, and access boundary. Separate spreadsheets or point tools leave gaps between discovery and governance, which is where privilege drift and credential abuse hide.

Why This Matters for Security Teams

Identity inventory is no longer just an access review exercise. Cloud workloads, SaaS integrations, service accounts, API keys, and AI-linked agents all create identities that can authenticate, inherit privilege, and move data. When those identities are scattered across separate tools, teams lose the ability to answer basic questions about ownership, purpose, and blast radius. That gap is where secret sprawl, dormant access, and unauthorized automation tend to accumulate.

Current guidance aligns identity inventory with governance, not just discovery. The NIST Cybersecurity Framework 2.0 emphasises asset visibility and access control outcomes, while NHIMG research has repeatedly shown how identity failures surface after compromise rather than during routine review. The Snowflake breach and Salesloft OAuth token breach are reminders that identity inventory must include tokens, integrations, and delegated access paths, not just named users. In the 2024 Non-Human Identity Security Report, 35.6% of organisations said consistent access across hybrid and multi-cloud environments was their top NHI challenge, which shows how quickly inventory gaps become governance gaps.

In practice, many security teams encounter privilege drift only after an integration has already been over-permissioned or an AI workflow has already chained access across systems.

How It Works in Practice

The most reliable model is a single inventory that treats each identity as a governed record, regardless of whether it belongs to a person, workload, SaaS connector, or AI agent. For each record, teams should capture at minimum: owner, system of record, business purpose, authentication method, access boundary, secret or token type, last-used timestamp, and review cadence. That lets the inventory answer operational questions such as who can approve a change, what data the identity can reach, and whether the identity is still needed.

For cloud and SaaS, discovery usually starts with IAM exports, SCIM directories, API gateway logs, vault records, and SaaS admin consoles. For AI systems, the inventory must also include tool-using agents, model-runtime service principals, MCP-style integrations, and any identity used to retrieve context or act on behalf of a user. NHI Management Group recommends tying the inventory back to the access boundary rather than the platform label, because the same secret or token may be reused across multiple environments. That is where standardisation matters: identities need to be catalogued consistently enough to support review, rotation, and revocation.

• Assign one accountable owner for each identity, even if technical administration is shared.
• Record whether the identity is human, NHI, or AI-linked, then map it to the specific workload or business process.
• Classify secrets by lifetime and rotation model, not just by vault location.
• Reconcile inventory entries against logs, vaults, and admin APIs on a fixed cadence.
• Flag identities with broad delegation, unused access, or no documented purpose for immediate review.

This approach is reinforced by NHIMG research on Azure Key Vault privilege escalation exposure and BeyondTrust API key breach, both of which show that inventory value depends on connecting identity data to privilege and secret handling. These controls tend to break down when teams cannot reconcile SaaS-issued tokens and ephemeral cloud credentials with a live owner and review process.

Common Variations and Edge Cases

Tighter inventory control often increases operational overhead, requiring organisations to balance complete visibility against the friction of maintaining accurate records. That tradeoff becomes sharper in federated SaaS estates, multi-cloud environments, and AI pipelines where identities are created dynamically and may expire quickly. There is no universal standard for how to represent every AI-linked identity yet, so current guidance suggests documenting the runtime service identity, the controlling application, and the specific action scope rather than trying to force every agent into a human-style user model.

One common exception is ephemeral infrastructure, where short-lived workloads may appear and disappear faster than a manual inventory can keep up. In those cases, best practice is evolving toward event-driven discovery and policy enforcement at creation time, rather than relying only on periodic audits. Another edge case is shared admin tooling, where several teams may touch the same platform account; those accounts still need one accountable owner and a clear purpose, even if operational use is distributed.

Security teams should also separate inventory from authorization. A complete list of identities does not mean all identities should be reviewed the same way. High-risk records, such as third-party OAuth grants, automation tokens, and agent tool accounts, need shorter review cycles and stronger revocation triggers. That lesson is consistent with the broader NHI problem set highlighted in the 2024 Non-Human Identity Security Report and the practical abuse patterns seen in the Codefinger AWS S3 ransomware attack.

Related resources from NHI Mgmt Group

• How should security teams inventory AI agents across SaaS, cloud, and low-code platforms?
• How should security teams govern non-human identities in cloud environments?
• How should security teams govern federated access across cloud and SaaS systems?
• How should security teams build a cryptographic inventory across cloud and CI/CD systems?