How should security teams find hidden non-human identities in cloud and application estates?

Start by correlating cloud inventories, CI/CD variables, secret stores, workload logs, and identity governance records. The goal is to map every token, certificate, and service account to a workload and owner. If an identity cannot be tied to a business function, treat it as unmanaged exposure rather than a benign artifact.

Why This Matters for Security Teams

Hidden non-human identities are rarely discovered during a neat inventory exercise. They surface when cloud permissions, application secrets, and service accounts have already drifted away from the systems and owners that created them. That matters because unmanaged NHIs expand the attack surface silently: a forgotten API key, an orphaned workload identity, or a stale certificate can become a durable foothold.

The practical problem is visibility, not just count. Most estates include secrets in CI/CD pipelines, embedded credentials in code, tokens issued to SaaS integrations, and machine identities created outside central governance. NHIMG research shows organisations still struggle to see the full picture, and incidents such as the JetBrains GitHub plugin token exposure and Snowflake breach show how exposed secrets and weak identity mapping turn into real compromise paths. The NIST Cybersecurity Framework 2.0 is useful here because it reinforces that inventory and risk management must include non-human assets, not only users and devices.

In practice, many security teams encounter hidden NHIs only after a token is abused, rather than through intentional discovery.

How It Works in Practice

Discovery works best when teams treat NHI hunting as correlation across multiple telemetry sources, not a single scan. Start with cloud inventories, then join them to identity governance data, secret stores, CI/CD variables, workload logs, and application configuration. The objective is to answer three questions for every credential or service account: what workload uses it, who owns it, and what business function it supports.

In mature environments, this often requires combining passive and active methods. Passive methods include mining logs for authentication events, token use, and certificate issuance. Active methods include searching repositories for hard-coded secrets, reviewing pipeline definitions, and enumerating cloud-native service principals, federated identities, and vault references. Where possible, tie each finding back to workload identity rather than a person. That gives security teams a durable evidence trail and reduces confusion between human access and machine access.

• Inventory secret-bearing systems first: vaults, CI/CD, source control, runtime configs, and brokered integrations.
• Map each secret or certificate to a workload, owner, and expiration date.
• Flag identities with no owner, no workload binding, or no recent use as unmanaged exposure.
• Cross-check privileged service accounts against 230M AWS environment compromise patterns and similar cloud abuse paths.

Current guidance also suggests layering this into existing control frameworks rather than running a one-time project. NIST AI RMF is increasingly relevant where machine identities support AI or automation workloads, and hidden credentials often show up in tooling similar to the exposures documented in Code Formatting Tools Credential Leaks. These controls tend to break down when shadow IT creates identities directly inside SaaS apps because the account exists outside the discovery sources being monitored.

Common Variations and Edge Cases

Tighter discovery often increases operational overhead, requiring organisations to balance visibility against developer friction and false positives. The tradeoff is real: more aggressive scanning can surface sensitive data faster, but it can also overwhelm teams with stale references, temporary build tokens, and intentionally ephemeral credentials.

There is no universal standard for this yet, but current guidance suggests treating context as the deciding factor. A short-lived token used by a deployment job is not the same as a long-lived credential embedded in a forgotten integration. Likewise, a service account attached to a single workload is easier to govern than a federated identity used across multiple clouds. In hybrid estates, hidden NHIs often hide in edge cases such as brokered third-party OAuth connections, local automation scripts, and vendor-managed integrations. NHIMG research indicates that organisations still have weak visibility into these attachments, which is why missing ownership should be treated as a control failure rather than a documentation gap.

For teams with agentic or automated workloads, the discovery challenge is even harder because identities can be created dynamically and used briefly. That makes JetBrains Marketplace AI Plugin Campaign style supply-chain exposure a useful reminder that hidden NHIs are often introduced through trusted tooling. The practical test is simple: if an identity cannot be tied to a workload, an owner, and a revocation path, it should be treated as exposure until proven otherwise.

Related resources from NHI Mgmt Group

• How should security teams govern non-human identities in cloud environments?
• How should security teams govern non-human identities at scale?
• How should security teams govern non-human identities for compliance?
• How should security teams govern non-human identities in Salesforce?