AI tools create a similar governance risk because they can hold and use access outside the normal lifecycle process. When discovery is incomplete, organisations lose track of who approved the access, what it reaches, and when it should be removed. That is the same failure pattern that appears in unmanaged service-account and token sprawl.
Why This Matters for Security Teams
AI tools create the same governance risk as unmanaged NHI access because both can accumulate privilege outside the normal identity lifecycle. When a tool is connected once and then forgotten, teams lose the ability to answer basic control questions: who approved it, what data it can reach, whether the access is still needed, and how quickly it can be removed. That is why NHI hygiene and AI tool governance should be treated as the same operational problem, not separate silos.
This issue is visible in NHIMG research on the State of Non-Human Identity Security, where lack of credential rotation was cited as the top cause of NHI-related attacks by 45% of organisations. The pattern is familiar: long-lived access, weak visibility, and delayed revocation. The OWASP Non-Human Identity Top 10 frames this as an identity governance failure, not just a tooling problem.
Security teams often assume AI tools are harmless because they are “just applications,” but once a tool can call APIs, read tickets, access chat, or trigger workflows, it becomes a non-human identity with real blast radius. In practice, many security teams encounter this only after the tool has already been granted broad access and no one can prove when or why it should be removed.
How It Works in Practice
The practical risk comes from the same mechanics that create unmanaged service-account sprawl. An AI tool is registered, connected to a SaaS platform, issued an API key, OAuth token, or service credential, and then allowed to operate with limited oversight. If the connection is not tied to a named owner, a clear purpose, and an expiry condition, it becomes functionally unmanaged even if it was approved at launch.
Current guidance suggests treating AI tools as workload identities and applying the same lifecycle discipline used for NHI inventory, attestation, and revocation. That means the access decision should not stop at initial approval. It should be re-evaluated at runtime and at defined review points, with explicit scope boundaries and short-lived credentials where possible. The Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is useful here because it emphasizes discovery, ownership, rotation, and removal as a single control loop, not isolated tasks.
- Assign each AI tool a business owner and technical owner.
- Bind access to the minimum API scopes and data domains required.
- Prefer short-lived tokens over reusable static secrets.
- Log tool actions in a way that supports approval, audit, and incident response.
- Revoke access automatically when the use case, project, or vendor relationship ends.
For implementation, the NIST Cybersecurity Framework 2.0 supports this with governance, asset management, and continuous monitoring expectations. The important point is that AI tools should not be trusted because they were approved once; they should be trusted only while their identity, scope, and telemetry remain verifiable. These controls tend to break down in fast-moving SaaS environments because app sprawl, delegated admin rights, and opaque OAuth grants make ownership and revocation inconsistent.
Common Variations and Edge Cases
Tighter governance often increases operational overhead, requiring organisations to balance faster AI adoption against the cost of inventory, review, and revocation discipline. That tradeoff becomes more visible when tools are embedded in employee workflows, vendor platforms, or low-code automation where access is granted indirectly and the owner is not obvious.
Best practice is evolving for shadow AI and agent-like tools that can chain actions across multiple systems. There is no universal standard for this yet, but the direction is clear: treat each tool as a separately governed identity, especially when it can store secrets, impersonate users, or trigger downstream automation. The Ultimate Guide to NHIs and 52 NHI Breaches Analysis both reinforce that the failure is rarely the first connection event; it is the forgotten access path that survives long after the original business need has changed.
One important edge case is vendor-managed AI features that inherit tenant permissions indirectly. Another is tools used by developers or analysts that start with narrow access and gradually expand through exceptions. In both cases, the control objective is the same: reduce standing access, force revalidation, and make revocation routine rather than exceptional.
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 OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Covers unmanaged NHI lifecycle and credential rotation risk. |
| OWASP Agentic AI Top 10 | AGENT-02 | AI tools with tool access create agentic governance and runtime authorization risk. |
| NIST CSF 2.0 | PR.AC-1 | Identity and access control applies directly to AI tool accounts and tokens. |
Inventory AI tool identities, rotate secrets, and revoke access when the use case ends.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
