Ownership should sit with the team accountable for the access relationship, not just the application team that created the workflow. If an AI tool uses tokens, delegated service access, or automation rights, those permissions should be governed like machine identities with clear lifecycle control, revocation authority, and review responsibility.
Who Should Own AI-Linked Credentials and Integrations?
Ownership should follow the access relationship, not the team that happened to build the workflow. When an AI tool is using tokens, delegated access, or automation rights, the accountable owner needs authority over scope, expiry, revocation, and review. That is especially true where the integration behaves like a machine identity rather than a one-off application setting.
Why Ownership Has to Follow the Access Relationship
AI-linked credentials often outlive the workflow that created them. If ownership sits only with the product team, revocation and recertification become ambiguous, and no one is clearly accountable when access should be reduced or removed. Treat the credential, token, or delegated permission as an access-bearing asset that needs a named operational owner.
That ownership should usually sit with the business or platform function that depends on the access, with security defining the control standard and engineering implementing the integration. If an AI system can act, call tools, or reach data, the owner must be able to answer a simple question: who approves this access, who can revoke it, and who reviews whether it is still justified?
The API Key Management Guide is useful here because it frames keys as lifecycle-managed credentials, not static configuration. The same ownership logic also applies to Secrets Management Guide and the broader problem of access material that must be rotated, scoped, and retired on purpose.
What Good Ownership Looks Like in Practice
Clear ownership means the security programme can name a control owner for each AI-linked integration, even if multiple teams touch it. The owner should know the integration’s purpose, the minimum access it needs, the rotation path for its credentials, and the escalation path if the integration is abused or no longer required.
Where the AI tool depends on long-lived secrets or delegated permissions, the right owner is the one who can make lifecycle decisions, not just the one who can edit code. That is why credentials, tokens, and service-style access should be governed like machine identities, with explicit review and revocation responsibility rather than informal shared custody.
This is also where the distinction between creating an integration and owning its access matters. An app team may build the workflow, but the team that benefits from or depends on the access should own the operational risk. For AI provider keys and similar credentials, the relevant question is not who integrated them first, but who can prove they are still needed and safely constrained.
The LLM Provider API Key Security and LLMjacking Guide is a good example of why ownership must be tied to abuse potential as well as implementation. When AI credentials are stolen or overused, the organisation that owns the access relationship needs to respond, not just the team that wrote the connector.
For a broader identity perspective, the Ultimate Guide to NHIs helps anchor AI-linked credentials in the same governance model used for service accounts, API tokens, and workload identities. That framing is useful when you need to decide whether the integration belongs in app ops, platform engineering, or a dedicated identity owner model.
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 addresses the attack surface, NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 — Improper Offboarding | AI-linked credentials need a clear owner to retire access when the workflow ends. |
| NHI-02 — Secret Leakage | Ownership must cover stored tokens and keys that can leak from AI integrations. | |
| NHI-05 — Overprivileged NHI | AI-linked access should be owned by whoever can enforce least privilege and review scope. | |
| Recommendation — Assign a responsible owner and revoke AI-linked access immediately when the integration is no longer needed. Centralise secret handling and rotate exposed AI credentials as soon as leakage is suspected. Review AI integration permissions regularly and reduce any access beyond the minimum required. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | AI-linked credentials require lifecycle control over issuance, rotation, and revocation. |
| AC-6 — Least Privilege | Ownership should support minimal access for AI tools and delegated integrations. | |
| AU-6 — Audit Review, Analysis, and Reporting | Ownership requires review responsibility so AI access use can be monitored and challenged. | |
| Recommendation — Manage AI tokens and keys through formal issuance, rotation, expiration, and revocation processes. Limit AI integrations to the smallest set of permissions needed for the approved task. Review AI access activity and investigate anomalies that indicate stale or excessive permissions. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | AI-linked credentials are access assets that need defined ownership and governance. |
| A.5.16 — Identity management | AI integrations should have governed identities or equivalent access subjects with owners. | |
| A.8.2 — Privileged access rights | AI tools with elevated rights need accountable ownership and review. | |
| Recommendation — Define access ownership and approval rules for every AI-linked integration and credential. Maintain a clear inventory and ownership model for AI-linked identities and access relationships. Track, review, and remove AI integrations that hold privileged access rights. | ||
| NIST Zero Trust (SP 800-207) | 3.2 — Least Privilege Access | AI-linked access should be continuously constrained and revalidated. |
| Recommendation — Continuously verify that each AI integration still needs its granted permissions. | ||
Practitioner Guidance
What to prioritise: Assign one accountable owner per AI-linked credential set or integration, and make sure that owner can approve scope, trigger rotation, and revoke access without waiting on the original developer.
What to verify: Check that every live AI integration has a named owner, a business justification, an expiry or review date, and a documented revocation path that security can actually execute.
Common mistake: Treating AI access as a code ownership issue instead of an access governance issue, which leaves stale tokens and delegated rights in place long after the workflow changes.
Practitioner takeaway: If the AI system can use authority, the owner must be the person or function accountable for that authority, not merely the team that installed it.